CIRP Annals - Manufacturing Technology 59 (2010) 740–759
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CIRP Annals - Manufacturing Technology
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Design verification and validation in product lifecycle
P.G. Maropoulos (1)a,*, D. Ceglarek (1)b
a
b
Department of Mechanical Engineering, University of Bath, Claverton Down, Bath BA2 7AY, UK
Warwick Digital Laboratory, University of Warwick, Coventry, UK
A R T I C L E I N F O
A B S T R A C T
Keywords:
Design
Validation
Verification
Lifecycle management
The verification and validation of engineering designs are of primary importance as they directly
influence production performance and ultimately define product functionality and customer perception.
Research in aspects of verification and validation is widely spread ranging from tools employed during
the digital design phase, to methods deployed for prototype verification and validation. This paper
reviews the standard definitions of verification and validation in the context of engineering design and
progresses to provide a coherent analysis and classification of these activities from preliminary design, to
design in the digital domain and the physical verification and validation of products and processes. The
scope of the paper includes aspects of system design and demonstrates how complex products are
validated in the context of their lifecycle. Industrial requirements are highlighted and research trends and
priorities identified.
ß 2010 CIRP.
1. Introduction
Globalisation coupled with product customisation and short
time to market have spearheaded new levels of competition
among manufacturers. In CIRP, the needs for design adaptability
[1], the ability to develop products and services for the ecommerce era [2] and the issues of dealing with design
complexity [3] have been recognised. To be successful in the
global market, manufacturing companies are increasingly
expanding simulation models from product and process based
(value chains) to service based (value networks) by focusing on
lifecycle simulations and design for product variation [4] to
obtain both quality of product and robustness of processes, and
to enable the validation and verification of products and
processes to 6-sigma. These methods are vital to reduce process
faults and facilitate efficient and effective engineering changes.
Current validation and verification-based approaches mainly
focus on product conformance to specifications, product functionality and process capability. However, even the most robust
systems can be subject to failures during product verification and
validation.
This paper presents the concepts of validation and verification
in the product lifecycle by including analysis and review of
literature and state-of-the-art in: (i) preliminary design, (ii) digital
product and process development; (iii) physical product and
process realisation; (iv) system and network design; and (v)
complex product verification and validation.
The paper starts with a summary of the scientific motivation for
the review of design verification and validation. The definitions of
verification and validation are then covered, including concepts
and definitions arising from ISO standards as well as software
* Corresponding author.
0007-8506/$ – see front matter ß 2010 CIRP.
doi:10.1016/j.cirp.2010.05.005
development. The paper also defines the design application areas
in terms of products, processes and systems and reviews mainstream methods and systems.
2. Motivation, scope and definitions of verification and
validation methods and technologies
2.1. Motivation
The current product and production system requirements that
influence the way products are developed and verified include:
Mass customisation and personalisation.
Reconfigurability and flexibility of production systems.
Responsive factories.
Products and processes need to be designed, verified and
validated in a manner that is compatible with the above industrial
requirements. Fig. 1 shows a representation of validating products
and processes after the digital modelling phase, clearly identifying
the research questions and business drivers.
Validation in the digital space is a key objective and industrial
requirement that drives research and development. If this were to
be feasible, the results would have been reduced lead times and
critically, fewer failures and better perceived product quality by
the customers. Fig. 2 shows the closed-loop nature of the process
required for managing the lifecycle data capture for design
validation. This ability presupposes:
Integrated and holistic views of design in order to be able to
validate in an integrated manner.
Digital modelling and representation ability for both the product
and the process (function and specification testing).
A time horizon that includes the product lifecycle.
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Fig. 1. Validation and verification requirements in the product lifecycle.
Fig. 2. Closed-loop validation and verification.
The following observations are valid in relation to the present
industrial practice for design verification and validation:
overall functionality at component, subsystem and complete
product level. Processes are also validated at each one of their
physical levels so as to provide the required physical attributes of
components, sub-assemblies and the overall product. The system
and network design and development also includes a digital phase
and major considerations are confirmed by validating real system
performance. Product lifecycle aspects are best exemplified by
considering how complex products are validated in the context of
lifecycle considerations. The framework shown in Fig. 3, puts a
coherent structure to the multiplicity of digital analyses, manufacturing processes and metrology technologies needed for the
verification and validation of complex products in their lifecycle.
These techniques and methods and their relevance to design
verification and validation are analysed herein.
Such activities are usually executed when the design process is
almost complete, during prototyping and first-off testing and
development. This results in frequent deviations from the
required form, dimensions or function, extending development
times and increasing the compliance cost.
This problem is both procedural (stage or time of execution of
such activities and requirement for different skills) and
theoretical (lack of robust verification and validation methods
for deployment during the digital design stages).
The aim is to execute verification and validation as early as
possible during the design process, by developing new generation digital or virtual testing methods.
Complexity in design makes verification and validation even
more difficult to apply as part of the design process.
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2.2. Scope of the keynote paper
2.2.1. A framework for design verification and validation
Fig. 3 shows the scope of the new framework for engineering
design verification and validation which is lifecycle based, tracking
the progression of engineering designs across four key stages: (i)
from the preliminary design stage that sets the requirements, (ii) to
the digital design domain, (iii) the physical, product and process
development and prototyping phase, and (iv) the consequent
design of the production system and network for the realisation of
complex products and processes.
Product and process designs are developed in the digital
domain and the final validation usually requires the execution of
physical trials to confirm the product properties, dimensions and
2.2.2. Keynote scope
The scope for this keynote is outlined in Fig. 4. The main focus of
the paper is on product and process verification and validation.
System perspectives are also included for completeness and
lifecycle aspects are covered by reviewing standards and practices
in relation to the verification and validation of complex products.
The paper principally deals with mechanical engineering design
from meso-scale to large-scale, and the corresponding processes,
typical of high complexity and value industry sectors such as
aerospace, marine and automotive.
2.3. Definitions of verification and validation
Verification and validation are the methods that are used for
confirming that a product, service, or system meets its respective
specifications and fulfils its intended purpose. In general terms,
verification is a quality control process that is used to evaluate
Fig. 3. A conceptual framework for design verification and validation.
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Fig. 4. Scope of the keynote paper.
whether or not a product, service, or system complies with
regulations, specifications, or conditions imposed at the start of a
development phase [5,6]. Validation, on the other hand, is a quality
assurance process of establishing evidence that provides a high
degree of assurance that a product, service, or system accomplishes
its intended use requirements [5,6]. Verification and validation
have been defined in various ways that do not necessarily comply
with standard definitions. For instance, journal articles and
textbooks use the terms ‘‘verification’’ and ‘‘validation’’ interchangeably [7,8], or in some cases there is reference to ‘‘verification, validation, and testing (VV&T)’’ as if it were a single concept,
with no discernible distinction among the three terms [9]. Table 1
shows definitions of verification and validation as provided by
international and national bodies.
The definitions given by ISO 9000 [16] originate from the
general field of quality and focus on the provision of ‘‘objective
evidence’’ that specified requirements have been fulfilled. The
verification process according to ISO is broadly defined, and
validation is focused on fulfilling an intended use or application.
The Global Harmonisation Task Force, defines verification in a
manner compatible with ISO, and process validation is based on
consistent generation of results that satisfy predetermined
requirements [19]. However, such generic definitions evolved
due to the specific demands of application domains. For example,
in the field of metrology, the Joint Committee for Guides in
Metrology defines verification on the basis that a ‘‘target
measurement uncertainty has been met’’ [17]. The definition of
validation is much less specific, referring to the adequacy of
requirements for an intended use. The verification definition by the
International Organisation of Legal Metrology [18] is based on the
interpretation of the word ‘‘accurate’’, and it clearly creates a direct
link with metrology in the process of establishing how different the
real artefact is from its modelling representation.
There are extensive definitions of verification and validation in
the context of digital design and these definitions also cover aspects
of modelling and simulation. These include the IEEE Standard 610
[10] and the definitions of the US Department of Defence (DoD) [12],
as shown in Table 1. The US Department of Navy [13] and the CFD
Committee of AIAA [14] provide definitions for modelling and
simulation software systems that are derivatives of those provided
by the US DoD. The US Food and Drug Administration has given
definitions of digital systems verification and validation [15], which
explicitly include references to the ‘‘consistency’’ and ‘‘correctness’’
of the software. SAE Aerospace [20] and Sargent [21] reported a
variety of design verification aspects, as shown in Fig. 5.
In summary, the generic definitions for design verification and
validation are given by ISO 9000 [16]. As the digital stages of design
become increasingly important, the verification of the modelling
Table 1
Definitions of verification and validation in the digital and physical domains.
V&V processes in digital design phase
V&V processes in physical world
Verification
Validation
The process of evaluating software to determine
whether the products of a given development
phase satisfy the conditions imposed at the
start of that phase [10]
The process of determining that a computational
model accurately represents the underlying
mathematical model and its solution [11]
The process of determining that a computer model,
simulation, or federation of models and simulations
implementations and their associated data accurately
represent the developer’s conceptual description and
specifications [12]
The process of determining the degree to which a
modelling and simulation (M&S) system and its
associated data are an accurate representation of the
real world from the perspective of the intended uses
of the model [13]
The process of determining that a model accurately
represents the developer’s conceptual description of
the model and the solution to the model [14]
Providing objective evidence that the design outputs
of a particular phase of the software development
lifecycle meet all of the specified requirements for
that phase [15]
The process of evaluating software during or at the
end of the development process to determine whether
it satisfies specified requirements [10]
Confirmation, through the provision of objective
evidence, that specified requirements have been
fulfilled [16]
Provision of objective evidence that a given item
fulfils specified requirements, such as confirmation
that a target measurement uncertainty can be met [17]
Pertains to the examination and marking and/or
issuing of a verification certificate for a measuring
system [18]
Confirmation by examination and provision of
evidence that the specified requirements have been
fulfilled [19]
Confirmation, through the provision of objective
evidence, that the requirements for a specific intended
use or application have been fulfilled [16]
Where the specified requirements are adequate for an
intended use [17]
The verification process ensures that the system
implementation satisfies the validated requirements [20]
The process of determining the degree to which a model
is an accurate representation of the real world from
the perspective of the intended uses of the model [11]
The process of determining the degree to which a model,
simulation, or federation of models and simulations,
and their associated data are accurate representations
of the real world from the perspective of the intended
use(s) [12]
The process of determining that an M&S implementation
and its associated data accurately represent the
developer’s conceptual description and specifications [13]
The process of determining the degree to which a model
is an accurate representation of the real world from the
perspective of the intended uses of the model [14]
Confirmation by examination and provision of objective
evidence that software specifications conform to user
needs and intended uses, and that the particular
requirements implemented through software can be
consistently fulfilled [15]
Objective evidence that a process consistently produces
a result or product meeting its predetermined
requirements [19]
Validation of requirements and specific assumptions is
the process of ensuring that the specified requirements
are sufficiently correct and complete so that the product
will meet applicable airworthiness requirements [20]
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Fig. 6. Transition of designer’s intent to physical realisation through GPS guidelines.
Fig. 5. Verification in digital and physical world (adapted from Refs. [20,21]).
and simulation aspects [10,12] will become increasingly applicable. The overall process for integrated digital and physical
prototype verification and validation is exemplified by SAE
Aerospace [20], see Fig. 5, and the metrological practice governing
the physical prototypes is given by VIM [17].
3. International standards related to product and process
design in the lifecycle perspective
International standards play an important role in preserving the
designer’s intent and seamlessly utilising the associated information and manufacturing practices in a heterogeneous manufacturing environment. The transition of the designer’s intent from the
digital design specification to the actual product and associated
service realisation is illustrated in Fig. 5. Today, as each phase of the
product’s lifecycle is globally dispersed in supply and knowledge
chains [2], international standards are essential to deploy
standardised manufacturing execution protocols in order to
establish an unambiguous definition ‘‘language’’ throughout a
global supply chain and ensure consistent product performance in
the service phase. Hence, the provisions of the most relevant to
product and process verification and validation standards are
analysed herein.
3.1. Standards for representing product information
Computer interpretable representation of product information
is utilised within a variety of CAx applications for design
verification and validation. The majority of these standards
represent geometric information and evolved to cover other
aspects. Standards such as Geometrical Product Specification (GPS)
[22], ASME Y14.5: Geometric Dimensioning and Tolerancing
(GD&T) [23], STandard for Exchange of Product model data (STEP)
[24] have thus evolved for modelling and preserving other aspects
of product related information such as tolerances, kinematics,
dynamics and manufacturing processes. For example, the STEP and
GPS standards have evolved, providing product specific informa-
tion constructs known as ‘‘application protocols’’ in STEP and ‘‘GPS
matrix’’ in GPS.
Current GPS standards define global guidelines along with
fundamental principles for capturing designer’s intent and
expressing design requirements. Product and process design
characteristics such as size, angle, orientation and surface texture
are considered as individual chains as shown in Fig. 6. The
information regarding each characteristic is categorised according
to its relevance in the product lifecycle. Each category is called a
‘‘link’’ within the GPS masterplan [22]. Thus, a comprehensive
‘‘chain-link’’ matrix (Fig. 6) has resulted in a number of GPS
standards which address how product specific characteristics can
be represented and utilised throughout the design, manufacture
and verification phases of the product. For example, designer’s
intent regarding the size of the product’s feature is preserved in the
‘‘size’’ chain of the GPS matrix.
Mathieu and Dantan [25] proposed to ISO a new model for
Geometric Specification and Verification called ‘‘GeoSpelling’’ as a
basis for GPS standards rebuilding. The merits of GPS standards
have been exploited in a variety of digital product design
applications such as coherent tolerancing process [26], evaluation
of measurement uncertainty [27] and quantitative characterisation of surface texture [28,29]. Srinivasan [30] identified the merits
of unifying and standardising ad hoc approaches practiced by
industry. GPS allows such unification and standardisation through
global guidelines described in the GPS masterplan [22]. More
recent GPS standards [31] introduced the concepts of specification
uncertainty and correlation uncertainty that directly influence
validation and verification.
A symbolic language called GD&T [23] has been developed for
describing nominal geometry of parts and assemblies and
allowable variation in the product design and verification phase.
GD&T brings significant benefits in design and inspection activities
as a correct GD&T representation captures design intent and shows
the functional requirements of the part as well as the method for its
inspection [23]. Arguably, the most important benefit of the GD&T
approach lies in ensuring, at the design phase, that component
parts will assemble into the final product and function as intended
[32]. Shen et al. [33] proposed a semantic GD&T representation
model, named the ‘‘constraint-tolerance-feature-graph’’ that is
claimed to satisfy all tolerance analysis needs. Kong et al. [34]
formulated an approach for the analysis of non-stationary
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tolerance variation during a multi-station assembly process with
GD&T considerations. The application of GD&T for mechanical
design has gained widespread acceptance by industry [35].
However, several organisations have attempted to implement
the method without a fundamental understanding of how the
design process is impacted [36]. Poorly applied GD&T, ambiguous
plus/minus location or orientation controls, and sometimes no
variation specifications are commonly encountered [37]. The need
to capture functional requirements and improve the design of parts
as well as to consider the cost and quality issues defined by GD&T
makes this subject an even more important element of mechanical
engineering design [38].
In summary, the GPS [22,31] and GD&T [23] standards are vital
for the correct and efficient verification of mechanical engineering
designs. There are exciting new research opportunities arising
from the utilisation of these standards to automate the bidirectional relationships between design specifications, process
capability and measurement uncertainty.
The STEP project was launched with the objective of conserving
the manufacturing context and developing information bridges
between segregated CAx domains [24]. EXPRESS [39] is used to
specify requirements on information content as ‘‘it consists of
language elements that allow an unambiguous data definition and
specification of constraints on the data defined’’. The development
of the STEP standard was governed by industry’s need to overcome
interoperability problems. The standard established a neutral data
file format that is used for developing domain specific applications
using application protocols (APs). For example, AP 219 [40]
provides information requirements for analysing the dimensional
inspection data and results of solid parts and assemblies. Fig. 7
shows a selected set of application protocols that are vitally
important for the communication and sharing of data required in
design verification and validation of mechanical components.
3.2. Standards for representing manufacturing processes
A ‘‘process’’ in a manufacturing context is defined as a
combination of activities that occur over a period of time in
which objects participate [41]. The National Institute of Standards
and Technology (NIST) in the USA developed the Process
Specification Language (PSL) [42] to create a generic, neutral
and high-level language for specifying processes and the integration of multiple process-related applications. PSL uses the ontology
based Knowledge Interchange Format to specify concepts,
terminology and relationships for processes. Similarly, a data
model for representing manufacturing processes was developed by
NIST, which later became a part of the international standard ISO
16100 for exchanging information between design and manufacturing process planning software systems for mechanical products
[43].
The need for comprehensive information regarding specific
manufacturing processes and the verification of components,
[(Fig._7)TD$IG]compelled practitioners to develop process specific international
standards such as DMIS [44], DML [45] and I++DME [46] for the
exchange of inspection process information and measurement
results in the production environment. Similarly, the BS EN ISO
8062 series [47] and the BS EN ISO 10135 [48] series of standards
within the GPS framework cover the requirements for casting and
moulding processes. Another set of process specific standards is
the ISO 14649 series [49], with parts corresponding to different
processes; for instance, part 16 [50] for performing inspection
operations in a STEP-NC manufacturing environment.
3.3. Standards for representing manufacturing resources
A typical manufacturing system consists of a range of resources
such as machine tools, material handling systems, fixtures, robotic
arms, and measurement systems [51]. Each resource has a distinct
purpose and thus provides specific capabilities that are utilised in
manufacturing decision-making. A variety of international standards have evolved in order to utilise and exchange the
information regarding manufacturing resources and their capabilities in a digital environment [52]. For example, ISO 13584 [53]
with the acronym PLIB is a series of standards for the computerbased representation and exchange of part library data. PLIB is fully
inter-operable with STEP [24]. Resource specific standards have
evolved to satisfy business needs. For example, ISO 13399 [54]
deals with the representation and exchange of cutting tool data
and ASME B5.59-2 [55] is an information model for machine tools.
Measurement equipment related GPS standards [56,57] were
developed to describe the acceptance tests for co-ordinate
measuring machines and general requirements for GPS measuring
equipment respectively.
3.4. Standards for preserving design verification knowledge
International standards are used to preserve and seamlessly
transfer context specific knowledge obtained through design
verification, within a heterogeneous manufacturing environment.
Business sectors such as, aerospace manufacturing, defence, ship
building and military equipment manufacturing intensively invest
in research and development activities and have a strong
requirement to conserve and reuse knowledge acquired through
the design verification processes. Consequently, ISO 10303 AP 209
[58] has been developed by aerospace and commercial research
organisations for associating engineering analysis data with
geometric data. ISO 10303 AP 237 deals with the exchange of
computational fluid dynamics (CFD) information, including
product geometry, associated meshes defining the computational
details and CFD boundary conditions [59].
4. Verification and validation in the early stages of design –
capture intent and confirm requirements
The early design stages are vitally important for the correct
capture of technical and lifecycle requirements arising from
understanding and interpreting market needs. Verification is
inherent in methods deployed during these important early stages,
although this is not always appreciated by designers and
manufacturing practitioners. This section outlines methods for
design idea validation and quality function deployment (QFD) as
well as the more technical aspects of ensuring that consistency in
terms of key design objectives is maintained using key characteristics (KCs) and Design for X (DFX) techniques.
4.1. Product idea validation and market analysis
Fig. 7. Integration of designer’s intents within STEP framework.
There are three key considerations that are applied in the early
stages of design: (1) to prioritise customer needs (CNs) in a
quantitative manner based on market analysis; (2) to select the
best design schema; and (3) to improve communication at all
levels of the organisation. Methods such as matrix prioritisation
and analytical hierarchy process [60] are applied to help the
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4.4. The use of key characteristics in early design
Fig. 8. Four-phase process planning by QFD [63].
enterprise determine where to invest the development resources
to achieve maximum payoff.
The traditional way is to analyse CNs systematically and to
transform them into the appropriate product features. However, it
is difficult to assess the performance of the transformation process
with an accurate quantitative evaluation. Büyüközkan et al. [61]
presented a fuzzy group decision-making approach to better align
CNs with objectives of product development in QFD. This
prioritisation of customer needs creates a set of criteria that is
used for validating the final product i.e., assessing whether the
enterprise is building the right product, service or system.
4.2. Quality function deployment
QFD is a customer-driven methodology for product design and
development that underpins quality systems and has found
extensive applications in industry via the development of a
multiplicity of tools and systems that aid an enterprise in
understanding the voice of the customer [60]. QFD efficiently
translates CNs into design requirements and parts deployment
[62]. As shown in Fig. 8, a generic QFD process consists of four
phases in order to relate the voice of the customer to product
design requirements (phase 1), and then translate these into parts
characteristics (phase 2), manufacturing operations (phase 3),
and production requirements (phase 4) [63]. During early design,
the first and second phases of the four QFD phases are
implemented [63] and part characteristics are defined. In
summary, QFD is critical to design validation as it translates
customer needs into part characteristics and production controls
that can then be used for design verification, by forming the set of
criteria against which product and process compliance can be
assessed.
4.3. Functional decomposition and flow analysis
The verification and validation process of a function can be
viewed as functional decomposition and flow analysis which aim
to break overall functionalities down to functionally independent
sub-functions as finely as possible [64]. A functional structure can
be validated by considering both logical and physical dependencies
and confirming matching inputs and outputs among sub-functions
[65]. Several flow analysis methods such as bond graph and Petri
nets [66] and modularity methods such as function structure
heuristic method [67], design structure matrix [68] and modular
function deployment [69] are applicable to the verification and
validation of functional structures.
In an era of increasing product sophistication, engineered
systems are likely to become more complicated, increasing the
functional requirements [3]. Suh [3] defined complexity as the
measure of uncertainty in achieving the functional requirements of
a complex system and outlined how axiomatic design can be used
to reduce design complexity while satisfying the functional
requirements within given constraints. As such, axiomatic design
can enhance the functional validation of designs.
Variability in production and measurement procedures can
result in lower than expected quality levels, compromised product
performance and increased rectification costs. Key characteristics
(KCs) are being used to help identify and reduce important root
causes of variability [70]. Research focused on KCs has had a
significant impact in improving product and process performance
in the context of the lifecycle [71,72]. KC methodologies have been
introduced into the product development practices of world-class
companies [73]. Thornton [74] categorised product related KCs
according to the level of the product model as KCs belonging to;
product, subsystem, component, feature and feature face. Thornton [75] proposed a method for variation risk management in
aircraft and automotive production by establishing a direct link
between KCs and the type of inspection process used for
verification.
The use of KCs for manufacturing planning during early design
enhances process verification. Dai and Tang [76] defined verification
parameters by prioritizing KCs. Whitney [77] proposed a KC oriented
method for assembly planning by selecting the necessary part
features, tools and machine capabilities. Wang and Ceglarek [78]
developed a KC based methodology for quality-driven sequence
planning. Suri et al. [79] introduced a technique based on key
inspection characteristics to enhance process capability. Maropoulos et al. [80] proposed the use of aggregate product models as a
method for the early integration of dimensional verification and
process planning for complex product design and assembly.
Maropoulos et al. [81] outlined the verification and validation
related benefits arising from the integration of measurement and
assembly using a digital enterprise framework that links key
elements of the product, process and resource models.
4.5. Design for X
Design for X (DFX) is an umbrella term used to denote design
philosophies and methodologies which aim to improve designs by
raising the designer’s awareness for a certain product lifecycle
value or characteristic represented by ‘X’ [82]. The design
considerations applied in DFX have a direct relationship to the
verification methods for the ‘‘X’’ objective.
Design for Manufacture (DFM) [77,83] includes a wide range of
design rules and guidelines defined from the perspective of
improving the manufacturability of parts. For example, the design
guidelines for end milling stipulate that milled features should be
designed in such a way so that the end mill required is limited to
3:1 in length to diameter ratio; the reason being that longer end
mills are prone to chatter that deteriorates surface quality.
Applying this DFM guideline will impact directly on end milling
process capability in terms of surface quality and this will influence
the process verification procedure, such as the sampling method
deployed and the method of surface roughness measurement.
The impact of Design for Assembly (DFA) [77,83] on verification
is also direct. For instance, the part reduction of an electromechanical sub-assembly as a consequence of applying DFA may
result in more complex parts that have additional features. This
will directly change the inspection plan in terms of the number,
type and sequence of measurement operations, the measurement
points per operation and the selection of the measuring device.
Also, DFA for automated assembly stipulates design methods so
that parts can be supplied in the right orientation and do not tangle
with other parts [84]. This again increases process yield and
influences the sampling method deployed for assembly verification data collection and analysis.
Design for Ergonomics is important in labour intensive
industries [85] and has a noticeable and positive effect on process
verification, as controls and displays are re-designed so that
readings cannot be misinterpreted. Design for changeover is vital
in high variety environments [86] and improves process verification as a consequence of high repeatability set-ups.
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Design for 6-sigma (DFSS) is a design activity that aims to
generate high capability, 6s processes, before production commences. DFSS is usually deployed within QFD and is also referred to
as ‘‘Define–Measure–Analyse–Design–Verify’’ [87]. This is an
explicit reflection of the inherent ability of DFSS to enhance the
verification and validation of processes.
There are considerable research challenges in developing new
methodologies that link DFSS with KCs, so that key product
features and dimensions are specified and evaluated by applying
process capability criteria. Such methods would need to be directly
integrated with the definition of GD&T, so that datum points, key
dimensions, inspection methods and process capability are
interlinked in an unambiguous manner.
5. Design verification and validation in the digital environment
Digital prototyping helps manufacturers to virtually simulate a
product and its associated lifecycle phases such as, product
manufacture, assembly and functionality, before the product is
physically realised. This gives manufacturers an excellent
opportunity to visualise and anticipate aspects of the physical
performance of a design with less reliance on costly physical
experimentation. Physical prototyping and testing is still a
requirement, especially for complex products. However, the clear
current industry trend is toward reducing physical testing by
replacing suitable aspects by virtual testing and verification. The
digital verification results are compared with the experimentation results; this validates and certifies computational code
embedded in a digital prototype. Thus, a validated digital
prototype can be utilised for verifying the physical performance
of the product manufactured in the globally dispersed supply
chain.
5.1. Digital mock-up
A digital mock-up (DMU), sometimes referred to as a virtual
prototype, is essentially a digital simulation of a physical
prototype and is increasingly used for the verification of product
functionality. DMU is emerging as the core design collaboration
tool, around which different engineering teams verify the product
through its entire lifecycle, from production planning to functional testing, maintenance and recycling [88,89]. Multiple
engineering teams can now operate in parallel, working on the
same DMU, and this facilitates the enterprise wide application of
concurrent engineering practice. Recently, the usage of DMU has
increased, mainly among aerospace and automotive companies,
owing in a large part to the availability of more robust models and
enhanced computing resources. For instance, the Chrysler
Corporation, used DMU to reduce automobile development cycle
by half, while resolving 1200 potential issues before the first
physical mock-up was built [90]. Using proprietary DMU systems,
Boeing was able to reduce errors and rework on its 777 airliner by
70–80%, saving 100,000 design hours and millions of dollars [90].
Similarly, Airbus is also increasingly exploiting the advantages of
DMU [91].
For complex engineering products, the use of DMU is not
without problems, the largest of which is ensuring data quality
between all of its suppliers, customers and design offices. For
instance, data loss when transferring from one CAD format to
another remains a major issue [91].
In summary, DMU is a powerful verification tool and research
for its development should be based on: (i) enhanced capabilities
to simulate functional performance using functional mock-up
methods, and (ii) the solid foundation of international standards.
The existing STEP (ISO 10303) standard captures adequately
geometric data, while data pertaining to history based modelling
[92], assembly [93], and kinematics linkages are less well
represented [94]. ISO 10303-105 [95] is a good base for kinematic
structure representation and supports case studies for machine
tool modelling [96].
5.2. Tolerance analysis and optimisation
The primary function of tolerance setting is to balance the
product functionality with economic factors [97]. Excessively tight
tolerances will add cost due to more complex processing stages
whereas inadequately wide tolerances will result in insufficient
quality and costly rework. Tolerances are vitally important in the
process of dimensional verification of mechanical parts and
assemblies as the uncertainty of the measurement instrument
needs to be an order of magnitude smaller than the tolerance value.
Historically, tolerances are decided on the basis of legacy practice
within a company and as Maropoulos et al. [81] suggest, many
tolerances are set based on process capability and not on the study
of tolerance build-up during assembly. A review of tolerancing
methods by Singh et al. [98] identifies the main academic and
industrial practices dealing with tolerancing as belonging to either
‘‘tolerance analysis’’ or ‘‘tolerance synthesis’’. In essence, tolerance
analysis attempts to estimate the assembly tolerance stack-up,
while synthesis considers the assembly and product requirements
and distributes the assembly tolerances accordingly [99].
5.2.1. Modelling assembly tolerances
Dantan and Qureshi [100] describe statistical tolerance analysis
as a 2D method that computes the probability that the product can
be assembled and will function under a given set of tolerances. The
assembly response function can be expressed as a function of the
individual and independent component dimensions [101]. As
shown in Fig. 9, there are two basic approaches to tolerance
analysis, the worst-case method and the root sum square method
[98]. The worst-case method assumes that the tolerances are at
their respective extremities and the stack-up is consistently
accumulative (i.e., there is no tolerance cancellation). This is a
pessimistic estimate, but due to its simplicity it is still relevant
today; however it can only be employed in one-dimension at a
time [102]. The root sum square (RSS) method conversely gives a
rather optimistic assembly tolerance estimate, as it is a simple
statistical model based on the normal distribution. As before, the
RSS method is only suited to single dimensional tolerance
problems [103].
A more advanced method that is somewhat more indicative of
tolerance stack-up in the physical world, is the Spotts modified
approach [104]; this is essentially an average of the worse-case and
the RSS model. The ‘‘correction factor’’ approach is also experimentally based, based on scaling the RSS to make it a more realistic
figure. However, this method has particular limitations if the
tolerances/dimensions in the stack-up vary greatly and/or are of
small quantities [98].
More complex assembly response functions and non-normal
tolerance distributions can cause difficulties when using traditional analytical techniques as a high number of samples is
required to create an accurate estimation of the assembly
response. In such cases, Monte Carlo Simulation (MCS) has become
a viable solution. MCS can be applied when the assembly response
function cannot be expressed analytically as a linear model and
also when dealing with the effects of tolerance stack-up within
kinematic systems [105]. In the ‘‘kinematic’’ approach [106], the
tolerance chain is treated as a kinematic loop, with the understanding that the movements of the links are actually small
displacements within prescribed tolerance zones. This approach
involves modelling the small displacements using small displace-
[(Fig._9)TD$IG]
Fig. 9. Tolerance analysis [98].
P.G. Maropoulos, D. Ceglarek / CIRP Annals - Manufacturing Technology 59 (2010) 740–759
ment torsors [107] and modelling the effects that local small
displacement have on the remote functional requirement using
Jacobian transforms [108]. Desrochers et al. [109] proposed a
unified Jacobian-torsor model for statistical or worst-case tolerance analysis or synthesis [110].
5.2.2. Digital tolerancing methods and tolerance optimisation
Optimizing tolerances aims to maximise the functional
performance and economic factors associated with tolerances.
The economic factor is often expressed in a quality loss function
[111] and in most applications the Taguchi loss function is used.
Govindaluri et al. [97] consider the quality loss from the
perspective of the customer and the manufacturing and rejection
costs by the manufacturer. When incorporating Taguchi’s quality
loss function Cheng and Maghsoodloo [112] found that when a
component’s mean varies, only the quality loss associated with
that component will be changed; whereas when a component’s
variance shifts, the optimal allowance, tolerance costs, and quality
losses associated with each component will be affected. Tolerance
optimisation methods are classed as either deterministic or
stochastic; the former considers the nominal values of design
variables with respect to given input values, using a single point for
evaluation, whereas the latter consider the statistical variation of
the design variables [113,114].
Computer Aided Tolerancing systems can provide a simulation
platform for modelling the effects of tolerance setting within a
manufacturing process or assembly [115,116]. Tolerance analysis
and synthesis are considered within a DMU to include aspects of
tolerance build-up and assembly clashes [117]. Tolerance design
methods have been summarised by Singh et al. [99] as shown in
Fig. 10, including traditional and advanced methods.
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design has made a direct and very positive impact on part
verification as helped to codify and standardise both the
manufacturing processes and the inspection methods used for
types of features, thus improving design verification. Research is
still required to provide coherence in relating inspection systems
and methods to processes, especially in cases where there is a wide
range of measurement options available, such as the verification of
machined features, or complex assembly features.
Case [122] used methods associated with external approach
directions for features to enhance process capability and Wong and
Wong [123] used volumetric machining features for part modelling in their feature-based design system. Several feature-based
design systems are reported with a focus on prismatic machining
process. In the case of machining, feature-based design allows the
corresponding definition of ‘‘standardised’’ machining processes
that are proven in terms of process capability. This is of major
significance, as it allows rapid verification of a design in terms of its
modelling entities and the corresponding machining process.
Feature-based methods had a profound effect on computer
automated process planning (CAPP) for machining. Gu et al. [124]
identified the sequence of machining process in four stages namely;
feature extraction, feature prioritisation, clustering of operations
and the identifying of precedence relationships. Laperriere and
ElMaraghy used precedence graphs for assembly sequence planning
[125]. Qiao et al. [126] used a genetic algorithm method to sequence
the machining operations for prismatic parts. Li et al. [127] and Ong
et al. [128] tried to solve the process planning problems by
combining the non-traditional optimisation techniques, namely
genetic algorithm and simulated annealing. Azab and ElMaraghy
used quadratic assignment for reconfiguring process plans [129].
The common problems and characteristics of these CAPP approaches
for machining are one or more of the following:
5.3. Features for machining CAD/CAM/CAPP verification
In the last two decades, extensive research efforts in various
segments of CAx integration using feature technology have been
reported especially for the integration of CAD and CAM. Salomons
et al. [118], and Subrahmanyam and Wozny [119] have identified
three major approaches of feature technology namely; interactive
feature definition, automatic feature recognition and design by
features.
In interactive feature definition, features are defined with
human assistance after creating the geometric model. Automatic
feature recognition involves the comparison of the geometric
model with pre-defined generic features. Many approaches for
feature recognition have been reported; Lin et al. [120] extracted
manufacturing features present in a feature-based design model,
while ElMaraghy and ElMaraghy [121] introduced the concept of
functional and manufacturing features.
Presently, the design – by – features approach has become the
core technology for product modelling. Feature definitions
(templates) are placed in the feature library, from which features
are instantiated by specifying dimension parameters, location
parameters and application related attributes. Feature-based
[(Fig._10)TD$IG]
Feature recognition is used in most of the approaches. Hence, the
feature-based databases of commercial software are not utilised.
After recognition, the features (mostly design oriented) are
converted into application (manufacturing) features using a
knowledge base or heuristic rules. The common attributes are
not directly transferred to application features.
The process plans produced by these systems consider only a
single machine set-up. But, in the factory environment, several
machines may be used in different set-ups.
The precedence constraints in the component are represented
with respect to features and not with respect to low-level
entities, namely operations.
The set-ups were optimised with respect to the tool approach
directions. This in turn reduces the search space or looses feasible
design points.
To conclude, process planning research has not as yet reached
the maturity of key methods to focus on verification and validation
in an integrated manner. The feature recognition approach is
theoretically the most generic approach to process planning but it
partly negates the design and process standardisation and
verification benefits of feature-based design.
5.4. Virtual assembly modelling and simulation
Fig. 10. Tolerance design methods [99].
Virtual or digital assembly modelling is a powerful and effective
technology for the verification of assemblies during the digital
design phase. Assembly process planning (APP) is a core
component of virtual assembly modelling as it deals with assembly
constraint identification, equipment selection and sequence
generation [130]. Wang and Ceglarek [131] proposed an assembly
sequence planning method which comprises: (1) sequence
generation for predetermined line configurations using k-piece
mixed-graph representation of assembly; (2) dimensional quality
model of variation propagation for assembly processes with
compliant parts; and (3) evaluation of sequences based on the
multivariate process capability index.
[(Fig._1)TD$IG]
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Ceglarek [144] proposed a GA and low-discrepancy sampling
technique-based optimal design space reduction method to
optimise the locator positions in a multi-station assembly system
while ensuring the robustness of the fixturing system in terms of
the product’s dimensional quality.
Fig. 11. The VADE usage scenario [134].
Using Virtual Reality (VR), the 3D digital mock-up of the
product can be manipulated with the assistance of VR interactive
devices. It, therefore, attracted great interest from researchers
dealing with assembly planning. The advantages of applying
virtual engineering for assembly process planning were summarised by Jun et al. [132]. From the concurrent engineering
perspective, it is preferable to implement the assembly and
disassembly process in a virtual environment at an early stage of
design, when only the geometric forms are determined and the
functions can still be defined [132,133].
The Virtual Assembly Design Environment (VADE) was created
to demonstrate the potential and the challenges involved in the
design and manufacturing processes [134]. Fig. 11 illustrates the
usage scenario of VADE. The VADE system allows the user to
perform assembly processes by hand and assembly tools on the
virtual product with the import data from a parametric CAD
system. By maintaining a dynamic correlation with a CAD system,
the design information created during the virtual assembly process
is updated at the end of using VADE.
Banerjee et al. [135] studied the effectiveness of VR in assembly
planning by comparing: blueprints, a non-immersive desktop VR
environment and an immersive projection-base VR environment.
The results showed that the completion time of the assembly
process was approximately halved by utilising VR. An Augmented
Reality (AR) based human-computer interface was developed by
Ong et al. [136] to provide an immersive and intuitive environment. Unlike VR, the assembly design and planning using AR can be
verified by manipulating the virtual prototypes in the real
assembly environment, which will decrease the possibility of redesigning and re-planning.
5.4.1. Digital tooling and fixturing for assembly
Digital assembly modelling is now well established in the
advanced engineering industries, like aerospace and automotive,
for the design of assemblies and their integration with the design
of tooling and the associated jigs and fixtures. Commercial
software systems allow the seamless integration of product,
process and resource models [137]. The data generated during
assembly tolerance analysis can be utilised by tool designers to
define appropriate tooling tolerances. Such systems are also being
deployed within ITER – the nuclear fusion project – to model the
manipulation of cassette tooling, the loading of which is robot
controlled [138]. Additionally, the digital mock-up of tooling can
simulate accessibility issues and lines of sight for an optical
measurement system [139].
Digital fixturing is a key enabling technology for low cost
tooling that will enhance industry’s capability for batch production
and customisation of products [140]. As an extension from the
established methods of rapid prototyping (RP) from a DMU to a
physical mock-up, a range of rapid tooling applications are being
developed [141]. An alternative to rapid tooling is to employ
reconfigurable tooling; this generally requires modular components that allow a virtually unlimited number of tooling
configurations. Ceglarek et al. [142] extended the ‘‘N-2-1’’ fixture
layout design methodology by introducing a movability restraint
condition which is essential for material handling fixture design.
Kong and Ceglarek [143] addressed a fixture workspace synthesis
method for reconfigurable assembly systems. Phoomboplab and
5.4.2. Stream-of-variation modelling and design synthesis
Stream-of-Variation Analysis (SOVA) is a mathematical model
to describe the relation between final product quality and process
parameters of complex multistage assembly [145,146]. SOVA can
predict potential downstream assembly problems, based on
evaluations of the design using a large array of process variables.
By integrating product and process design in a pre-production
simulation, SOVA can head off individual assembly errors that
contribute to an accumulating set of dimensional variations, which
ultimately result in out-of-tolerance parts and products. Once in
the ramp-up stage of production, SOVA can compare predicted
misalignments with actual measurements to determine the degree
of mismatch in the assemblies and diagnose the root causes of the
errors [145,146].
Individual design tasks must be integrated in order to optimise
the design of the entire system. Phoomboplab and Ceglarek [147]
proposed a design synthesis method based on a hybrid design
structure matrix which integrates design tasks with design
configurations of key control characteristics, especially for
dimensional management in multistage assembly systems. The
method can generate design tasks sequences to minimise
simulation time as well as benchmark design task sequences in
terms of dimensional quality improvement.
5.5. Digital measurement modelling and planning
5.5.1. Measurement and inspection planning techniques
The measurement process, often called inspection process, is
now a vital element of integrated design and manufacturing [148].
Computer Aided Inspection Planning (CAIP) systems have been
developed to accomplish the measurement planning task by the
following generic procedures: (1) CAD interface and feature
recognition, (2) determination of the inspection sequence of the
features of a part, (3) determination of the number of measuring
points and their locations, (4) determination of the measuring
paths, and (5) simulation and verification [149]. The stages of CAIP
for Co-ordinate Measuring Machines (CMMs), are defined as;
establish the best sequence of inspection steps, the detailed
inspection procedure of each feature, feature accessibility by
probes, probe path planning and collision checking, generating the
CMM control commands, and the post-processing of measured
data such as statistical and cost analysis [150].
The first generation of inspection planning systems was
developed by Hopp [151] and ElMaraghy and Gu [152]. Automatic
inspection planning for dimensional and geometric inspections has
two distinguished levels: macro- and micro-level planning
[153,154]. Subsequently, Lee et al. [155] divided the planning
process into two steps: global inspection planning that is focused
on the generation of an optimum inspection sequence and local
inspection planning that is focused on minimizing errors and times
throughout the measurement process.
Research in CAIP falls into two categories: (a) tolerance-driven
inspection process planning and (b) geometry-based inspection
process planning [148]. The former considers inspections on
features with allocated tolerance requirements while the latter
aims to conduct an entire geometry inspection by comparing the
obtained complete geometric description of a part or product with
the design model. The geometry-based CAIP systems theoretically
offer a more coherent inspection process but at a high time and
cost [148]. Recent research has been carried out aiming at the
automation of the inspection features reorganisation, by extracting
from the CAD model directly. Similar research concerning feature
clustering, probe accessibility and orientation analysis dominated
research interest for CMM-based inspection planning carried out
[(Fig._12)TD$IG]
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Fig. 12. Overview of the theoretical framework for integrating measurement with assembly planning.
by Limaiem and ElMaraghy [156], Zhang et al. [157] and Hwang
et al. [158].
With the rapid development of artificial intelligence and
knowledge-based techniques, Expert Systems, Neural Networks
and Fuzzy Logic were used to automate the measurement planning
process. The expert system developed by Moroni et al. [159]
tackles the problem of selecting touch probes and generating the
measurement configurations. Lu et al. [160] and Hwang et al. [158]
employed artificial neural network techniques to obtain the
optimum inspection sequence while Beg and Shunmugam
[161,162] achieved the same objective utilizing Fuzzy Logic on a
prismatic part inspection process. Mohib et al. [163] used
knowledge rules to select the most appropriate probe type and
optimised the planned inspection tasks using a hybrid laser/CMM
for complex geometries.
5.5.2. Metrology process modelling for verification planning
Process modelling is an essential technology for design
evaluation and process planning based on the codification of
engineering knowledge and analytical methods [164,165]. There is
a scarcity of metrology process models for measurement planning
and this may be due to the traditional industrial perception of
metrology simply being a verification step, rather than being an
essential element of the production process [166]. Moreover, new
frameless metrology systems have been integrated with production and assembly, enhancing the need for developing a process
model to codify their capabilities [80,81].
Maropoulos et al. [166] proposed a theoretical framework for
the development of metrology process models for integrating
product design with assembly planning, based on the Digital
Enterprise Technology methodology [167,168]. Fig. 12 shows the
metrology framework, with the metrology process model positioned central to the integration of the design verification process
with the verification of assembly operations and the subsequent
deployment of measurement systems that support measurementassisted automation. The framework explicitly recognises the need
to co-ordinate the digital verification aspects (left part of Fig. 12),
with those that involve the physical deployment of measurement
equipment for product and process verification (right part of
Fig. 12) [166,168].
Industry requires the definition of new research projects
addressing the development and evaluation of integrated metrology and assembly methods and systems that offer superior
positional and orientation accuracy, with in-built verification
capability. Such systems must be fully compliant with relevant
standards and best practice guides including; ISO GUM [169],
ASME B89.4.19 [170] and STEP (ISO 10303) [24].
5.5.3. Measurement and inspection equipment selection
A vitally important stage in the digital verification planning is
the identification and selection of inspection equipment. This
largely refers to measuring systems deployed for dimensional and
shape validation of parts and assemblies. There is a very wide
spectrum of physical scale and accuracy requirements for which
such systems need to be selected covering industrial production
from small parts (measured in millimeters) to large, complex
products such as aircraft, ships, and wind turbines [166,171,172].
New techniques such as absolute length measuring interferometry
and six-degrees-of-freedom probes are frequently combined with
more traditional systems such as CMMs to cover the dimensional
and shape verification needs of modern products [171,172]. The
selection process needs to be based on metrology process models
and employs multiple criteria with a key requirement being the
definition and minimisation of measurement uncertainty
[163,171]. Cai et al. [168,173] proposed an approach for large
volume metrology instruments selection based on measurability
characteristics (MCs) analysis. Inspired by the concept of quality
characteristics, MCs can be used for instrument selection on the
basis of measurement capability, cost and technology readiness.
Muelaner et al. [174] proposed an approach employing a data
filtering technique for instrument selection and Cuypers et al.
[175] specify the task requirements and part restrictions before
selecting instruments manually.
There are exciting, new research challenges in generic
measurement systems modelling and capability derivation that
are essential for instrument selection and measurement planning
within CAIP. Research is also needed for the integration of CAIP
with CAPP, based on the coherent modelling of capabilities.
5.6. Computational and virtual methods for functional product
verification and optimisation
5.6.1. Structural function verification and finite elements analysis
The growing interest in reducing reliance on testing and cut the
cost and time of certification of structural systems has pushed the
academic and industrial world toward the development of Virtual
Testing Labs (VTL) where the Finite Element Analysis (FEA)
technique is employed to predict the possible behaviour of real
world structures until failure (Fig. 13). However, to reduce and
replace physical testing by virtual FEA testing, procedures must be
put in place to demonstrate that the virtual tests are able to
replicate actual tests and to generate the necessary confidence
within the design and certification communities.
The first stage of FEA is the ‘‘idealisation process’’ which takes
the real-life structural design problem and turns it into an idealised
mathematical model, the Finite Element Model (FEM). The second
stage involves selecting appropriate finite elements, mesh layouts
and solution algorithms to define the structural behaviour of the
[(Fig._13)TD$IG]idealised mechanical system. The creation of an error-control
Fig. 13. Virtual testing procedure.
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[(Fig._14)TD$IG]
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procedure to facilitate the user of the FEA in solving structural
design problems has been extensively studied. Other methods for
creating error-free FE models may involve the use of sensitivity
analyses [176]. Besides these intrinsic FEA errors, other uncertainties are present such as the experimental boundary conditions,
exact panel geometry and presence of initial imperfections that
affect the accuracy of the virtual testing. Such issues are more
pronounced for structures made of newly developed materials
such as hybrid materials, fibre reinforced plastics (composites) due
to their high dimensional variability of products. This is becoming
an important issue for thick large-scale structures where
measurement of residual stresses and distortion are challenging
tasks. To solve these issues, upstream 3D digital measurement and
quality control techniques need to be employed in a synergistic
manner with the finite element method for accurate representation of structural and material behaviour under in-service loads
(static, vibration, cyclic loads and impact).
While classical computational stress analyses provide good
predictions in the elastic regime, they have not previously
achieved predictive accuracy in the presence of damage and
fracture. This limitation is starting to be overcome by new
simulation strategies, which combine advances in the generality
and physical realism of damage formulations with new experimental techniques for probing the physics of failure at the micron
and nanometer scales. These research advances are making
possible high-fidelity virtual tests, where the mechanical behaviour of a structure up to ultimate failure is computed through
simulations of the physical processes involved at the atomic [177],
microscopic and structural scales [178].
5.6.2. Design function verification using computational fluid
dynamics
With the increasing availability of affordable access to
substantial computing resources, computational fluid dynamics
(CFD) is now becoming established as a viable tool for computer
aided engineering and design, in spite of uncertainties that
continue to surround the topics of automated mesh generation,
solution sensitivity to mesh size and distribution, and the
verification and realism of turbulence models. CFD software offers
increasingly sophisticated (and computationally demanding)
analysis features such as free-surface modelling, fluid-structure
interaction (FSI) and large eddy simulation (LES).
The turbomachinery and aircraft industries have made use of
CFD for many years to study flows around smooth-shaped
aerodynamic surfaces. Calibrated physical models are used for
these flows using highly structured ‘‘curvilinear’’ (body-fitted)
meshes to make best use of available resources. CFD has resulted in
significant improvements to the design of compressor and turbine
blades [179], including the use of inverse design and multiobjective optimisation techniques [180], with the attention of the
industry and researchers now turning ever more assiduously to
improving the use of valuable compressor bleed air in gas-turbine
internal-air cooling systems [179,181].
In aircraft design, the requirement to carry out large-scale
computations of complete aircraft configurations motivated the
development of empirical ‘‘one-equation’’ models of turbulence for
computational economy [182]. Following a period in which
turbulence models tended to move toward more complicated,
multiple-equation closures (such as shear–stress, v2-f or the even
more substantial Reynolds–Stress models), the robustness and
relative economy of one-equation models, such as Spalart and
Allmaras [182], is enjoying a return to more widespread favour,
and developments of such models to account for more complicated
flow situations are now being proposed and introduced [183].
An important issue with the handling of complex geometries
such as car body surfaces is the efficient translation from solid
model geometry (CAD) representations into a form suitable for
automated mesh generation for CFD. Dawes [184] proposes a
tightly integrated approach in which a pre-defined mesh also acts
as the surface geometry detection mechanism (using algorithms
Fig. 14. Isosurface of instantaneous vorticity over an F-18C aircraft at 308 angle of
attack [185].
derived from medical imaging). This also lends itself to boundary
surface adaptation in response to the flow, a process known as
sculpting. Similar modelling of the interface between flexible
membranes or solid surfaces and the forces exerted on them by a
fluid medium is the basis of FSI, where finite element modelling
can be integrated with CFD to calculate structural deformation in
response to varying fluid dynamics loads.
LES offers the prospect of less reliance of solutions on the often
incomplete representation of flow physics using turbulence
models. In LES, an unsteady turbulent flow is simulated in full
three-dimensional and time-accurate detail, with only the exception of very small-scale (so-called ‘‘sub-grid’’) energy dissipation
processes. The matching of LES techniques to more traditional
modelling methods in low turbulence research, such as near walls,
offers the prospect of high-fidelity ‘‘numerical experiments’’ being
conducted replacing the need for large-scale physical testing. The
unsteady information provided by the LES technique also lends
itself naturally to the unsteady aerodynamics of separated flows,
for example around wind turbine blades or around aircraft at very
high angles of attack as shown in Fig. 14, as well as providing the
fluctuating pressure information that is vital for controlling
unsteady vibrations or acoustic signatures.
6. Physical product and process verification and validation
6.1. Product design – physical verification and validation
Before digital prototyping and testing became the prerequisites
of rapid product development, physical prototyping techniques
were prevalent in industry and have influenced product performance, quality and competitiveness in the global markets. Physical
testing is still an expected industry practice, frequently linked to
product certification. For example, aerospace products undergo
strict testing to pass certification criteria and automobile
manufacturers are required to test their prototypes following
combustion and safety standards. Moreover, physical testing
generates valuable knowledge and data that can be utilised to
enhance the design of future products or variants.
6.1.1. Dimensional and shape verification and validation
Component verification is the process of assessing the
conformance of key features and characteristics of a manufactured
component to the specifications prescribed by the product
designers, as these are captured by the GD&T notations. The
scope of this paper is according to the GPS standard [186], that
prescribes the surface, geometric and dimensional characteristics
involved in verification, as shown in Fig. 15.
[(Fig._15)TD$IG]
Fig. 15. Dimensional and shape characteristics of GPS standards [186].
P.G. Maropoulos, D. Ceglarek / CIRP Annals - Manufacturing Technology 59 (2010) 740–759
Designers define tolerances on core models that are intended to
describe the maximum allowable variation from the nominal size.
Tolerances do not include any allowance for, or knowledge of, the
measurement uncertainty of the equipment used to verify
the dimensions. The standard ISO 14253 [187] makes it clear that
the onus is on the supplier of the measurement data to guarantee
the conformance to specification (tolerance) of the measurements,
and that the data takes account of measurement uncertainty.
There are several ways of carrying out dimensional and shape
verification [171] including direct or indirect measurements, and
measuring either all the parts (100% inspection) or a selection of
parts. Direct measurements are taken off the part itself by deploying
metrology systems suitable for the physical size and scale of the
artefacts and these systems are outlined in the enabling technologies
Section 6.4. Indirect dimensional verification requires taking inferred
dimensions from something other than the part, for example by
measuring the jig that is used to assemble the part. Verification may
also be inferred statistically through controlling and measuring the
process, as outlined in Section 6.3, and this can bring significant cost
benefits through improvements to process capability.
The level of inspection required for any given feature is dictated
by the risk of non-conformance. Depending on the industry sector,
design risk is driven by performance, safety and fit. Process and
inspection risks are dictated by the capability of the process and
inspection systems. Due to the criticality of aerospace components,
high-risk features will always be subject to 100% inspection.
Features that can be effectively controlled by validating the
manufacturing process can be subjected to a reduced inspection
regime, typically yielding a 50–75% reduction in final inspection
load, reducing measurement time per part.
A freeform surface, also known as a complex or sculpted
surface, is classified in ISO 17450-1:2007 [186] as a complex
feature with no invariance degree. Existing technologies for
measuring free form surfaces are detailed by Savio et al. [188].
Photogrammetry and laser scanning are mature technologies for
surface characterisation with measurement accuracies of 5 parts in
105 [189] and 1 part in 104 respectively. Structured light devices
are less mature technologies with accuracy 1 part in 105 but they
have potential for achieving higher accuracy than laser line
scanners due to the fundamental limits imposed by speckle effects
[190,191]. This is where a hybrid system [163] would be
advantageous. While the ISO GPS standard allows profile
tolerances on freeform surfaces like straightness [192], roundness
[193] and cylindricity [194], there is no standard for the
verification of freeform surfaces. Multiple instruments are applicable for surface verification, as shown in Fig. 16.
The production uncertainties of a free form surface, compounded by the edge trimming and the assembly processes that
freeform surfaces typically are involved in, eventually manifest
themselves in gaps, steps and interferences between the surfaces.
Gap and flush problems on a fluid dynamic device, such as an
aircraft wing, are detrimental to its performance and the fit of
automotive panels is indicative of the build quality of the product.
[(Fig._16)TD$IG]
751
The assembly methods used to minimise freeform surface interface
problems can be classified as follows;
Build to nominal: the assembled product tolerance is met by
simply making the key features of the parts as accurately as
possible. Typically used for small products with features that can
be accurately produced.
Measure and adjust: the assembled product tolerance is met by
measuring the interfaces and adjusting some of the parts’
position and/or orientation to minimise interface problems. For
larger parts which can be difficult and expensive to produce to
tight tolerances (such as door panels in the automotive industry),
the position and orientation may be manipulated manually or
automatically to minimise the overall interface problems
[195,196].
Measure for production: the assembled product tolerance is met
by measuring one side of the interface and producing the other
side using the measured data. For very large freeform shapes
such as wings and wind turbine blades, it is very difficult and
expensive to produce parts to tight tolerances. It is often
preferable to tailor parts to fit the specific physical assembly by
producing parts directly using measurements from the assembly
[90,188].
6.1.2. Design structure mapping and hidden features
Hidden features can be defined as those which do not easily
provide line-of-sight access, as occurs commonly in cluttered
assembly environments and complex and enclosed products.
Measurement of these features generally requires an ability to ‘‘see
around corners’’ or measure directly through opaque objects.
Possible approaches include; networks of line-of-sight instruments; mirrors; articulated CMM arms; through-skin sensing
(using Hall effect sensors to locate holes, fitted with magnets, on
components hidden by other components); and six-degrees-offreedom probing. A key issue with networks of line-of-sight
instruments is closing the metrological loop and including
sufficient common points from one instrument to the next, so
as to minimise error buildup.
6.1.3. Measurement equipment deployment
Production metrology begins with the set-up of systems and
continues through the in-process measurement and metrology
enabled automation [80,81]. Metrology must be seen as a
manufacturing process and Muelaner et al. [174] developed a
method for measurement planning and instrument deployment.
Specification of the environmental conditions in which the
measurement is to be carried out should include the average
temperature, temperature gradients, pressure, humidity and
carbon dioxide content [197]. Accuracy, properly defined as
measurement uncertainty [169], is a key performance indicator
for metrology. Much work has already been carried out to model
measurement uncertainty in industrial measurement processes
especially for large volume applications [171] using models
Fig. 16. Examples of freeform surface verification applications.
[(Fig._17)TD$IG]
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752
Table 3
Non-destructive evaluation techniques.
NDE method
Principle of operation
Acoustic emission
Detection of stress waves from defects in
materials
Ultrasonic detection of sub-surface defects
Monitoring of metallic structures under a
magnetic field
Colour change of dyes in cracks based on
capillary action
IR camera measures thermal profile of structures
Pulsed light generates radiation from
sub-surface defects
Laser beam Doppler shift detects vibrations
and defects
Sheared laser-generated image acts as a
reference image of a surface. Application of load
or heat reveals defects
Ultrasonic imaging process
C-scan
Eddy current
Dye penetrant
Fig. 17. Mechanical design, verification and validation of products.
Infrared thermography
Photothermal imaging
created for laser-based spherical co-ordinate measurement
systems, such as laser trackers and laser radar [170,197]. Coordinate measurements may be calculated from a number of
angular measurements obtained using cameras, theodolites, and
iGPS [198]. Calculating the measurement is a complex task, since
measurement uncertainty impacts on part rejection rates
[173,174] and the accuracy of manufacturing processes.
Decision rules for proving conformance or non-conformance
with specifications are clearly defined by international standards.
A component dimension must be accompanied by a tolerance
[199] giving a lower specification limit (LSL) and an upper
specification limit (USL) while a measurement result must be
accompanied by an estimate of measurement uncertainty (U)
[169]. Product conformance can be proven by a measurement
result that is greater than LSL + U and less than USL-U [187].
Laser vibrometry
Shearography
Acoustography
6.2. Product testing and validation
model is required to model the mechanical behaviour of parts. A
key feature of the measurement of materials parameters is the
effective use of instrumentation. Strain measurement devices such
as strain gauges, extensometers and lasers are well known but
techniques such as Electronic Speckle Pattern Interferometry
(ESPI), Holographic Interferometry and Digital Image Correlation
(DIC) [203] provide more accurate 2D and 3D information on strain
distributions around stress concentrations.
An obvious method of evaluating products and components is
to perform static structural tests in tension, compression and shear
to destruction. Performance under cyclic load (fatigue), constant
stress (creep) and constant strain (stress relaxation) will allow the
determination of parameters such as fatigue life (constant
amplitude and complex load or strain), fatigue limit, creep
compliance and stress relaxation modulus. The observation and
understanding of fracture is achieved by the application of optical,
electron and atomic force microscopy. Non-destructive evaluation
(NDE) includes a plethora of techniques, often used to locate
defects. Some NDE methods are summarised in Table 3.
6.2.1. Mechanical design testing
The effective mechanical design of a stand-alone product or a
structural component is predicated on key stages of development
which are summarised in Fig. 17. As already described in Section
5.6.1, the output of FEA modelling depends on the construction of
accurate meshed or meshless continua and the correct assignment
of materials properties. In many cases such materials property
information is available from materials textbooks [200] or in the
form of software [201] but if new materials or bespoke composite
materials are to be used, materials evaluation is needed to define
mechanical properties.
Using a range of test coupon geometries, materials evaluation
performs the dual role of firstly confirming the correct selection of
materials and secondly providing materials properties for FE
modelling. Mechanical tests are published by standards bodies such
as ASTM International and BSI British Standards. The mechanical
testing of fibre composites is given by Hodgkinson [202]. Some
materials parameters and materials tests are given in Table 2.
Materials tests will determine elastic properties and the onset
of yield and will determine whether a linear or a non-linear FE
Table 2
Selected materials parameters and associated test methods.
Property
Parameter
Test method
Strength (maximum, yield, etc.)
s (MPa)
Strain (maximum, yield, etc.)
e
Young’s modulus, stiffness
E, cij (GPa)
Dynamic stiffness
Shear strength
Shear–strain
Shear modulus, stiffness
Elastic compliance
Poisson’s ratio
Work of fracture
Edyn (GPa)
t (MPa)
Critical strain energy release rate
Gc (J m 2)
Critical stress intensity factor
Kc (Pa m1/2)
Thermal expansion coefficient
Glass transition temperature
a (K 1)
Tension, compression,
flexure, etc.
Tension, compression,
flexure, etc.
Tension, compression,
flexure, etc.
Vibration, time of flight
Torsion, shear, tension
Torsion, shear, tension
Torsion, shear, tension
All of the above
Tension, compression
Pendulum and drop
impact
Fracture mechanics
tests
Fracture mechanics
tests
Dilatometer
DSC, DMTA
g
G, cij (GPa)
Sij (m2 N 1)
nij
gf (J m 2)
Tg (K)
[(Fig._18)TD$IG]
6.2.2. Flow related physical verification and validation
The validation of CFD analysis deals with the assessments of
comparison between computational and experimental results
[14,204] as shown in Fig. 18 and this generates valuable data for
improving the convergence of Large Eddy Simulation and
experimental tests. The key parameters in CFD validation tests
deal with the aerodynamic forces that consist of three force
components (lift, drag, side force) and three moments (pitching,
yawing, rolling). The static aerodynamic forces and moments can
be measured indirectly by integrating the surface pressure
distribution [204] or directly by strain gauge balance, internal
spring balance and load cell. The unsteady aerodynamic forces and
moments acting on a maneuvering air vehicle [205] can be
measured by using strain gauge balance and load cell.
The external flow structure of an air vehicle can be illustrated
qualitatively by flow pattern images and quantitatively by
measuring flow velocities. Qualitative flow patterns can be
Fig. 18. Flow validation process [14].
[(Fig._19)TD$IG]
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obtained by using flow visualisation techniques such as; light
scattering particles, dye visualisation, smoke wire, tuft-grid
method and oil-film method. The Laser-Induced Fluorescent
technique can visualise the flow pattern in a 2D plane of a 3D
flow field [206]. Quantitative data of the flow structures can be
obtained by measuring flow velocities using pitot tubes (one
velocity) and five hole probes (three velocities) for steady velocity
measurement at one point. Fluctuating velocities can be measured
by using thermal anemometers (intrusive) and laser doppler
velocimetry (non-intrusive). Particle tracking velocimetry and
particle image velocimetry are capable of obtaining velocity
information on a 2D plane and volumetric three-component
velocimetry has been applied successfully in capturing the whole
volumetric flow information [207,208].
753
Fig. 19. Digitisation methods for dimensional verification.
follows normal distributions. However, these approaches are
insufficient in the case of an ill-conditioned system. An Enhanced
Piecewise Least Squares approach was proposed by Ceglarek et al.
[219] to diagnose the six sigma root causes associated with
product variation.
6.3. Physical process verification and validation
6.4. Enabling verification technologies
The formal manufacturing process verification involves the
stages of inspection, analysis, testing and demonstration. Process
validation is a means of ensuring that manufacturing processes are
capable of consistently producing a finished product of the
required quality and it typically involves the following formal
methods; fault inspection, dependability analysis, hazard analysis,
reproducibility analysis and risk analysis [11]. Process validation is
conducted in the context of a system including design control,
quality assurance, process control, and corrective and preventive
action [19].
6.3.1. Statistical process control and Taguchi’s robust design
Within the field of statistical process control (SPC), a large
number of techniques [209] have become established with the goal
of improving the quality of manufactured products through the
reduction of variability. SPC uses empirical evidence and statistical
analysis to identify quality problems. All processes contain some
unavoidable random variability with random causes referred to in
SPC as chance causes. Avoidable sources of variability such as faults
in machinery, operator errors or defects in materials are referred to
as assignable causes. A primary objective in SPC is to detect where
processes are out of statistical control so that assignable causes can
be identified and eliminated.
Taguchi’s robust design objective is to reduce the output
variation from the target by reducing the sensitivity to noise, such
as manufacturing variations and deterioration over time [210]. The
approach uses the ‘‘loss model’’ because it actually fits a loss
measure in a signal-to-noise (S/N) ratio format. The idea is to
maximise S/N through design of experiments. The focus is to
increase the robustness of the system’s performance.
6.3.2. Six sigma and root cause analysis
Developed at Motorola in the early 1980s, 6-sigma is a business
process methodology that enhances customer satisfaction from
products or services by improving manufacturing processes [211].
Design for Six Sigma (DFSS) is a methodology utilizing tools,
training and measurements to enable the design of products and
processes that meet customer needs and can be produced at six
sigma quality levels [87,212].
To control dimensional variations during manufacturing,
efficient six sigma fault root cause diagnosis is critical for
improving the quality and productivity of processes [144,213].
Ceglarek and Shi [214] proposed a diagnostic approach involving
single faults in a single assembly fixture and this work was
extended by Ding et al. [215], using the state space modelling
technique. In order to overcome problems related to an illconditioned system, Rong et al. [216] have proposed unrotated
Singular Value Decomposition and matrix partitioning techniques.
Liu and Hu [217] proposed designated component analysis for
dimensional fault diagnosis by pre-defining a set of fault patterns
called designated components. Apley and Lee [218] proposed
independent component analysis to model the fault variation
pattern with the assumption that no more than one error source
The physical scale and shape of the component and the
accuracy of the required measurement tasks are key determinant
factors for the selection of verification methods and technologies.
Fig. 19 shows a classification of digitisation methods for
dimensional verification and validation.
Broadly speaking, contact methods are suitable for small to
medium size components, of <1 m3 volume, while non-contact
methods can be applied for much larger parts. There is merit in
combining both contact and non-contact methods in one hybrid
measurement system as demonstrated by Mohib et al. [163]. Over
the past ten years there has been a rapid growth of large volume
metrology systems that can deploy contact or non-contact
methods. Another classification of these systems relates to their
configuration [220]; centralised systems have one main unit (such
as a laser tracker), while distributed systems have more than one
unit (such as the infrared GPS) that work together for measurement of the same point. The result of any measurement is
inevitably affected by a number of systematic and non-systematic
errors which contribute to the overall value of measurement
uncertainty as shown in Fig. 20. Therefore, regardless of the scale,
the dimensional measurement results need to be accompanied by
the statement of uncertainty as defined by GUM [169].
7. Verification of systems and networks
The design of manufacturing systems is carried out using
criteria related to flow of materials and values of quality, cost and
delivery, as dictated by just-in-time methods. Such considerations
are beyond the scope of this paper that focuses on the use of
discrete event simulation (DES) and radio frequency identification
(RFID) for manufacturing system verification.
7.1. Discrete event modelling and simulation
Manufacturing systems are designed by considering a variety of
parameters such as material flow, resource allocation and
[(Fig._20)TD$IG]utilisation that define performance within the factory and the
Fig. 20. Contributing factors to measurement uncertainty [223].
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[(Fig._21)TD$IG]
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supply chain. DES is widely used for the design of systems. DES
allows the dynamics of complex manufacturing systems to be
verified without physical implementation. Simulation is identified
as the second most widely used technique in the field of operations
management after modelling [221]. DES utilises a multi-state
mathematical model of the system where events happen in a
chronological sequence, are instantaneous and change the state of
the system [222].
DES environments with 3D capabilities have been developed
leading to the concept of the ‘‘digital factory’’ [224]. The results
from continuing research into the field of DES have been
implemented in industry to the extent where almost every major
manufacturing enterprise uses this technique to verify facility
configuration, throughput times, material inventory issues and
logistics in the digital design phase, or to evaluate improvement
ideas before their physical deployment. Johansson et al. [225]
conducted a survey and reported the need to improve the
coherence and reliability of data provided via DES to enhance
their use by industry for verification and validation of activities.
7.2. RFID methods for the verification of production logistics
RFID technology uses tags for responding to radio frequency
(RF) signals by transmitting a constituent data, readers for sending
and receiving RF signals and software to process data. RFIDs have
seen rapid adoption in the manufacturing, service and logistics
industries and this section outlines the use of RFID technology for
system design verification and validation. RFID sensors are an
effective means of collecting and processing real-time data from
manufactured parts, products, processes and resources, thus
creating a traceable, real-time view of the production system
and the supply chain, allowing the verification of production
schedules and logistics [226].
For modelling large/complex systems, DES systems require
modelling assumptions regarding the behaviour of elements of the
system (statistical distributions, etc) and the inputting of a large
amount of data. The quality of DES system output is a function of
the correctness of these assumptions and input data. The use of
RFIDs in conjunction with DES can dramatically improve the
quality of DES decision-making by the provision of verified input
data regarding key behaviours of the real system.
Due to the quality and quantity of real-time RFID data, there is
extensive potential to utilise such data for the active adaptation
and reconfiguration of a system as reported by Huang et al. [227]
and the creation of wireless kanbans with embedded RFIDs as
outlined by Zhang et al. [228]. RFID technology finds widespread
exploitation in supply chain management and logistics for
improving decision responsiveness and reducing supply chain
cost via the provision of verified data [229,230].
7.2.1. Managing information loss in product manufacture
Jun at al. proposed a framework for the utilisation of RFIDs in
the product lifecycle [231]. Here the main hypothesis is that during
the digital phases of design and planning, the data and knowledge
is usually captured and codified at acceptable levels using
commercial CAx systems. As product development transits from
the digital to physical phase the information flow become less and
less complete and the wideranging applications of RFIDs can
enhance the information capture and utilisation during product
manufacture, product service and recycling [231]. This ability can
arguably enhance the design of production systems and networks
and improve new product designs by the utilisation of service data.
Fig. 21. A conceptual framework for PLM strategy development [235].
common themes. Stark [233] introduces PLM by stating that it is
‘‘the activity of managing a company’s products all the way across
their lifecycles in the most effective way’’. Ameri and Dutta [234]
evolved the definition further by arguing that PLM is a ‘‘knowledge
management solution which supports processes throughout the
product lifecycle within the extended enterprise’’. Abramovici and
Sieg [235] published details of a major PLM survey in which key
findings included the maturity of PLM interaces with CAD and the
corresponding maturity in capturing product design data as shown
in Fig. 21. The trend clearly demonstrates the considerable
prospects available for improving verification during the physical
stages of product lifecycle, by improving the rate of capturing and
re-using relevant data using PLM. As reported by Jun et al. [231],
the increasing use of RFIDs will impact positively on PLM data
completeness.
As lifecycle management covers the complete period from
product concept definition to disposal, it generates a compelling
context in which to analyse the sharing and exchange of data
between the plethora of CAx systems and the impact of respective
standards [236] as shown in Fig. 22. It can be seen from Fig. 22 that
STEP has a dominant position in terms of PLM data exchange and
Peak et al. [237] and Ming et al. [238] argue that XML and UMLbased STEP are promising technologies for improving PLM
interoperability. Despite all the activity in developing open
standards, Gielingh [239] points out that the uptake of open
standards, in general, has been very poor and that one of the major
reasons is that meaning is often lost in data translation. This is a
key research area for PLM.
8.2. Verification and validation of complex products in the context of
the lifecycle
Complex engineering products, like automobiles and commercial aircrafts, require a set of verification and validation stages that
satisfy respective legislative requirements governing their use and
the increasingly demanding nature of customer aspirations, all
within a cost competitive package. In addition, the products
themselves are highly complex and designed by large engineering
teams spread across many countries and organisations – factors
that, when combined with the exacting requirements, necessitate
a formal and robust design and development methodology in
terms of verification be employed.
[(Fig._2)TD$IG]
8. Methods for the lifecycle verification of complex products
8.1. Enabling technologies and standards for product lifecycle
management
There are many definitions of Product Lifecycle Management
(PLM). While no single definition has emerged [232], there are
Fig. 22. Current standards and their coverage [236].
[(Fig._23)TD$IG]
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755
Fig. 23. The V model for the verification of complex engineering products (adapted from Refs. [20,240]).
The V model of verifying product development, as shown in
Fig. 23, is capturing the aerospace recommended practice for the
development of civil aircraft and systems according to ARP4754a
standard [20]. Broadly speaking, the left side of the V model shows
the top-down requirements development and validation starting
from the product and cascading down to systems and discrete
items, the design of which corresponds to the very bottom of the V
model. The right side of the V model represents the bottom up
process of verification that starts by verifying the design of discrete
items by evaluating whether the respective requirements have
been met, and proceeding with the verification of systems and the
complete aircraft [20].
The same verification process has been adopted for the
development of complex automotive systems, like power-trains.
A significant common aspect is the verification of functional
requirements as captured by QFD. For complex systems, QFD needs
some augmentation where the customer responses are complex, as
in the subjective assessment of vehicle acceleration. Pickering and
Brace [240] describe an automated method to analyse data from
driveability tests of existing vehicles in order to generate
correlations between the subjective driver ratings and objective
test data. This allows objective assessments of power-train
performance to ensure that requirements are fulfilled. For
commercial aircraft, the process depicted in Fig. 23 has duration
of several years and involves the use of a raft of engineering and
software design systems and methods with the process being
managed using PLM systems across the enterprise. For instance,
the verification of stress requirements will involve use of FEA at
item level and the flow performance will be verified using CFD and
LES as outlined in Sections 5.6.1 and 5.6.2 respectively.
verified GD&T [166,168,173]. Other trends include the expansion
of Design for ‘X’ to include measurability and the application of
new design guidelines [166]. These are examples of ‘‘frontloading’’
by building in measurement process knowledge early into the
lifecycle.
Measurement uncertainty is being measured, but not used
adequately. Recently, there have been considerable efforts in
evaluating the uncertainty of different measurement techniques
[241], and software tools are emerging to allow predictions of
measurement uncertainty to be made [242]. However, typically
this information is only used within ‘islands’ of automated
inspection processes. Measurement uncertainty is expected to
be accounted for early in the product lifecycle. Research in
improving measurement simulation is ongoing to make these kind
of environments easier for designers to use [243].
9.3. Verification modelling and planning
9. Key future requirements and trends
Metrology is integral to manufacturing processes, but its
development in terms of measurement modelling and planning
is embryonic when compared to processes and additional research
is needed in metrology process modelling [166]. In the modern
production environment, metrology is becoming tightly integrated
with the manufacturing processes and such integration can
provide valuable information about process capability and
improve the design of future products [81]. For example, new
measurement techniques allow many devices to be taken to the
part [171], on-machine measurement is more common [241], or
measurement is used to facilitate assembly [80]. However,
measurement and manufacturing process planning are still not
sufficiently interlinked [166,241] and considerable verification
benefits will arise from their integration.
9.1. PLM and international standards
9.4. Early design verification in the digital domain
One of the major trends in PLM will be to attempt to build
product and process knowledge earlier in the product lifecycle;
this is termed as ‘‘frontloading’’. In Srinivasan’s review [35] of
standards for product geometry specification, verification, and
exchange it was observed that standards have developed rapidly
since the advent of the digital enterprise. However, some of the
standards, especially the open standards, have poor uptake within
industry [239]. Furthermore, Zheng et al. [73] found that a key
priority is to provide feedback to ‘‘close the gap’’ between the
physical and digital world in the context of PLM. Integration is
expected to become easier over time with the increasing emphasis
on open standards for data exchange.
A key future trend is the requirement for early design
verification. It is well documented that early design phases
account for a large percentage of lifecycle costs. This is especially
true for complex engineering products and for such applications
the early verification of components and the corresponding
functional verification of systems are critically important tasks.
The challenges are significant, including; methods to deal with
verification using low design data-intensity, enhance the scope of
functional verification with the development of integrated
functional mock-up, and techniques for integrated product and
process verification.
10. Concluding comments
9.2. GD&T and measurement uncertainty
The use of GD&T is widespread in industry [35], but is not
adjusted for measurability. Although it is normal for manufacturing process capability to be considered during the design stages,
the GD&T that is applied rarely takes account of measurement
processes and their capabilities. Research is ongoing to address this
issues, including measurement instrument selection that is carried
out from early design, allowing the setting of measurability-
This paper analysed methods and techniques for design
verification and validation, especially focusing on mechanical
engineering products of meso to large-scale, and the corresponding manufacturing processes. There is clear evidence that digital
domain design verification and validation is a high industrial
priority and there is evident research focus in such methods, as
well as considerable coverage via international standards. Physical
product and process verification and validation remain important
756
P.G. Maropoulos, D. Ceglarek / CIRP Annals - Manufacturing Technology 59 (2010) 740–759
requirements, especially for complex products that require
certification, such as aerospace.
There is a gradual, but clear development of new measurement,
inspection and verification modelling and planning methods, to
underpin design verification both at the digital phase and the
physical testing of products and processes. Such methods are
underpinned by new enabling technologies and the trend for the
integration of metrology with production processes.
The development of enhanced PLM capabilities, in terms of
codifying and capturing post-design verification data and knowledge, will be vitally important for the successful adoption and
implementation of new design verification and validation methods
by manufacturing industry.
Acknowledgements
The authors would like to gratefully acknowledge the support
and contribution of many colleagues, from CIRP and other
academics, in the development of this paper. Contributions were
received from; Prof Luc Mathieu, Prof Luc Laperriere, Prof Hoda
ElMaraghy, Prof Torsten Kjellberg, Prof Robert Wilhelm, Prof
Gunnar Sohlenius, Prof Stephen Newman, Prof Rainer Stark, Dr
Aydin Nassehi, Dr Martin Ansell, Dr Michele Meo, Dr Michael
Wilson, Dr Alicia Kim, Dr Zhijin Wang and Dr Chris Brace. Last, but
not least, we would like to note that we are especially grateful to Dr
Parag Vichare, from the University of Bath, whose contribution in
relation to the preparation of this keynote paper has been
outstanding.
References
[1] Gu P, Hashemian M, Nee AYC (2004) Adaptable Design. CIRP Annals –
Manufacturing Technology 53(2):539–557.
[2] Tseng MM, Kjellberg T, Lu SCY (2003) Design in the New E-Commerce Era.
CIRP Annals – Manufacturing Technology 52(2):509–519.
[3] Suh NP (2005) Complexity in Engineering. CIRP Annals – Manufacturing
Technology 54(2):46–63.
[4] ElMaraghy HA (2008) Changing and Evolving Products and Systems – Models
and Enablers. in ElMaraghy HA, (Ed.) Changeable and Reconfigurable Manufacturing Systems.. Springer-Verlag Publ, pp. 25–45.
[5] Babuska I, Oden JT (2004) Verification and Validation in Computational
Engineering and Science: Basic Concepts. Computer Methods in Applied
Mechanics and Engineering 193(36–38):4057–4066.
[6] Plant R, Gamble R (2003) Methodologies for the Development of Knowledgebased Systems. Knowledge Engineering Review 18(1):47–81.
[7] Jagdev HS, Browne J, Jordan P (1995) Verification and Validation Issues in
Manufacturing Models. Computers in Industry 25(3):331–353.
[8] Dzida W, Freitag R (1998) Making Use of Scenarios for Validating Analysis and
Design. IEEE Transactions on Software Engineering 24(12):1182–1196.
[9] Allen NA, Shaffer CA, Watson LT (2005) Building Modeling Tools That Support
Verification Validation, and Testing for the Domain Expert. Proceedings of the
2005 Winter Simulation Conference, Orlando, FL, 419–426.
[10] Geraci A (1991) IEEE Standard Computer Dictionary Compilation of IEEE
Standard Computer Glossaries .
[11] ASME (2006) Guide for Verification and Validation in Computational Solid
Mechanics, Ptc 60/V&V 10.
[12] U.S. Department of Defence (2008) Documentation of Verification, Validation &
Accreditation (VV&A) for Models and Simulations.
[13] U.S. Department of Navy (2004) Modelling and Simulation Verification, Validation, and Accreditation Implementation Handbook.
[14] American Institute of Aeronautics and Astronautics (AIAA) (1998) Guide for
the Verification and Validation of Computational Fluid Dynamics Simulations,
AIAA-G-077-1998.
[15] U.S. Food and Drug Administration (FDA) (2002) General Principles of Software
Validation, Final Guidance for Industry and FDA Staff.
[16] ISO 9000 (2005) Quality Management Systems: Fundamentals and Vocabulary.
[17] Joint Committee for Guides in Metrology (JCGM) (2008) VIM: International
Vocabulary of Metrology – Basic and General Concepts and Associated Terms.
[18] International Organization of Legal Metrology (OIML) (2009) International
Vocabulary of Terms in Legal Metrology (VIML).
[19] Global Harmonization Task Force (GHTF) (2004) Quality Management System
– Process Validation Guidance, 2 ed.
[20] SAE Aerospace ARP4754a (2009) Aerospace Recommended Practice.
[21] Sargent RG (2005) Validation and Verification of Simulation Models. Proceedings of the 2005 Winter Simulation Conference, 130–143.
[22] ISO/TR 14638 (1995) Geometrical Product Specification (GPS) – Masterplan.
[23] ASME Y14.5 (2009) Dimensioning and Tolerancing. The American Society of
Mechanical Engineers.
[24] ISO 10303-1 (1994) Industrial Automation Systems and Integration – Product
Data Representation and Exchange. Part 1: Overview and Fundamental Principles.
[25] Mathieu L, Dantan JY (2003) Geospelling: A Common Language for Geometrical Product Specification and Verification to Express Method Uncertainty.
Proceedings of 8th CIRP Seminar on Computer Aided Tolerancing, NC, USA, 70–
79.
[26] Dantan JY, Ballu A, Mathieu L (2008) Geometrical Product Specifications –
Model for Product Life Cycle. Computer-Aided Design 40:493–501.
[27] Heping P, Xiangqian J (2009) Evaluation and Management Procedure of
Measurement Uncertainty in New Generation Geometrical Product Specification (GPS). Measurement 42(5):653–660.
[28] De Chiffre L, Lonardo P, Trumpold H, Lucca DA, Goch G, Brown CA, Raja J,
Hansen HN (2000) Quantitative Characterisation of Surface Texture. CIRP
Annals – Manufacturing Technology 49(2):635–652.
[29] Balsamo A, Di Ciommo M, Mugno R, Rebaglia BI, Ricci E, Grella R (1999)
Evaluation of CMM Uncertainty through Monte Carlo Simulations. CIRP
Annals – Manufacturing Technology 48(1):425–428.
[30] Srinivasan V (2007) Computational Metrology for the Design and Manufacture of Product Geometry: A Classification and Synthesis. Journal of Computing and Information Science in Engineering 7(1):3–9.
[31] DD ISO/TS 17450-2 (2002) Geometrical Product Specifications (GPS) – General
Concepts. Part 2: Basic Tenets, Specifications, Operators and Uncertainties.
[32] Chiabert P, Lombardi F, Orlando M (1998) Benefits of Geometric Dimensioning and Tolerancing. Journal of Materials Processing Technology 78(1–3):29–
35.
[33] Shen ZS, Shah JJ, Davidson JK (2008) Analysis Neutral Data Structure for
GD&T. Journal of Intelligent Manufacturing 19(4):455–472.
[34] Kong Z, Huang W, Oztekin A (2009) Variation Propagation Analysis for
Multistation Assembly Process with Consideration of GD&T Factors. Journal
of Manufacturing Science and Engineering 131(5). 10.1115/1111.4000094.
[35] Srinivasan V (2008) Standardizing the Specification, Verification, and
Exchange of Product Geometry: Research, Status and Trends. Computer-Aided
Design 40:738–749.
[36] Rennels KE (2003) Current Methodologies for Geometric Dimensioning and
Tolerancing. Proceedings of the Electrical Insulation Conference and Electrical
Manufacturing & Coil Winding Technology Conference, 565–569.
[37] Watts D (2007) The ‘‘GD&T Knowledge Gap’’ In Industry. Proceedings of
the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Las Vegas, NV, 597–
604.
[38] Zhang Y, Yang M (2009) A Coordinate SPC Model for Assuring Designated Fit
Quality Via Quality-oriented Statistical Tolerancing. Computers and Industrial
Engineering 57(1):73–79.
[39] ISO 10303-11 (1994) Industrial Automation Systems and Integration – Product
Data Representation and Exchange. Part 11: Description Methods: EXPRESS
Language Reference Manual.
[40] ISO CD 10303-219 (2005) Industrial Automation Systems and Integration –
Product Data Representation and Exchange. Part 219: Application Protocol:
Dimensional Inspection Information Exchange.
[41] Bock C (2006) Interprocess Communication in the Process Specification Language, NISTIR 7348, NIST, Gaithersburg, MD.
[42] ISO 18629-1 (2004) Industrial Automation Systems and Integration – Process
Specification Language. Part 1: Overview and Basic Principles.
[43] Matsuda M, Arai E, Nakano N, Wakai H, Takeda H, Takata M, Sasaki H (2005)
An Interoperability Framework and Capability Profiling for Manufacturing Software Knowledge and Skill Chains in Engineering and Manufacturing Information
Infrastructure in the Era of Global Communications. Springer, Boston. pp. 75–
84.
[44] DMIS 4.0 (2001) Dimensional Measuring Interface Standard, ANSI/CAM-I104.0.
[45] DML 2.0 (2004) Dimensional Markup Language.
[46] International Association of Co-ordinate Measuring Machine Manufacturers
(2009) Dimensional Measurement Equipment Interface.
[47] BS EN ISO 8062 (2007) Geometrical Product Specifications (GPS) – Dimensional
and Geometrical Tolerances for Moulded Parts Part 1: Vocabulary.
[48] BS EN ISO 10135 (2009) Geometrical Product Specifications (GPS) – Drawing
Indications for Moulded Parts in Technical Product Documentation (Tpd).
[49] ISO 14649-1 (2002) Industrial Automation Systems and Integration – Physical
Device Control – Data Model for Computerized Numerical Controllers. Part 1:
Overview and Fundamental Principles.
[50] ISO WD 14649-16 (2004) Industrial Automation Systems and Integration –
Physical Device Control – Data Model for Computerized Numerical Controllers.
Part 16: Data for Touch Probing Based Inspection.
[51] Vichare P, Nassehi A, Newman ST (2009) A Unified Manufacturing Resource
Model for Representation of CNC Machine Tools. Proceeding of the IMechE Part
B Journal of Engineering Manufacture 223(5):463–483.
[52] Vichare P, Nassehi A, Kumar S, Newman ST (2009) A Unified Manufacturing
Resource Model for Representation of CNC Machining Systems Elements.
Robotics and Computer Integrated Manufacturing 25(6):999–1007.
[53] ISO 13584-42 (1998) Industrial Automation Systems and Integration. Part 42:
Parts Library, Methodology for Structuring Parts Families.
[54] ISO 13399-1 (2006) Cutting Tool Data Representation and Exchange. Part 1:
Overview, Fundamental Principles and General Information Model.
[55] ASME B5.59-2 (2005) Information Technology for Machine Tools. Part 2:
Data Specification for Properties of Machine Tools for Milling and Turning
(Draft).
[56] BS EN ISO 10360 (2001) Geometrical Product Specifications (GPS) – Acceptance
and Reverification Tests for Coordinate Measuring Machines (CMM). Part 1:
Vocabulary.
[57] BS EN ISO 14978 (2006) Geometrical Product Specifications (GPS) – General
Concepts and Requirements for GPS Measuring Equipment.
[58] ISO 10303-209 (2001) Industrial Automation Systems and Integration – Product
Data Representation and Exchange. Part 209: Application Protocol: Composite
and Metallic Structural Analysis and Related Design.
P.G. Maropoulos, D. Ceglarek / CIRP Annals - Manufacturing Technology 59 (2010) 740–759
[59] ISO WD 10303-237 (2003) Industrial Automation Systems and Integration –
Product Data Representation and Exchange. Part 237: Application Protocol: Fluid
Dynamics Data.
[60] Ramanathan R, Yunfeng J (2009) Incorporating Cost and Environmental
Factors in Quality Function Deployment Using Data Envelopment Analysis.
Omega 37(3):711–723.
[61] Büyüközkan G, Ertay T, Kahraman C, Ruan D (2004) Determining the Importance Weights for the Design Requirements in the House of Quality Using the
Fuzzy Analytic Network Approach. International Journal of Intelligent Systems
19(5):443–461.
[62] Shimomura Y, Hara T, Arai T (2008) A Service Evaluation Method Using
Mathematical Methodologies. CIRP Annals – Manufacturing Technology
57(1):437–440.
[63] Chen LH, Ko WC (2009) Fuzzy Linear Programming Models for New Product
Design Using QFD with FMEA. Applied Mathematical Modelling 33(2):633–
647.
[64] Ullman D (1992) The Mechanical Design Process. McGraw-Hill, New York.
[65] Kim D, Xirouchakis P (2010) CO2DE: A Decision Support System for Collaborative Design. Journal of Engineering Design 21(1):31–48.
[66] Deng YM, Britton G, Tor S (2000) Constraint-based Functional Design Verification for Conceptual Design. Computer-Aided Design 32:889–899.
[67] Stone RB, McAdams DA, Kayyalethekkel VJ (2004) A Product Architecturebased Conceptual DFA Technique. Design Studies 25(3):301–325.
[68] Ulrich K, Eppinger S (2004) Product Design and Development. McGraw-Hill,
New York.
[69] Ericsson A, Erixon G (1999) Controlling Design Variants: Modular Product
Platforms. ASME Press, New York.
[70] Ceglarek D, Shi J (1995) Dimensional Variation Reduction for Automotive
Body Assembly. Manufacturing Review 8(2):139–154.
[71] Whitney DE (2006) The Role of Key Characteristics in the Design of Mechanical Assemblies. Assembly Automation 26(4):315–322.
[72] Mathieu L, Marguet B (2001) Integrated Design Method to Improve Producibility Based on Product Key Characteristics and Assembly Sequences. Annals
of the CIRP 50(1):85–88.
[73] Zheng LY, McMahon CA, Li L, Ding L, Jamshidi J (2008) Key Characteristics
Management in Product Lifecycle Management: A Survey of Methodologies
and Practices. Proceedings of the IMechE Part B Journal of Engineering Manufacture 222(8):989–1008.
[74] Thornton AC (1999) A Mathematical Framework for the Key Characteristic
Process. Research in Engineering Design 11(3):145–157.
[75] Thornton AC (2004) Variation Risk Management: Focusing Quality Improvements in Product Development and Production. Wiley, NJ.
[76] Dai W, Tang XQ (2008) Quality Plan Model for Product Development. Proceedings of the 38th International Conference on Computers & Industrial Engineering, Bejing, China, 1535–1541.
[77] Whitney DE (2004) Mechanical Assemblies: Their Design, Manufacture and Role
in Product Development. Oxford University Press, USA.
[78] Wang H, Ceglarek D (2005) Quality-driven Sequence Planning and Line
Configuration Selection for Compliant Structure Assemblies. CIRP Annals –
Manufacturing Technology 54(1):31–35.
[79] Suri R, Frey DD, Otto KN (2001) Key Inspection Characteristics. Journal of
Mechanical Design 123(4):479–485.
[80] Maropoulos P, Zhang D, Rolt S, Chapman P, Rogers B (2006) Integration of
Measurement Planning with Aggregate Product Modelling for Spacecraft
Design and Assembly. Proceedings of the IMechE Part B Journal of Engineering
Manufacture 220(10):1687–1695.
[81] Maropoulos P, Zhang D, Chapman P, Bramall D, Rogers B (2007) Key
Digital Enterprise Technology Methods for Large Volume Metrology and
Assembly Integration. International Journal of Production Research
45(7):1539–1559.
[82] Kuo T-C, Huang SH, Zhang H-C (2001) Design for Manufacture and Design for
X: Concepts. Applications and Perspectives Computers and Industrial Engineering 41(3):241–260.
[83] Boothroyd P, Knight WA (2002) Product Design for Manufacture and Assembly.
CRC Press, New York, USA.
[84] Andreasen MM (1988) Design for Assembly. Springer-Verlag, New York, USA.
[85] Pahl G, Beitz W, Wallace K (1996) Engineering Design: A Systematic Approach.
Springer, New York, USA.
[86] Reik MP, McIntosh RI, Culley SJ, Mileham AR, Owen GW (2006) A Formal
Design for Changeover Methodology. Part 1: Theory and Background. Proceedings of the IMechE Part B Journal of Engineering Manufacture 220(8):1225–
1235.
[87] Yang K, Ei-Haik BS (2003) Design for Six Sigma: A Roadmap for Product
Development. McGraw-Hill.
[88] Wang GG (2002) Definition and Review of Virtual Prototyping. Journal of
Computing and Information Science in Engineering 2(3):232–237.
[89] Wörn H, Frey D, Keitel J (2000) Digital Factory – Planning and Running
Enterprises of the Future. Proceedings of the 26th Annual Conference of the IEEE
Electronics Society, 1286–1291.
[90] Rooks B (1998) A Shorter Product Development Time with Digital Mock-up.
Assembly Automation 18(1):34–38.
[91] Garbade R, Dolezal WR (2007) DMU at Airbus – Evolution of the Digital Mockup (DMU) at Airbus to the Centre of Aircraft Development User Keynote, the
Future of Product Development. Proceedings of the 17th CIRP Design Conference, 3–12.
[92] Pratt MJ, Amnderson BD, Ranger T (2005) Towards the Standardized
Exchange of Parameterized Feature-based CAD Models. Computer-Aided
Design 37(12):1251–1265.
[93] ISO 10303-109 (2004) Industrial Automation Systems and Integration – Product
Data Representation and Exchange. Part 109: Integrated Application Resource:
Kinematic and Geometric Constraints for Assembly Models.
757
[94] Contero M, Company P, Vila C, Aleixios N (2002) Product Data Quality and
Collaborative Engineering. IEEE Computer Graphics and Applications 22(3):32–
42.
[95] ISO 10303-105 (1996) Industrial Automation Systems and Integration – Product
Data Representation and Exchange. Part 105: Integrated Application Resources:
Kinematics.
[96] Kjellberg T, Von Euler-Chelpina A, Hedlinda M, Lundgrena M, Sivarda G,
Chena D (2009) The Machine Tool Model – A Core Part of the Digital Factory.
Annals of the CIRP 58(1):425–428.
[97] Govindaluri MS, Shin S, Cho BR (2004) Tolerance Optimization Using the
Lambert W Function: An Empirical Approach. International Journal of Production Research 42(16):3235–3251.
[98] Singh PK, Jain PK, Jain SC (2009) Important Issues in Tolerance Design of
Mechanical Assemblies. Part 1: Tolerance Analysis. Proceedings of the IMechE
Part B Journal of Engineering Manufacture 223(10):1225–1247.
[99] Singh PK, Jain PK, Jain SC (2009) Important Issues in Tolerance Design of
Mechanical Assemblies. Part.2: Tolerance Synthesis. Proceedings of the IMechE
Part B Journal of Engineering Manufacture 223(10):1249–1287.
[100] Dantan J-Y, Qureshi A-J (2009) Worst-case and Statistical Tolerance Analysis
Based on Quantified Constraint Satisfaction Problems and Monte Carlo
Simulation. Computer-Aided Design 41(1):1–12.
[101] Nigam SD, Turner JU (1995) Review of Statistical Approaches to Tolerance
Analysis. Computer-Aided Design 27(1):6–15.
[102] Shen Z, Ameta G, Shah JJ, Davidson JK (2005) A Comparative Study of
Tolerance Analysis Methods. Journal of Computing and Information Science
in Engineering 5(3):247–256.
[103] Lin CY, Huang WH, Jeng MC, Doong JL (1997) Study of an Assembly Tolerance
Allocation Model Based on Monte Carlo Simulation. Journal of Materials
Processing Technology 70(1–3):9–16.
[104] Spotts MF (1978) Dimensioning Stacked Assemblies. Machine Design
50(9):60–63.
[105] Chun H (2008) Multibody Approach for Tolerance Analysis and Optimization
of Mechanical Systems. Journal of Mechanical Science and Technology
22(2):276–286.
[106] Rivest L, Fortin C, Desrochers A (1993) Tolerance Modeling for 3D Analysispresenting a Kinematic Formulation. Proceedings of 3rd CIRP Seminars on
Computer Aided Tolerancing, France, 51–74.
[107] Bourdet P, Ballot E (1995) Geometrical Behaviour Laws for Computer Aided
Tolerancing. Proceedings of 4th CIRP Seminars on Computer Aided Tolerancing,
Tokyo, 143–154.
[108] Laperrière L, Desrochers A (2001) Modeling Assembly Quality Requirements
Using Jacobian or Screw Transforms: A Comparison. Proceedings of the IEEE
International Symposium on Assembly and Task Planning, 330–336.
[109] Desrochers A, Ghie W, Laperrière L (2003) Application of a Unified JacobianTorsor Model for Tolerance Analysis. ASME Journal of Computing and Information Science in Engineering 3(1):2–14.
[110] Ghie W, Laperrière L, Desrochers A (2009) Statistical Tolerance Analysis
Using the Unified Jacobian-Torsor Model. International Journal of Production
Research . 10.1080/00207540902824982.
[111] Shin S, Govindaluri MS, Cho BR (2005) Integrating the Lambert Function to a
Tolerance Optimization Problem. Quality and Reliability Engineering International 21(8):795–808.
[112] Cheng B-W, Maghsoodloo S (1995) Optimization of Mechanical Assembly
Tolerances by Incorporating Taguchi’s Quality Loss Function. Journal of
Manufacturing Systems 14(4):264–276.
[113] Benanzer TW, Grandhi RV, Krol WP (2009) Reliability-based Optimization of
Design Variance to Identify Critical Tolerances. Advances in Engineering Software 40(4):305–311.
[114] Ballu A, Plantec JY, Mathieu L (2008) Geometrical Reliability of Over Constrained Mechanisms with Gaps. Annals of the CIRP 57(1):159–162.
[115] Jeang A (2001) Computer-aided Tolerance Synthesis with Statistical Method
and Optimization Techniques. Quality and Reliability Engineering International
17(2):131–139.
[116] Gadallah M, ElMaraghy HA (1998) A New Algorithim for Combinatorial
Optimization: Application to Tolerance Synthesis with Optimum Process
Selection. in ElMaraghy HA, (Ed.) Geometric Design Tolerancing: Theories,
Standards and Applications. Kluwer Academic Publ, pp. 265–281.
[117] Curran R, Gomis G, Castagne S, Butterfield J, Edgar T, Higgins C, McKeever C
(2007) Integrated Digital Design for Manufacture for Reduced Life Cycle Cost.
International Journal of Production Economics 109(1–2):27–40.
[118] Salomons OW, Houten FJAMV, Kals HJJ (1993) Review of Research in Featurebased Design. Journal of Manufacturing Systems 12:113–132.
[119] Subrahmanyam S, Wozny M (1995) An Overview of Automatic Feature
Recognition Techniques for Computer-aided Process Planning. Computers
in Industry 26(1):1–21.
[120] Lin AC, Lin SY, Cheng SB (1997) Extraction of Manufacturing Features from a
Feature-based Design Model. International Journal of Production Research
35(12):3249–3288.
[121] ElMaraghy HA, ElMaraghy WH (1994) Computer-aided Inspection Planning
(Caip). in Shah JJ, (Ed.) Advances in Feature Based Manufacturing. Elsevier Publ,
pp. 363–396.
[122] Case K (1994) Using a Design by Features CAD System for Process Capability
Modeling. Computer Integrated Manufacturing Systems 7(1):39–49.
[123] Wong TN, Wong KW (1998) Feature-based Design by Volumetric Machining
Features. International Journal of Production Research 36(10):2839–2862.
[124] Gu Z, Zhang YF, Nee AYC (1997) Identification of Important Features for
Machining Operations Sequence Generation. International Journal of Production Research 35(8):2285–2307.
[125] Laperriere L, ElMaraghy HA (1994) Assembly Sequences Planning for Simulaneous Engineering Applications. Internation Journal of Advanced Manufacturing Technology 9:231–244.
758
P.G. Maropoulos, D. Ceglarek / CIRP Annals - Manufacturing Technology 59 (2010) 740–759
[126] Qiao L, Wang XY, Wang SC (2000) A GA-based Approach to Machining
Operation Sequencing for Prismatic Parts. International Journal of Production
Research 38(14):3283–3303.
[127] Li WD, Ong SK, Nee AYC (2002) Hybrid Genetic Algorithm and Simulated
Annealing Approach for the Optimization of Process Plans for Prismatic Parts.
International Journal of Production Research 40(8):1899–1922.
[128] Ong SK, Ding J, Nee AYC (2002) Hybrid GA and SA Dynamic Set-up Planning
Optimization. International Journal of Production Research 40(18):4697–4719.
[129] Azab A, ElMaraghy HA (2007) A Novel Qap Mathematical Programming
Formulation for Process Planning in Reconfigurable Manufacturing. Proceedings of the 4th International CIRP Conference on Digital Enterprise Technology
(DET’07), Bath, UK, 259–268.
[130] Zhao J, Masood S (1999) An Intelligent Computer-aided Assembly Process
Planning System. International Journal of Advanced Manufacturing Technology
15:332–337.
[131] Wang H, Ceglarek D (2005) Quality-driven Sequence Planning for Compliant
Structure Assemblies. Annals of the CIRP 54(1):31–35.
[132] Jun Y, Liu JH, Ning RX, Zhang Y (2005) Assembly Process Modeling for Virtual
Assembly Process Planning. International Journal of Computer Integrated
Manufacturing 18(6):442–451.
[133] Bullinger HJ, Richter M, Seidel KA (2000) Virtual Assembly Planning. Human
Factors and Ergonomics in Manufacturing 10:331–341.
[134] Jayaram S, Jayaram U, Wang Y, Tirumali H, Lyons K, Hart P (1999) Vade: A
Virtual Assembly Design Environment. Computer Graphics and Applications
IEEE 19(6):44–50.
[135] Banerjee A, Banerjee P, Ye N, Dech F (1999) Assembly Planning Effectiveness
Using Virtual Reality. Presence 8(2):204–217.
[136] Ong SK, Pang Y, Nee AYC (2007) Augmented Reality Aided Assembly Design
and Planning. Annals of the CIRP 56(1):49–52.
[137] Butterfield J, Crosby S, Curran R, Price M, Armstrong CG, Raghunathan S,
McAleenan D, Gibson C (2007) Optimization of Aircraft Fuselage Assembly
Process Using Digital Manufacturing. Journal of Computing and Information
Science in Engineering 7(3):269–275.
[138] Esque S, Mattila J, Siuko M, Vilenius M, Järvenpää J, Semeraro L, Irving M,
Damiani C (2009) The Use of Digital Mock-ups on the Development of the
Divertor Test Platform 2. Fusion Engineering and Design 84(2–6):752–756.
[139] Wöhlke G, Schiller E (2005) Digital Planning Validation in Automotive
Industry. Computers in Industry 56(4):393–405.
[140] Bernard A, Fischer A (2002) New Trends in Rapid Product Development. CIRP
Annals – Manufacturing Technology 51(2):635–652.
[141] Rosochowski A, Matuszak A (2000) Rapid Tooling: The State of the Art. Journal
of Materials Processing Technology 106(1–3):191–198.
[142] Ceglarek D, Li H, Tang Y (2001) Modeling and Optimization of Fixture for
Handling Compliant Sheet Metal Parts. Transactions of ASME Journal of
Manufacturing Science and Engineering 123(3):473–480.
[143] Kong Z, Ceglarek D (2006) Fixture Workspace Synthesis for Reconfigurable
Assembly Systems. Journal of Manufacturing Systems 25(1):25–38.
[144] Phoomboplab T, Ceglarek D (2008) Process Yield Improvement through
Optimal Design of Fixture Layout in 3d Multi-station Assembly Systems.
ASME Transactions Journal of Manufacturing Science and Engineering
130:061005.
[145] Huang W, Lin J, Bezdecny M, Kong Z, Ceglarek D (2007) Stream-of-Variation
(SOVA) Modeling I: A Generic 3d Variation Model for Rigid Body Assembly in
Single Station Assembly Processes. ASME Transactions on Journal of Manufacturing Science and Engineering 129(4):821–831.
[146] Huang W, Lin J, Kong Z, Ceglarek D (2007) Stream-of-Variation (SOVA)
Modeling II: A Generic 3D Variation Model for Rigid Body Assembly in Multi
Station Assembly Processes. ASME Transactions on Journal of Manufacturing
Science and Engineering 129(4):832–842.
[147] Phoomboplab T, Ceglarek D (2007) Design Synthesis Framework for Dimensional Management in Multi-Station Assembly Systems. Annals of the CIRP
56(1):153–158.
[148] Zhao F, Xu X, Xie SQ (2009) Computer-aided Inspection Planning – The State
of the Art. Computers in Industry 60:453–466.
[149] Cho MW, Lee H, Yoon GS, Choi J (2005) A Feature-based Inspection Planning
System for Coordinate Measuring Machines. The International Journal of
Advanced Manufacturing Technology 26:1078–1087.
[150] Lin YJ, Mahabaleshwarkar R, Massina E (2001) CAD-based CMM Dimensional
Inspection Path Planning: A Generic Algorithm. Robotica 19(2):137–148.
[151] Hopp TH (1984) CAD-directed Inspection. Annals of the CIRP 33(1):357–
361.
[152] ElMaraghy HA, Gu P (1987) Expert System for Inspection Planning. Annals of
the CIRP 36(1):85–89.
[153] ElMaraghy HA (1993) Evolution and Future Prespectives of CAPP. Annals of
the CIRP 42:739–751.
[154] ElMaraghy HA (2007) Reconfigurable Process Plans for Responsive Manufacturing Systems. in Cunha PF, Maropoulos PG, (Eds.) Digital Enterprise
Technology: Perspectives and Future Challenges. Springer, US, Boston , pp.
35–44.
[155] Lee H, Cho MW, Yoon GS, Choi JH (2004) A Computer-Aided Inspection
Planning System for On-machine Measurement-Part I: Global Inspection
Planning. KSME International Journal 18(8):1349–1357.
[156] Limaiem A, ElMaraghy HA (1999) CATIP: A Computer-aided Tactile Inspection Planning System. International Journal of Production Research 37(2):447–
465.
[157] Zhang SG, Ajmal A, Wootton J, Chisholm A (2000) A Feature-Based Inspection
Process Planning System for Coordinate Measuring Machine. Journal of
Materials Processing Technology 107:111–118.
[158] Hwang CY, Tsai CY, Chang CA (2004) Efficient Inspection Planning for
Coordinate Measuring Machines. The International Journal of Advanced Manufacturing Technology 23(9–10):732–742.
[159] Moroni G, Polini W, Semeraro Q (1998) Knowledge Based Method for Touch
Probe Configuration in an Automated Inspection System. Journal of Materials
Processing Technology 76(1–3):153–160.
[160] Lu CG, Morton D, Wu MH, Myler P (1999) Genetic Algorithm Modelling and
Solution of Inspection Path Planning on a Coordinate Measuring Machine. The
International Journal of Advanced Manufacturing Technology 15(6):409–416.
[161] Beg J, Shunmugam MS (2003) Application of Fuzzy Logic in the Selection of
Part Orientation and Probe Orientation Sequencing for Prismatic Parts.
International Journal of Production Research 41(12):2799–2815.
[162] Beg J, Shunmugam MS (2002) An Object Oriented Planner for Inspection of
Prismatic Parts: OOPIPP. The International Journal of Advanced Manufacturing
Technology 19(12):905–916.
[163] Mohib A, Azab A, ElMaraghy H (2009) Feature-based Hybrid Inspection
Planning: A Mathematical Programming Approach. International Journal of
Computer Integrated Manufacturing 22(1):13–29.
[164] Baxter D, Gao J, Case K, Harding J, Young B, Cochrane S, Dani S (2007) An
Engineering Design Knowledge Reuse Methodology Using Process Modeling.
Research in Engineering Design 18(1):37–48.
[165] Maropoulos P, Bramall D, McKay K (2003) Assessing the Manufacturability of
Early Product Designs Using Aggregate Process Models. Proceedings of IMechE
Part B Journal of Engineering Manufacture 217:1203–1214.
[166] Maropoulos P, Guo Y, Jamshidi J, Cai B (2008) Large Volume Metrology
Process Models: A Framework for Integrating Measurement with Assembly
Planning. Annals of the CIRP 57:477–480.
[167] Maropoulos P, Rogers B, Chapman P, McKay K, Bramall D (2003) A Novel
Digital Enterprise Technology Framework for the Distributed Development
and Validation of Complex Products. Annals of the CIRP 52(1):389–392.
[168] Cai B, Guo Y, Jamshidi J, Maropoulos PG (2008) Large Volume Measurability
Analysis for Early Design. Proceedings of the 5th International Conference on
Digital Enterprise Technology, Nantes, France, 807–819.
[169] ISO/IEC Guide (1995) General Metrology. Part 3: Guide to the Expression of
Uncertainty in Measurement (GUM).
[170] ASME B89.4.19 (2006) Performance Evaluation of Laser-based Spherical Coordinate Measurement Systems.
[171] Peggs G, Maropoulos P, Hughes E, Forbes A, Robson S, Ziebart M, Muralikrishnan B (2009) Recent Developments in Large-scale Dimensional Metrology. Proceedigns of IMechE Part B Journal of Engineering Manufacture
223(6):571–595.
[172] Estler W, Edmundson K, Peggs G, Parker D (2002) Large-scale Metrology – An
Update. Annals of the CIRP 51(2):587–609.
[173] Cai B, Dai W, Muelaner J, Maropoulos P (2009) Measurability Characteristics
Mapping for Large Volume Metrology Instruments Selection. Proceedings of
the 7th International Conference on Manufacturing Research (ICMR09), Warwick, UK, 438–442.
[174] Muelaner J, Cai B, Maropoulos P (2010) Large Volume Metrology Instrument
Selection and Measurability Analysis. Proceedings of IMechE Part B Journal of
Engineering Manufacture . 10.1243/09544054JEM09541676.
[175] Cuypers W, Van Gestel N, Voet A, Kruth J, Mingneau J, Bleys P (2009) Optical
Measurement Techniques for Mobile and Large-scale Dimensional Metrology. Optical Measurements 47(3–4):292–300.
[176] Morris AJ (1996) The Qualification of Safety Critical Structures by Finite
Element Analysis Methods. Proceedigns of IMechE Part G Journal of Aerospace
Engineering ;(210)203–208.
[177] Rossi M, Meo M (2009) On the Estimation of Mechanical Properties of Singlewalled Carbon Nanotubes by Using a Molecular-mechanics Based Fe
Approach. Composites Science and Technology 69(9):1394–1398.
[178] Guida M, Meo M, Riccio M, Marulo F (2008) Analysis of Bird Impact on a
Composite Tailplane Leading Edge. Applied Composite Materials 15(4–6):241–
257.
[179] Denton JD, Dawes WN (1998) Computational Fluid Dynamics for Turbomachinery Design. Proceedings of IMechE Part C Journal of Mechanical Engineering
Science 213:107–124.
[180] Samad A, Kim KY (2008) Shape Optimization of an Axial Compressor Blade by
Multi-objective Genetic Algorithm. Proceedings of IMechE Part A Journal of
Power and Energy 222(6):599–611.
[181] Chew JW, Hills NJ (2007) Computational Fluid Dynamics for Turbomachinery
Internal Air Systems. Philosophical Transactions of the Royal Society (Series
A): Theme Issue. Computational Fluid Dynamics in Aerospace Engineering
365:2587–2611.
[182] Spalart PR, Allmaras SR (1994) A One-equation Turbulence Model for Aerodynamic Flows. Recherche Aerospatiale 1:5–21.
[183] Shur ML, Strelets MK, Travin AK, Spalart PR (2000) Turbulence Modeling in
Rotating and Curved Channels: Assessing the Spalart–Shur Correction. AIAA
Journal 38(5):784–792.
[184] Dawes WN (2008) Rapid Prototyping Design Optimization Using Flow
Sculpting. Journal of Turbomachinery 130(3). 10.1115/1111.2777178.
[185] Morton S, Steenman M, Cummings R, Forsythe J (2003) Des Grid Resolution
Issues for Vortical Flows on a Delta Wing and an F/A-18c. AIAA Journal . 20031103.
[186] DD CEN ISO/TS 17450-1 (2007) Geometrical Product Specifications (GPS) –
General Concepts. Part 1: Model for Geometrical Specification and Verification.
[187] BS EN ISO 14253-1 (1999) Geometrical Product Specifications (GPS) – Inspection by Measurement of Workpieces and Measuring Equipment. Part 1: Decision
Rules for Proving Conformance or Non-Conformance with Specifications.
[188] Savio E, Chiffre LD, Schmitt R (2007) Metrology of Freeform Shaped Parts.
CIRP Annals – Manufacturing Technology 56(2):810–835.
[189] Shortis MR, Clarke TA, Robson S (1995) Practical Testing of the Precision and
Accuracy of Target Image Centring Algorithms. Proceedings of the SPIE
2598:65–76.
[190] Dorsch RG, Hausler G, Herrmann JM (1994) Laser Triangulation: Fundamental Uncertainty in Distance Measurement. Applied Optics 33(7):1306–1314.
P.G. Maropoulos, D. Ceglarek / CIRP Annals - Manufacturing Technology 59 (2010) 740–759
[191] Huntley JM (1999) Simple Model for Image-plane Polychromatic Speckle
Contrast. Applied Optics 38(11):2212–2215.
[192] DD CE ISO/TS 12780-2 (2007) Geometrical Product Specifications (GPS) –
Straightness Part 2: Specification Operators.
[193] DD CE ISO/TS 12181-2 (2007) Geometrical Product Specifications (GPS) –
Roundness Part 2: Specification Operators.
[194] DD CE ISO/TS 12180-2 (2007) Geometrical Product Specifications (GPS) –
Cylindricity Part 2: Specification Operators.
[195] Kimberley W (2004) The Drive for Quality. Automotive Engineer 29(9):40–42.
[196] Quin WH, Hsieh LH, Seliqer G (1996) On the Optimization of Automobile
Panel Fitting. Proceedings of the IEEE conference on Robotics and Automation,
Minneapolis, MN, USA, 1268–1274.
[197] Ciddor PE (1996) Refractive Index of Air: New Equations for the Visible and
near Infrared. Applied Optics 35:1566–1573.
[198] Muelaner J, Wang Z, Jamshidi J, Maropoulos PG, Mileham AR, Hughes EB,
Forbes AB (2008) iGPS – An Initial Assessment of Technical and Deployment
Capability. Proceedings of the 3rd International Conference on Manufacturing
Engineering, Kassandra-Chalkidiki, Greece, 805–810.
[199] BS EN ISO 1101 (2005) Geometrical Product Specifications (GPS) – Tolerances of
Form, Orientation, Location and Run-Out.
[200] Ashby MF (2005) Materials Selection in Mechanical Design. Elsevier, Butterworth-Heinemann.
[201] CES EduPack (2009) Materials Selection Software. Granta Design.
[202] Hodgkinson JM (2000) Mechanical Testing of Advanced Fibre Composites.
Woodhead Publishing Ltd.
[203] Sutton MA, McNeill SR, Helm JD, Chao YJ (2000) in Rastogi PK, (Ed.)
Photomechanics. Springer-Verlag, pp. 323–372.
[204] William LO, Timothy GT (2002) Verification and Validation in Computational
Fluid Dynamics. Progress in Aerospace Sciences 38:209–272.
[205] Williams NM, Wang Z, Gursul I (2008) Active Flow Control on a Nonslender
Delta Wing. Journal of Aircraft 45(6):2100–2110.
[206] Gursul I, Wang Z, Vardaki E (2007) Review of Flow Control Mechanisms of
Leading-edge Vortice. Progress in Aerospace Sciences 43:246–270.
[207] Willert CE, Gharib M (1992) Three-dimensional Particle Imaging with a
Single Camera. Experiments in Fluids 12:353–358.
[208] Pereira F, Gharib M, Dabiri D, Modarress D (2002) Defocusing Digital Particle
Image Velocimetry: A 3-Component 3-Dimensional DPIV Measurement
Technique: Application to Bubbly Flows. Experiments in Fluids 78–84.
[209] Motorcu AR, Gullu A (2006) Statistical Process Control in Machining, a Case
Study for Machine Tool Capability and Process Capability. Materials and
Design 27(5):364–372.
[210] Huang W, Kong Z (2008) Process Capability Sensitivity Analysis for Design
Evaluation of Multi Station Assembly Systems. Proceedings of the IEEE International Conference on Automation Science and Engineering, Washington DC,
USA, 400–405.
[211] Douglas CM, William HW (2008) An Overview of Six Sigma. International
Statistical Review 76(3):329–346.
[212] Hahn GJ (2005) Six Sigma: 20 Key Lessons Learned. Quality and Reliability
Engineering International 21(3):225–233.
[213] Shiu W, Apley D, Ceglarek D, Shi J (2003) Tolerance Allocation for Sheet Metal
Assembly Using Beam-Based Model. Transactions of IIE Design and Manufacturing 35(4):329–342.
[214] Ceglarek D, Shi J (1996) Fixture Failure Diagnosis for Autobody Assembly
Using Pattern Recognition. ASME Transactions Journal of Engineering Industry
118(1):55–66.
[215] Ding Y, Ceglarek D, Shi J (2002) Fault Diagnosis of Multistage Manufacturing
Assembly Processes by Using State Space Approach. ASME Transactions
Journal of Manufacturing Science and Engineering 124(2):313–322.
[216] Rong Q, Shi J, Ceglarek D (2001) Adjusted Least Squares Approach for
Diagnosis of Compliant Assemblies in the Presence of III-Conditioned Problems. ASME Transactions Journal of Manufacturing Science and Engineering
123(3):453–461.
[217] Liu G, Hu J (2005) Assembly Fixture Fault Diagnosis Using Designated
Component Analysis. ASME Transactions Journal of Manufacturing Science
and Engineering 127(2):358–368.
[218] Apley D, Lee HY (2003) Identifying Spatial Variation Patterns in Multivariate
Manufacturing Processes: A Blind Separation Approach. Technometrics
45(3):220–234.
[219] Ceglarek D, Prakash. Tripathi A, Kong Z (2007) Diagnosis of Product Failures
in Ill-Conditioned Multi-Station Assembly Systems Using Enhanced Pls
759
Method. Proceedings of the 40th CIRP International Manufacturing Systems
Seminar, Liverpool, UK, .
[220] Maisano DA, Jamshidi J, Franceschini F, Maropoulos PG, Mastrogiacomo L,
Mileham AR, Owen GW (2009) A Comparison of Two Distributed Largevolume Measurement Systems: The Mobile Spatial Co-ordinate Measuring
System and the Indoor Global Positioning System. Proceedings of IMechE Part
B Journal of Engineering Manufacture 223(5):511–521.
[221] Jahangirian M, Eldabi T, Naseer A, Stergioulas LK, Young T (2010) Simulation
in Manufacturing and Business: A Review. European Journal of Operations
Research 203(1):1–13.
[222] Robinson S (2004) Simulation – The Practice of Model Development and Use.
Wiley.
[223] Jamshidi J, Kayani A, Iravani P, Maropoulos PG, Summers MD (2010) Manufacturing and Assembly Automation by Integrated Metrology Systems for
Aircraft Wing Fabrication. Proceedigns of IMechE Part B Journal of Engineering
Manufacture 224(1):25–36.
[224] Zhong Y, Shirinzadeh B (2005) Virtual Factory for Manufacturing Process
Visualization. Complexity International 12:1–12.
[225] Johansson B, Johnsson J, Kinnander A (2003) Information Structure to Support Discrete Event Simulation in Manufacturing Systems. Proceedings of the
2003 Winter Simulation Conference, LA, USA, .
[226] Huang GQ, Wright PK, Newman ST (2009) Wireless Manufacturing: A Literature Review. Recent Developments and Case Studies International Journal of
Computer Integrated Manufacturing 22(7):1–16.
[227] Huang GQ, Zhang YF, Dai QY, Ho O, Xu FJ (2009) Agent-based Workflow
Management for RFID-enabled Real-time Reconfigurable Manufacturing Collaborative Design and Planning for Digital Manufacturing. Springer, London. pp.
341–364.
[228] Zhang Y, Pingyu J, Huang G (2008) RFID-based Smart Kanbans for Just-inTime Manufacturing. International Journal of Materials and Product Technology
33(1/2):170–184.
[229] Intermec Technologies Corporation (2007) White Paper on Supply Chain RFID:
How It Works and Why It Pays.
[230] Lee YM, Cheng F, Leung YT (2004) Exploring the Impact of RFID Supply Chain
Dynamics. Proceedings of the Winter Simulation Conference, 1145–1152.
[231] Jun HB, Shin JH, Kim YS, Kiritsis D, Xirouchakis P (2009) A Framework for RFID
Applications in Product Lifecycle Management. International Journal of Computer Integrated Manufacturing 22(7):595–615.
[232] Cheung W, Shaefer D (2009) Product Lifecycle Management: State-of-the-Art
and Future Perspectives. in Cruz-Cunha M, (Ed.) Enterprise Information Systems for Business Integration in Smes: Technological, Organizational, and Social
Dimensions. 37–55.
[233] Stark J (2005) Product Lifecycle Management: 21st Century Paradigm for
Product Realisation. Springer-Verlag, London.
[234] Ameri F, Dutta D (2005) Product Lifecycle Management: Closing the Knowledge Loops. Computer-Aided Design and Applications 2(5):577–590.
[235] Abramovici M, Sieg O (2002) Status and Development Trends of Product
Lifecycle Management Systems. Proceedings of the IPPD 2002 Wroclaw, .
[236] Rachuri S, Subrahmanian E, Bouras A, Fenves SJ, Foufou S, Sriram RD (2008)
Information Sharing and Exchange in the Context of Product Lifecycle
Management: Role of Standards. Computer-Aided Design 40(7):789–800.
[237] Peak RS, Lubell J, Srinivasan V, Waterbury SC (2004) STEP, XML, and UML:
Complementary Technologies. Journal of Computing and Information Science
in Engineering 4(4):379–390.
[238] Ming XG, Yan JQ, Wang XH, Li SN, Lu WF, Peng QJ, Ma YS (2008) Collaborative
Process Planning and Manufacturing in Product Lifecycle Management.
Computers in Industry 59(2–3):154–166.
[239] Gielingh W (2008) An Assessment of the Current State of Product Data
Technologies. Computer-Aided Design 40(7):750–759.
[240] Pickering SG, Brace CJ (2007) Automated Data Processing and Metric Generation for Driveability Analysis. Proceedings of IMechE Part D Journal of
Automobile Engineering 221(4):429–441.
[241] Zhao F, Xu X, Xie S (2009) Computer-Aided Inspection Planning-the State of
the Art. Computers in Industry 60(7):453–466.
[242] Beaman J, Morse E (2010) Experimental Evaluation of Software Estimates of
Task Specific Measurement Uncertainty for Cmms. Precision Engineering
34(1):28–33.
[243] Germani M, Mandorli F, Mengoni M, Raffaeli R (2010) CAD-based Environment to Bridge the Gap between Product Design and Tolerance Control.
Precision Engineering 34(1):7–15.