Robotics and Computer-Integrated Manufacturing 27 (2011) 1–12
Contents lists available at ScienceDirect
Robotics and Computer-Integrated Manufacturing
journal homepage: www.elsevier.com/locate/rcim
Review
A review and analysis of current computer-aided fixture design approaches
Iain Boyle a,n, Yiming Rong a, David C. Brown b
a
b
CAM Lab, Department of Mechanical Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, USA
Computer Science Department, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, USA
a r t i c l e in fo
abstract
Article history:
Received 17 September 2009
Received in revised form
12 May 2010
Accepted 27 May 2010
A key characteristic of the modern market place is the consumer demand for variety. To respond
effectively to this demand, manufacturers need to ensure that their manufacturing practices are
sufficiently flexible to allow them to achieve rapid product development. Fixturing, which involves
using fixtures to secure workpieces during machining so that they can be transformed into parts that
meet required design specifications, is a significant contributing factor towards achieving manufacturing flexibility. To enable flexible fixturing, considerable levels of research effort have been devoted to
supporting the process of fixture design through the development of computer-aided fixture design
(CAFD) tools and approaches. This paper contains a review of these research efforts. Over seventy-five
CAFD tools and approaches are reviewed in terms of the fixture design phases they support and the
underlying technology upon which they are based. The primary conclusion of the review is that while
significant advances have been made in supporting fixture design, there are primarily two research
issues that require further effort. The first of these is that current CAFD research is segmented in nature
and there remains a need to provide more cohesive fixture design support. Secondly, a greater focus is
required on supporting the detailed design of a fixture’s physical structure.
& 2010 Elsevier Ltd. All rights reserved.
Keywords:
Computer-aided fixture design
Fixture design
Fixture planning
Fixture verification
Setup planning
Unit design
Contents
1.
2.
3.
4.
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Fixture design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Current CAFD approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3.1.
Setup planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3.1.1.
Approaches to setup planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3.2.
Fixture planning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3.2.1.
Approaches to defining the fixturing requirement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3.2.2.
Approaches to non-optimized layout planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3.2.3.
Approaches to layout planning optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
3.3.
Unit design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.3.1.
Approaches to conceptual unit design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.3.2.
Approaches to detailed unit design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.4.
Verification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.4.1.
Approaches to constraining requirements verification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.4.2.
Approaches to tolerance requirements verification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.4.3.
Approaches to collision detection requirements verification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3.4.4.
Approaches to usability and affordability requirements verification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.5.
Representation of fixturing information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
An analysis of CAFD research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
4.1.
The segmented nature of CAFD research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
4.2.
Effectively supporting unit design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.3.
Comprehensively formulating the fixturing requirement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
n
Corresponding author. Current address: Department of Design, Manufacture and Engineering Management, University of Strathclyde, James Weir Building, 75 Montrose
Street, Glasgow G1 1XJ, UK. Tel.: + 44 141 548 2374; fax: + 44 141 552 0557.
E-mail address: iain.m.boyle@strath.ac.uk (I. Boyle).
0736-5845/$ - see front matter & 2010 Elsevier Ltd. All rights reserved.
doi:10.1016/j.rcim.2010.05.008
2
I. Boyle et al. / Robotics and Computer-Integrated Manufacturing 27 (2011) 1–12
5.
4.4.
Validating CAFD research outputs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1. Introduction
A key concern for manufacturing companies is developing the
ability to design and produce a variety of high quality products
within short timeframes. Quick release of a new product into the
market place, ahead of any competitors, is a crucial factor in being
able to secure a higher percentage of the market place and
increased profit margin. As a result of the consumer desire for
variety, batch production of products is now more the norm than
mass production, which has resulted in the need for manufacturers to develop flexible manufacturing practices to achieve a
rapid turnaround in product development.
A number of factors contribute to an organization’s ability to
achieve flexible manufacturing, one of which is the use of fixtures
during production in which workpieces go through a number of
machining operations to produce individual parts which are
subsequently assembled into products. Fixtures are used to rapidly,
accurately, and securely position workpieces during machining such
that all machined parts fall within the design specifications for that
part. This accuracy facilitates the inter-changeability of parts that is
prevalent in much of modern manufacturing where many different
products feature common parts.
The costs associated with fixturing can account for 10–20% of
the total cost of a manufacturing system [1]. These costs relate not
only to fixture manufacture, assembly, and operation, but also to
their design. Hence there are significant benefits to be reaped by
reducing the design costs associated with fixturing and two
approaches have been adopted in pursuit of this aim. One has
concentrated on developing flexible fixturing systems, such as the
use of phase-changing materials to hold workpieces in place [2]
and the development of commercial modular fixture systems.
However, the significant limitation of the flexible fixturing mantra
is that it does not address the difficulty of designing fixtures. To
combat this problem, a second research approach has been to
develop computer-aided fixture design (CAFD) systems that
support and simplify the fixture design process and it is this
research that is reviewed within this paper.
Section 2 describes the principal phases of and the wide
variety of requirements driving the fixture design process.
locating unit
Subsequently in Section 3 an overview of research efforts that
have focused upon the development of techniques and tools for
supporting these individual phases of the design process is
provided. Section 4 critiques these efforts to identify current gaps
in CAFD research, and finally the paper concludes by offering
some potential directions for future CAFD research. Before
proceeding, it is worth noting that there have been previous
reviews of fixturing research, most recently Bi and Zhang [1] and
Pehlivan and Summers [3]. Bi and Zhang, while providing some
details on CAFD research, tend to focus upon the development of
flexible fixturing systems, and Pehlivan and Summers focus upon
information integration within fixture design. The value of this
paper is that it provides an in-depth review and critique of current
CAFD techniques and tools and how they provide support across
the entire fixture design process.
2. Fixture design
This section outlines the main features of fixtures and more
pertinently of the fixture design process against which research
efforts will be reviewed and critiqued in Sections 3 and 4,
respectively. Physically a fixture consists of devices that support
and clamp a workpiece [4,5]. Fig. 1 represents a typical example of
a fixture in which the workpiece rests on locators that accurately
locate it. Clamps hold the workpiece against the locators during
machining thus securing the workpiece’s location. The locating
units themselves consist of the locator supporting unit and the
locator that contacts the workpiece. The clamping units consist of
a clamp supporting unit and a clamp that contacts the workpiece
and exerts a clamping force to restrain it.
Typically the design process by which such fixtures are created
has four phases: setup planning, fixture planning, unit design, and
verification, as illustrated in Fig. 2, which is adapted from Kang
et al. [6]. During setup planning workpiece and machining
information is analyzed to determine the number of setups
required to perform all necessary machining operations and the
appropriate locating datums for each setup. A setup represents
the combination of processes that can be performed on a
clamp
baseplate
clamp supporting
unit
workpiece
Fig. 1. A typical fixture (a) without and (b) with a workpiece.
I. Boyle et al. / Robotics and Computer-Integrated Manufacturing 27 (2011) 1–12
workpiece CAD model,
machining information,
design considerations
Setup planning:
Identify setups
Determine locating datums
Fixture planning:
Define fixturing requirements
Determine fixture layout plan
Unit design:
Conceptual unit design
Detailed unit design
Verification:
Verify fixture against fixturing
requirements (Table 1)
finished setup plan,
fixture design,
materials listing
Fig. 2. The fixture design process (adapted from Kang et al. [6]).
3
can be grouped into six classes (Table 1). The ‘‘physical’’
requirements class is the most basic and relates to ensuring the
fixture can physically support the workpiece. The ‘‘tolerance’’
requirements relate to ensuring that the locating tolerances are
sufficient to locate the workpiece accurately and similarly the
‘‘constraining’’ requirements focus on maintaining this accuracy
as the workpiece and fixture are subjected to machining forces.
The ‘‘affordability’’ requirements relate to ensuring the fixture
represents value, for example in terms of material, operating, and
assembly/disassembly costs.
The ‘‘collision detection’’ requirements focus upon ensuring
that the fixture does not collide with the machining path, the
workpiece, or indeed itself. The ‘‘usability’’ requirements relate to
fixture ergonomics and include for example needs related to
ensuring that a fixture features error-proofing to prevent
incorrect insertion of a workpiece, and chip shedding, where the
fixture assists in the removal of machined chips from the
workpiece.
As with many design situations, the conflicting nature of these
requirements is problematic. For example a heavy fixture can be
advantageous in terms of stability but can adversely affect cost
(due to increased material costs) and usability (because the
increased weight may hinder manual handling). Such conflicts
add to the complexity of fixture design and contribute to the need
for the CAFD research reviewed in Section 3.
Table 1
Fixturing requirements.
Generic
requirement
Abstract sub-requirement examples
Physical
The fixture must be physically capable of accommodating
the workpiece geometry and weight.
The fixture must allow access to the workpiece features to
be machined.
Tolerance
The fixture locating tolerances should be sufficient to satisfy
part design tolerances.
Constraining
The fixture shall ensure workpiece stability (i.e., ensure that
workpiece force and moment equilibrium are maintained).
The fixture shall ensure that the fixture/workpiece stiffness
is sufficient to prevent deformation from occurring that
could result in design tolerances not being achieved.
Fig. 3. The six degrees of freedom.
workpiece without having to alter the position or orientation of
the workpiece manually. To generate a fixture for each setup the
fixture planning, unit design, and verification phases are executed.
During fixture planning, the fixturing requirements for a setup
are generated and the layout plan, which represents the first step
towards a solution to these requirements is generated. This layout
plan details the workpiece surfaces with which the fixture’s
locating and clamping units will establish contact, together with
the surface positions of the locating and clamping points. The
number and position of locating points must be such that a
workpiece’s six degrees of freedom (Fig. 3) are adequately
constrained during machining [7] and there are a variety of
conceptual locating point layouts that can facilitate this, such as
the 3-2-1 locating principle [4]. In the third phase, suitable unit
designs (i.e., the locating and clamping units) are generated and
the fixture is subsequently tested during the verification phase to
ensure that it satisfies the fixturing requirements driving the
design process. It is worth noting that verification of setups and
fixture plans can take place as they are generated and prior to unit
design.
Fixturing requirements, which although not shown in Kang
et al. [6] are typically generated during the fixture planning phase,
Affordability
The fixture cost shall not exceed desired levels.
The fixture assembly/disassembly times shall not exceed
desired levels.
The fixture operation time shall not exceed desired levels.
Collision
prevention
The fixture shall not cause toolpath–fixture collisions to
occur.
The fixture shall cause workpiece–fixture collisions to occur
Usability
(other than at the designated locating and clamping
positions).
The fixture shall not cause fixture–fixture collisions to occur
(other than at the designated fixture component connection
points).
The fixture weight shall not exceed desired levels.
The fixture shall not cause surface damage at the
workpiece/fixture interface.
The fixture shall provide tool guidance to designated
workpiece features.
The fixture shall ensure error-proofing (i.e., the fixture
should prevent incorrect insertion of the workpiece into the
fixture).
The fixture shall facilitate chip shedding (i.e., the fixture
should provide a means for allowing machined chips to flow
away from the workpiece and fixture).
4
I. Boyle et al. / Robotics and Computer-Integrated Manufacturing 27 (2011) 1–12
3. Current CAFD approaches
This section describes current CAFD research efforts, focusing
on the manner in which they support the four phases of fixture
design. Table 2 provides a summary of research efforts based
upon the design phases they support, the fixture requirements
they seek to address (bold text highlights that the requirement is
addressed to a significant degree of depth, whilst normal text that
the degree of depth is lesser in nature), and the underlying
technology upon which they are primarily based. Sections 3.1–3.4
describes different approaches for supporting setup planning,
fixture planning, unit design, and verification, respectively. In
addition, Section 3.5 discusses CAFD research efforts with regard
to representing fixturing information.
3.1. Setup planning
Setup planning involves the identification of machining setups,
where an individual setup defines the features that can be
machined on a workpiece without having to alter the position or
orientation of the workpiece manually. Thereafter, the remaining
phases of the design process focus on developing individual
fixtures for each setup that secure the workpiece. From a fixturing
viewpoint, the key outputs from the setup planning stage are the
identification of each required setup and the locating datums (i.e.,
the primary surfaces that will be used to locate the workpiece in
the fixture).
The key task within setup planning is the grouping or
clustering of features that can be machined within a single setup.
Machining features can be defined as the volume swept by a
cutting tool, and typical examples include holes, slots, surfaces,
and pockets [8]. Clustering of these features into individual setups
is dependent upon a number of factors (including the tolerance
dependencies between features, the capability of the machine
tools that will be used to create the features, the direction of the
cutting tool approach, and the feature machining precedence
order), and a number of techniques have been developed to
support setup planning. Graph theory and heuristic reasoning are
the most common techniques used to support setup planning,
although matrix based techniques and neural networks have also
been employed.
3.1.1. Approaches to setup planning
The use of graph theory to determine and represent setups has
been a particularly popular approach [9–11]. Graphs consist of
two sets of elements: vertices, which represent workpiece
features, and edges, which represent the relationships that exist
between features and drive setup identification. Their nature can
vary, for example in Sarma and Wright [9] consideration of
feature machining precedence relationships is prominent,
whereas Huang and Zhang [10] focus upon the tolerance
relationships that exist between features. Given that these edges
can be weighted in accordance with the tolerance magnitudes,
this graph approach can also facilitate the identification of setups
that can minimize tolerance stack up errors between setups
through the grouping of tight tolerances. However, this can prove
problematic given the difficulty of comparing the magnitude of
different tolerance types to each other thus Huang [12] includes
the use of tolerance factors [13] as a means of facilitating such
comparisons, which are refined and extended by Huang and Liu
[14] to cater for a greater variety of tolerance types and the case of
multiple tolerance requirements being associated with the same
set of features.
While some methods use undirected graphs to assist setup
identification [11], Yao et al. [15], Zhang and Lin [16], and Zhang
et al. [17] use directed graphs that facilitate the determination
and explicit representation of which features should be used as
locating datums (Fig. 4) in addition to setup identification and
sequencing. Also, Yao et al. refine the identified setups through
consideration of available machine tool capability in a two stage
setup planning process.
Experiential knowledge, in the form of heuristic reasoning, has
also been used to assist setup planning. Its popularity stems from
the fact that fixture design effectiveness has been considered to
be dependent upon the experience of the fixture designer [18]. To
support setup planning, such knowledge has typically been held
in the form of empirically derived heuristic rules, although object
oriented approaches have on occasion been adopted [19]. For
example Gologlu [20] uses heuristic rules together with geometric
reasoning to support feature clustering, feature machining
precedence, and locating datum selection. Within such heuristic
approaches, the focus tends to fall upon rules concerning the
physical nature of features and machining processes used to
create them [21,22]. Although some techniques do include feature
tolerance considerations [23], their depth of analysis can be less
than that found within the graph based techniques [24]. Similarly,
kinematic approaches [25] have been used to facilitate a deeper
analysis of the impact of tool approach directions upon feature
clustering than is typically achieved using rule-based approaches.
However, it is worth noting that graph based approaches are often
augmented with experiential rule-bases to increase their overall
effectiveness [16].
Matrix based approaches have also been used to support setup
planning, in which a matrix defining feature clusters is generated
and subsequently refined. Ong et al. [26] determine a feature
precedence matrix outlining the order in which features can be
machined, which is then optimized against a number of cost
indicators (such as machine tool cost, change over time, etc.) in a
hybrid genetic algorithm-simulated annealing approach through
consideration of dynamically changing machine tool capabilities.
Hebbal and Mehta [27] generate an initial feature grouping
matrix based upon the machine tool approach direction for each
feature which is subsequently refined through the application of
algorithms that consider locating faces and feature tolerances.
Alternatively, the use of neural networks to support setup
planning has also been investigated. Neural networks are
interconnected networks of simple elements, where the interconnections are ‘‘learned’’ from a set of example data. Once
educated, these networks can generate solutions for new
problems fed into the network. Ming and Mak [28] use a neural
network approach in which feature precedence, tool approach
direction, and tolerance relationships are fed into a Kohonen selforganizing neural network to group operations for individual
features into setups.
3.2. Fixture planning
Fixture planning involves the comprehensive definition of
a fixturing requirement in terms of the physical, tolerance,
constraining, affordability, collision prevention, and usability
requirements listed in Table 1, and the creation of a fixture
layout plan. The layout plan represents the first part of the fixture
solution to these requirements, and specifies the position of the
locating and clamping points on the workpiece. Many layout
planning approaches feature verification, particularly with regard
to the constraining requirements. Typically this verification forms
part of a feedback loop that seeks to optimize the layout plan with
respect to these requirements. Techniques used to support fixture
planning are now discussed with respect to fixture requirement
definition, layout planning, and layout optimization.
I. Boyle et al. / Robotics and Computer-Integrated Manufacturing 27 (2011) 1–12
5
Table 2
Current CAFD approaches.
Research effort
Amaral et al. [74]
An et al. [63]
Bansal et al. [23]
Boyle et al. [31]
Brost and Goldberg [40]
Cai et al. [25]
Camelio et al. [79]
Cecil [66]
Deng and Melkote [30]
Gologlu [20]
Hebbal and Mehta [27]
Hu and Rong [84]
Huang [12]
Huang and Liu [14]
Huang and Zhang [10]
Hunter et al. [32]
Hunter et al. [33]
Hurtado and Melkote [68]
Hurtado and Melkote [67]
Joneja and Chang [34]
Kang et al. [6]
Kashyap and DeVries [44]
Kaya [47]
Kim et al. [24]
Kong and Ceglarek [53]
Krishnakumar and Melkote [45]
Krishnakumar et al. [46]
Kumar and Nee [18]
Kumar et al. [60]
Kumar et al. [59]
Kumar et al. [82]
Lee et al. [54]
Li et al. [36]
Li and Melkote [51]
Liao and Hu [29]
Lin and Huang [38]
Liu and Strong [70]
Mervyn et al. [88]
Mervyn et al. [65]
De Meter [50]
Ming and Mak [28]
Nee and Kumar [35]
Nnaji and Alladin [55]
Ong et al. [26]
Pelinescu and Wang [42]
Peng et al. [64]
Perremans [57]
Rai and Xirouchakis [76]
Rameshbabu and Shunmugam [21]
Ratchev et al. [75]
Roy and Liao [83]
Roy and Liao [39]
Roy and Liao [72]
Ryll et al. [81]
Sarma and Wright [9]
Satyanarayana and Melkote [78]
Siebenaler and Melkote [77]
Song and Rong [71]
Trappey and Liu [69]
Vallapuzha et al. [48]
Waiyagan and Bohez [22]
Wang [80]
Wang and Rong [37]
Wang et al. [41]
Wu et al. [58]
Wu et al. [61]
Wu and Chan [43]
Wu and Zhang [19]
Wu et al. [73]
Yao et al. [15]
Zhang et al. [17]
Zhang and Lin [16]
Zhou et al. [11]
SP
FP
UD
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V
Requirements considered
Underlying technology
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PR, CR
PR
PR, TR, CR
PR, TR, AR, CR, CDR, UR
PR, CR
PR, TR
PR, TR
PR, CR
PR, CR
PR, TR
PR, TR, AR
CDR
PR, TR
PR, TR
PR, TR
PR, TR
PR, TR, CR
PR, CR
PR, CR
PR, CR
PR, TR, CR
PR, CR
PR, CR
PR, TR
PR
PR, CR
PR, CR
PR, TR, CR, UR
PR, AR
PR
PR, CDR
PR
PR
PR, CR
PR, CR
PR
PR, CR
PR
PR, CR
PR, CR
PR, TR
PR, TR, CR, AR, CDR
PR, CR
PR, AR
PR, CR
PR, CDR
PR
PR, CR
PR
PR, CR
PR, CR, CDR
PR, CR
PR, CR
PR, TR, CDR
PR, AR
PR, CR
PR, CR
PR, CR
PR, CR
PR, CR
PR
PR, TR
PR, TR
PR, CR
PR
PR, TR, CR
PR, CR
PR, TR
PR, CR, UR
PR, TR, AR
PR, TR
PR, TR
PR, TR
Displacement optimization
Parametric modeling
Rule-based approach, tolerance sensitivity analysis
Case-based reasoning with displacement analysis
Geometry and graphical force analysis
Kinematic algorithm
Kinematic variation analysis
Stress fracture analysis
Force analysis using Particle Swarm Optimization [90]
Heuristic rule-base approach with geometric reasoning
Matrix analysis
Augmented two dimensional geometric overlay
Graph theory using tolerance factors
Graph theory using tolerance normalization
Graph theory using tolerance analysis
Rule-based approach
Rule-based approach
Stiffness-displacement analysis
Optimized stiffness-displacement analysis
Heuristic preferences with screw-set theory
Geometric and kinetic model analysis
Displacement optimization using penalty-function methods
Genetic Algorithm (GA) based optimized stiffness-displacement analysis
Qualitative rule-based analysis
Procrustes-based pairwise optimization
Displacement optimization using GA
Displacement optimization using GA
Case-based reasoning
GA/neural network
Rule induction and re-use
Swept volume analysis
Genetic algorithm optimization
Case-based reasoning
Nonlinear optimization algorithm
Finite element and nonlinear rigid body dynamics analysis
Group technology/neural network
Force and moment analysis
Heuristic rule-base
Non-optimized evolutionary algorithm
Pseudo-gradient based optimization
Neural network
Rule-based approach augmented with FEA
Rule-based approach, stability analysis
Precedence matrix with genetic algorithm
Multi-objective optimization using an interchange algorithm
Geometric constraint based reasoning
Rule-based approach
Finite element analysis
Rule-based approach featuring graph analysis
Finite element analysis
Blackboard framework
Rule-based with displacement analysis
Force and moment equilibrium analysis
Geometric reasoning
Graph approach
Finite element analysis
Finite element analysis
Geometric constraint reasoning
Force and moment equilibrium analysis
Genetic algorithm optimization using finite element analysis
Rule-based approach
Tolerance analysis
Case-based reasoning
Multi-criteria optimization
Geometry and rule-based approach
Geometry-based reasoning
GA based optimization using screw theory
Object oriented reasoning with fuzzy set optimization
Mechanical linkage analysis
Directed graph theory using tolerance analysis
Directed graph theory using tolerance analysis
Directed graph theory using tolerance analysis
Graph theory using tolerance analysis
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Key: SP—setup planning; PR—physical requirements; FP—fixture planning; TR—tolerance requirements; UD—unit design; AR—affordability requirements;
V—verification; CR—constraining requirements; CDR—collision detection requirements; UR—usability requirements.
6
I. Boyle et al. / Robotics and Computer-Integrated Manufacturing 27 (2011) 1–12
Setup 1
F
Setup 2
A
X
C
A
Y
B
D
Z
B
Z
Datum feature
E
Machining feature
Fig. 4. A workpiece (a) and its directed graphs showing the locating datums (b) (adapted from Zhang et al. [17]).
3.2.1. Approaches to defining the fixturing requirement
Comprehensive fixture requirement definition has received
limited attention, primarily focusing upon the definition of
individual requirements within the physical, tolerance, and
constraining requirements. For example, Zhang et al. [17] undertake tolerance requirement definition through an analysis of
workpiece feature tolerances to determine the allowed tolerance
at each locating point and the decomposition of that tolerance
into its sources. The allowed locating point accuracy is composed
of a number of factors, such as the locating unit tolerance, the
machine tool tolerance, the workpiece deformation at the locating
point, and so on. These decomposed tolerance requirements can
subsequently drive fixture design: e.g., the tolerance of the
locating unit developed in the unit design phase cannot exceed
the specified locating unit tolerance. In a similar individualistic
vein, definition of the clamping force requirements that clamping
units must achieve has also received attention [29,30].
In a more holistic approach, Boyle et al. [31] facilitate a
comprehensive requirement specification through the use of
skeleton requirement sets that provide an initial decomposition
of the requirements listed in Table 1, and which are subsequently
refined through a series of analyses and interaction with the
fixture designer. Hunter et al. [32,33] also focus on functional
requirement driven fixture design, but restrict their focus
primarily to the physical and constraining requirements.
3.2.2. Approaches to non-optimized layout planning
Layout planning is concerned with the identification of the
locating principle, which defines the number and general
arrangement of locating and clamping points, the workpiece
surfaces they contact, and the surface coordinate positions where
contact occurs. For non-optimized layout planning, approaches
based upon the re-use of experiential knowledge have been used.
In addition to rule-based approaches [20,34,35] that are similar in
nature to those discussed in Section 3.1, case-based reasoning has
also been used. CBR is a general problem solving technique that
uses specific knowledge of previous problems to solve new ones.
In applying this approach to layout planning, a layout plan for a
workpiece is obtained by retrieving the plan used for a similar
workpiece from a case library containing knowledge of previous
workpieces and their layout plans [18,36,37]. Workpiece similarity is typically characterized through indexing workpieces
according to their part family classification, tolerances, features,
and so on. Lin and Huang [38] adopt a similar workpiece
classification approach, but retrieve layout plans using a neural
network. Further work has sought to verify layout plans and
repair them if necessary. For example Roy and Liao [39] perform a
workpiece deformation analysis and if deformation is too great
employ heuristic rules to relocate and retest locating and
clamping positions.
3.2.3. Approaches to layout planning optimization
Layout plan optimization is common within CAFD and occurs
with respect to workpiece stability and deformation, which are
both constraining requirements. Stability based optimization
typically focuses upon ensuring a layout plan satisfies the
kinematic form closure constraint (in which a set of contacts
completely constrain infinitesimal part motion) and augmenting
this with optimization against some form of stability-based
requirement, such as minimizing forces at the locating and/or
clamping points [40–42]. Wu and Chan [43] focused on optimizing stability (measuring stability is discussed in Section 3.4) using
a Genetic Algorithm (GA), which is a technique frequently
employed in deformation based optimization.
GAs, which are an example of evolutionary algorithms, are
often used to solve optimization problems and draw their
I. Boyle et al. / Robotics and Computer-Integrated Manufacturing 27 (2011) 1–12
inspiration from biological evolution. Applying GAs in support of
fixture planning, potential layout plan solutions are encoded as
binary strings, tested, evaluated, and subjected to ‘‘biological’’
modification through reproduction, mutation, and crossover to
generate improved solutions until an optimal state is reached.
Typically deformation testing is employed using a finite element
analysis in which a workpiece is discretized to create a series of
nodes that represent potential locating and clamping contact
points, as performed for example by Kashyap and DeVries [44].
Sets of contact points are encoded and tested, and the GA used to
develop new contact point sets until an optimum is reached that
minimizes workpiece deformation caused by machining and
clamping forces [45,46]. Rather than use nodes, some CAFD
approaches use geometric data (such as spatial coordinates) in the
GA, which can offer improved accuracy as they account for the
physical distance that exists between nodes [47,48].
Pseudo-gradient techniques [49] have also been employed to
achieve optimization [50,51]. Vallapuzha et al. [52] compared the
effectiveness of GA and pseudo-gradient optimization, concluding
that GAs provided higher quality optimizations given their ability
to search for global solutions, whereas pseudo-gradient techniques tended to converge on local optimums.
Rather than concentrating on fixture designs for individual
parts, Kong and Ceglarek [53] define a method that identifies the
fixture workspace for a family of parts based on the individual
configuration of the fixture-locating layout for each part. The
method uses Procrustes analysis to identify a preliminary workspace layout that is subjected to pairwise optimization of fixture
configurations for a given part family to determine the best
superposition of locating points for a family of parts that can be
assembled on a single reconfigurable assembly fixture. This
builds upon earlier work by Lee et al. [54] through attempting
to simplify the computational demands of the optimization
algorithm.
3.3. Unit design
Unit design involves both the conceptual and detailed
definition of the locating and clamping units of a fixture, together
with the baseplate to which they are attached (Fig. 5). These units
consist of a locator or clamp that contacts the workpiece and is
itself attached to a structural support, which in turn connects
with the baseplate. These structural supports serve multiple
functions, for example providing the locating and clamping units
with sufficient rigidity such that the fixture can withstand applied
machining and clamping forces and thus result in the part feature
design tolerances being obtained, and allowing the clamp or
clamp work action
clamp
fulcrum
supporting units
baseplate connectors
Fig. 5. An example of a clamping unit.
7
locator to contact the workpiece at the appropriate position. Unit
design has in general received less attention than both fixture
planning and verification, but a number of techniques have been
applied to support both conceptual and detailed unit design.
3.3.1. Approaches to conceptual unit design
Conceptual unit design has focused upon the definition of the
types and numbers of elements that an individual unit should
comprise, as well as their general layout. There are a wide variety
of locators, clamps, and structural support elements, each of
which can be more suited to some fixturing problems than others.
As with both setup planning and fixture layout planning, rulebased approaches have been adopted to support conceptual unit
design, in which heuristic rules are used to select preferred
elements from which the units should be constructed in response
to considerations such as workpiece contact features (surface
type, surface texture, etc.) and machining operations within the
setup [35,55–58]. In addition to using heuristic rules as a means of
generating conceptual designs, Kumar et al. [59] use an inductive
reasoning technique to create decision trees from which such
fixturing rules can be obtained through examination of each
decision tree path.
Neural network approaches have also been used to support
conceptual unit design. Kumar et al. [60] use a combined
GA/neural network approach in which a neural network is trained
with a selection of previous design problems and their solutions.
A GA generates possible solutions which are evaluated using the
neural network, which subsequently guides the GA. Lin and
Huang [38] also use a neural network in a simplified case-based
reasoning (CBR) approach in which fixturing problems are coded
in terms of their geometrical structure and a neural network used
to find similar workpieces and their unit designs. In contrast,
Wang and Rong [37] and Boyle et al. [31] use a conventional CBR
approach to retrieve units in which the fixturing functional
requirements form the basis of retrieval, which are then subject to
refinement and/or modification during detailed unit design.
3.3.2. Approaches to detailed unit design
Many, but not all systems that perform conceptual design also
perform detailed design, where the dominant techniques are rule,
geometry, and behavior based. Detailed design involves the
definition of the units in terms of their dimensions, material
types, and so on. Geometry, in particular the acting height of
locating and clamping units, plays a key role in the design of
individual units in which the objective is to select and assemble
defined unit elements to provide a unit of suitable acting height
[61,62]. An et al. [63] developed a geometry based system in
which the dimensions of individual elements were generated in
relation to the primary dimension of that element (typically its
required height) through parametric dimension relationships.
This was augmented with a relationship knowledge base of how
different elements could be configured to form a single unit.
Similarly, Peng et al. [64] use geometric constraint reasoning to
assist in the assembly of user selected elements to form individual
units in a more interactive approach.
Alternatively, rule-based approaches have also been used to
define detailed units, in which workpiece and fixture layout
information (i.e., the locating and clamping positions) is reasoned
over using design rules to select and assemble appropriately sized
elements [32,55,56]. In contrast, Mervyn et al. [65] adopt an
evolutionary algorithm approach to the development of units, in
which layout planning and unit design take place concurrently
until a satisfactory solution is reached.
Typically, rule and geometry based approaches do not
explicitly consider the required strength of units during their
8
I. Boyle et al. / Robotics and Computer-Integrated Manufacturing 27 (2011) 1–12
design. However for a fixture to achieve its function, it must be
able to withstand the machining and clamping forces imposed
upon it such that part design tolerances can be met. To address
this, a number of behaviorally driven approaches to unit design
have been developed that focus upon ensuring units have
sufficient strength. Cecil [66] performed some preliminary work
on dimensioning strap clamps to prevent failure by stress
fracture, but does not consider tolerances or the supporting
structural unit. Hurtado and Melkote [67] developed a model for
the synthesis of fixturing configurations in simple pin-array type
flexible machining fixtures, in which the minimum number of
pins, their position, and dimensions are determined that can
achieve stability and stiffness goals for a workpiece through
consideration of the fixture/workpiece stiffness matrix, and
extended this for modular fixtures [68]. Boyle et al. [31] also
consider the required stiffness of more complex unit designs
within their case-based reasoning method. Having retrieved a
conceptual design that offers the correct type of function, this
design’s physical structure is then adapted using dynamically
selected adaptation strategies until it offers the correct level of
stiffness.
3.4. Verification
Verification focuses upon ensuring that developed fixture
designs (in terms of their setup plans, layout plans, and physical
units) satisfy the fixturing requirements. It should be noted from
Table 2 that the majority of CAFD approaches perform some type
of verification, but within this section of the review the focus will
be upon those research efforts in which verification is a major
feature of the work. Verification takes place against the tolerance,
constraining, collision detection, usability, and affordability
requirements (Table 1). Explicit verification against the physical
requirements is not normally considered to be a significant
verification task given its tight coupling with the process of
designing fixtures. Constraining requirements verification has
received the most research attention, closely followed by
tolerance and collision detection requirements verification. In
contrast, work on affordability and usability requirements
verification has attracted little focus.
3.4.1. Approaches to constraining requirements verification
Constraining requirements verification focuses upon verification of a fixture design against stability and deformation
requirements. Stability verification seeks to ensure that part
motion is restrained during machining (other than that caused
by fixture and workpiece deformation). While some stability
verification approaches are analytical and focus upon ensuring
that force and moment equilibrium exists on a workpiece when
subjected to machining and clamping forces [6,61,69,70], others
adopt a less rigorous approach in which locating directions are
assessed to determine if a workpiece’s six degrees of freedom are
restrained [31,71]. As discussed in Section 3.2, verification is often
performed during optimization [41] and in such a vein Liao and
Hu [29] consider the effects dynamically varying machining forces
have on the minimum clamping forces required to maintain
stability, and Deng and Melkote [30] also consider how dynamic
material removal affects the required clamping loads.
Whilst the majority of approaches provide a boolean definition
of stability, Roy and Liao [72] attempt to qualitatively define the
stability of a fixture design through initially determining
the critical stability situation for a layout plan. Subsequently the
locating and clamping positions are altered and the virtual work
required to maintain equilibrium calculated to provide a qualitative evaluation of relative stability between different layout
plans. Kang et al. [6] adopt the concept of a normalized contact
stability index (CSI) to explicitly measure stability at a specific
contact fixture/workpiece contact point. In a less analytical
approach, Wu et al. [73] use a rule-based approach to qualitatively evaluate stability of a layout plan.
Deformation analysis has focused primarily upon analyzing
workpiece deformation using finite element analysis [39,44,74,75].
This involves discretizing the workpiece into elements that form a
mesh, selecting the type of analytical elements to represent the
mesh during analysis, and defining the boundary conditions that
exist on the workpiece (e.g., at the fixture/workpiece interface). Rai
and Xirouchakis [76] extend the finite element analysis to consider
the effects of workpiece geometry changes due to material removal
during machining. Variations in such approaches include the
different boundary condition definitions and mesh elements
selected, and Siebenaler and Melkote [77] investigate the effects
of various mesh parameters on predicted workpiece deformation.
Similarly, Satyanarayana and Melkote [78] discuss the modeling
of boundary conditions at the workpiece/fixture contact interface
and develop and experimentally validate a set of guidelines for
modeling the contact interface.
In comparison to the significant quantity of research that
has been conducted on workpiece deformation, work on analyzing fixture deformation is very limited and where values are
required to support workpiece deformation analysis when
specifying the workpiece/fixture boundary conditions, they are
assumed. Boyle et al. [31] do however explicitly measure the
deformation of individual units and amend the fixture structure
if the deformation exceeds that required, and Hurtado and
Melkote adopt a similar approach for pin-array [67] and modular
fixtures [68].
3.4.2. Approaches to tolerance requirements verification
The focus within research on tolerance requirements verification has varied. For example, Camelio et al. [79] developed a
methodology based upon linking part errors to the fixture faults
causing them via a kinematics-based machining process variation
model. In contrast to this, Wang [80] developed a tolerance
analysis technique that computed the new positions of part
features in response to different locator and workpiece datum
geometric errors, and Bansal et al. [23] facilitate sensitivity
analysis using different locating positions to determine which
minimize the locating error. However it should be noted that such
techniques do not confirm tolerance satisfaction by relating the
results of such an analysis to the required design tolerances
(many fixture planning optimization techniques suffer from a
similar issue). Kang et al. [6] do directly relate part feature
deviations to locating tolerance errors, and amend locating
tolerances until the part design tolerances are satisfied, and Boyle
et al. [31] extend such an approach to consider locator unit
deformation during machining and the effect that has upon
satisfying part tolerances.
3.4.3. Approaches to collision detection requirements verification
Verification checks that fixtures do not interfere with machine
tool cutting paths, that fixture elements do not collide with each
other, and that the fixture does not collide with the workpiece.
The latter case is relatively simple to verify given the static
interference checks that many modern CAD systems can perform
and fixture–fixture collision detection can be checked using
geometric constraint reasoning to ensure that fixture elements
do not collide other than at their designed contact points [64]. Ryll
et al. [81] perform collision detection for reconfiguration of
flexible fixturing systems in which a list of reconfiguration steps
I. Boyle et al. / Robotics and Computer-Integrated Manufacturing 27 (2011) 1–12
for altering contact positions are determined and virtually
executed to identify if any units will collide.
However, fixture–toolpath collision detection presents a more
complex challenge. Kumar et al. [82] use a cutter swept volume
approach in which the cross sectional area of a cutting tool is
extruded along the toolpath to create a swept volume. A static
interference check is then performed to ascertain if this swept
volume coincides with any part of the fixture: i.e., if there is a
collision. Roy and Liao [83] not only identify collisions but also use
heuristic rules to adjust support and clamping positions such that
collisions will not occur.
Hu and Rong [84] developed a less computationally demanding two dimensional approach in which fixture elements and
cutting tools are modeled in two dimensions with a height value.
An interference algorithm then checks for overlaps between the
2D fixture and toolpath elements, and subsequently uses the
height information to identify if a collision has occurred.
3.4.4. Approaches to usability and affordability requirements
verification
Verification of usability and affordability requirements has
received comparatively little attention. Rule-based approaches to
fixture design [35] can provide some capability for generating
designs that satisfy these requirements, but more analytical
approaches have been developed to perform verification. Boyle
et al. [31] use heuristically developed algorithms to calculate
fixture performance with regard to affordability requirements
(e.g., cost, assembly time, operation time, etc.), and Ong et al. [26]
enable optimization using a combined genetic algorithm-simulated annealing approach.
3.5. Representation of fixturing information
In recent years, there has been an embryonic shift towards
consideration of how fixture design information can be represented. CAFD research is segmented in nature, with research
efforts focusing upon specific requirements or design phases of
fixturing problems rather than providing holistic fixture design
support. However, some initial work has been conducted with a
view to obtaining a greater understanding of information
representation in support of CAFD integration [85]. At an abstract
level, Pehlivan and Summers [3] analyze fifteen CAFD tools in
terms of which design phases they assist, and their information
inputs and outputs. In addition to information flows, Cecil
interviewed experienced fixture designers to develop an Information Intensive Function Model (IIFM) that relates information
flows with fixture design activities [86].
Both Pehlivan and Summers and Cecil focus upon the
information flows that exist within fixture design, but provide
no details of how that information might be represented. At this
deeper level of detail, a number of representations for fixturing
information have been developed. Boyle et al. [31] propose the
use of Axiomatic Design decomposition [87] as a means of
representing and linking fixturing requirements and the fixture
design parameters that satisfy them (see Section 3.2.1). Hunter
et al. [32,33] use a Unified Modeling Language (UML) approach for
representing fixture functional information to support knowledge
re-use and link it to their Integration DEFinition Functional Model
(IDEF0) of the fixture design process.
At an implementation level, Extensible Markup Language
(XML) schemas have been developed to represent fixture design
information [37,64,88,89], which can support information integration through the ease with which they allow information to be
represented and transferred. For example Wang and Rong [37]
detail a fixturing dictionary that defines objects within fixture
9
design, their properties, and how the different entities are related,
which is implemented using an XML schema. It is worth noting
however that although research efforts are moving towards
information representation within CAFD, there remains as yet
no agreed standard to support effective integration.
4. An analysis of CAFD research
As Section 3 has highlighted, there has been a considerable
amount of research conducted with regard to the development of
CAFD approaches and subsequent software implementations of
those tools. In particular, CAFD research has focused upon setup
planning, fixture planning, and verification, in which fixture
planning and verification have been closely integrated with
respect to minimizing workpiece deformation by controlling the
locating and clamping positions. However there remain a number
of research issues that if addressed would significantly increase
the effectiveness with which fixture design can be supported. The
remainder of Section 4 discusses the following four research
issues, respectively:
Much CAFD research is segmented in nature and a need
remains to provide more holistic support for fixture design
that integrates the four phases of the design process (Section
4.1).
There remains a lack of attention on effectively supporting unit
design (Section 4.2).
There remains a lack of attention on developing CAFD
approaches that can derive and subsequently incorporate a
comprehensive understanding of the functional requirements
for fixturing problems (Section 4.3).
Many of the CAFD approaches have been tested for simple
workpieces that are unrepresentative of those encountered in
industry, thus the effectiveness of developed techniques
cannot be stated with confidence (Section 4.4).
4.1. The segmented nature of CAFD research
With regard to the segmented nature of CAFD research, Table 1
clearly illustrates that the need to integrate existing CAFD
approaches to provide effective support across the four phases
of the fixture design process remains. Although a number of
research efforts have attempted to support all four phases
[31,34,35], the depth of support that is provided varies across
each phase and is typically not as great as that of those research
efforts which concentrate on supporting specific phases. For
example Joneja and Chang’s [34] ability to perform setup planning
is less than that of Yao et al. [15], who focus exclusively on that
task and for example pay more attention to consideration of
tolerance stack-ups within their graph-based approach. There
remains therefore a need to consider how all of the disparate
CAFD approaches can be integrated to provide cohesive design
support.
This goes beyond the information integration considered by
Pehlivan and Summers [3] and which has been partially
addressed through the attempts to structure fixturing information
(Section 3.5). The key issue of control also has to be resolved. This
relates to controlling the design process and deciding where
responsibility for decision making should fall if a conflict occurs
between any of the individual approaches. This becomes an issue
particularly when verification of the design is attempted. For
example, given the situation where workpiece deformation is
found to be excessive at the workpiece/locator contact points,
there are three abstract courses of remedial action. One is to
adjust the locating points, as performed in Vallapuzha et al. [48], a
10
I. Boyle et al. / Robotics and Computer-Integrated Manufacturing 27 (2011) 1–12
second is to strengthen the locating units to provide greater
rigidity, as performed in Boyle et al. [31], and the third is a
combination of both. Integrating the above approaches from an
information point of view will not resolve the problem of which
remedial course of action should be taken. Rather, methods need
to be developed to manage their integration so that the true cause
of the fixture design failure can be identified, and an appropriate
remedy generated and identified. Overall therefore, to achieve
cohesive integration of current CAFD approaches at both an
implementation and conceptual level, the need exists to define
how their integration is controlled to ensure effective support is
provided during fixture design.
4.2. Effectively supporting unit design
The second issue relates to the continuing lack of focus upon
supporting detailed unit design. As Table 2 indicates, there are
several CAFD approaches that are capable of generating fixture
unit structures, but with the exception of a handful of research
efforts [31,68] unit design is typically confined to satisfying
workpiece geometry. In essence this means identifying the
necessary acting height of the unit and then determining all
other dimensions based upon some form of parametric design or
heuristic rule execution. While Boyle et al. [31] and Hurtado and
Melkote [68] have made some initial progress on addressing the
stiffness requirements of individual units within the design
process, there remains considerable scope for further research,
particularly with regard to the modeling and modification of more
physically complex unit designs. It is also important to note that
this lack of focus on unit design reduces the effectiveness of the
fixture planning stages in which unit stiffness values are assumed
because if subsequent unit designs are not designed to these
assumed stiffness values then the fixture planning layout analysis
is invalid.
4.3. Comprehensively formulating the fixturing requirement
The third CAFD research issue relates to the above point, but in
a more general form. Specifically, it concerns the lack of a
comprehensive formulation of fixturing requirements. As the
‘‘requirements considered’’ column in Table 2 illustrates, the
majority of CAFD approaches focus upon satisfying a restricted set
from the physical, constraining, tolerance, or collision detection
requirements, and indeed the affordability and usability requirements receive little attention at all in comparison. However,
effective designs are reliant upon comprehensively understanding
the problem for which they are intended to be the solution yet
currently within the CAFD community research is typically
focused on individual requirements. Research conducted on
defining and representing fixturing requirements (Section 3.2.1)
provides a solid basis for addressing this issue, but there remains
a need to understand how these requirements influence the final
design: e.g., how should the affordability and usability requirements be incorporated into setup planning, how do the constraining requirements affect setup planning, etc.
4.4. Validating CAFD research outputs
The final issue concerning the current status of CAFD approaches
is their validation. To illustrate that they represent value to
industry, validation needs to take place within a context similar
to that encountered within the manufacturing industry. Thus, CAFD
approaches need to be tested and evaluated using complex
workpieces that are representative of those encountered in
industry. However, a significant number have been demonstrated
and evaluated using simple workpieces that typically consist of
planar and occasionally cylindrical surfaces [30,31,47], which do
not represent challenging fixturing problems. This is not the case
for all CAFD approaches though. For example Yao et al.’s [15]
approach to setup planning has been applied to determining setups
for brake calipers, Song and Rong [71] applied their fixture planning
approach to a steering knuckle, and Wu et al. [73] applied their
fixture planning approach to a pump-casing. It is to these types of
workpieces that CAFD efforts need to be applied if they are to
present a convincing argument that they represent value to
industry and can reduce costs associated with fixturing. However,
there is currently a lack of available literature on the effectiveness
of CAFD systems within industry and some rigorous studies of their
effectiveness, not only in terms of the quality of solution they
generate but also on the impact they have at an organizational level
(e.g., in terms of improved efficiency) would be of significant
benefit in terms of justifying current CAFD research outputs and
providing direction for future efforts.
5. Conclusion
This paper has presented a review of current approaches for
supporting fixture design. CAFD approaches have been reviewed
in terms of the design phases they support and the underlying
technology upon which they are based. Currently, the strengths of
CAFD research lie within the verification approaches that focus
upon checking workpiece stability and deformation during
machining, and the layout planning approaches that seek to
minimize workpiece deformation caused by machining forces.
Similarly setup planning has received considerable attention
although it is worth noting that little effort has been devoted to
supporting setup optimization in the same vein as that for layout
planning. However, the segmented nature of CAFD research and
the continuing lack of focus upon unit design remain areas of
concern within the fixture design domain.
In terms of increasing the effectiveness of CAFD research
outputs therefore, two primary avenues of development present
themselves. Firstly, a greater focus is required on supporting unit
design, particularly with regard to determining unit stiffness and
relating unit stiffness requirements to unit structure. Secondly,
there remains a need to cohesively integrate the segmented CAFD
approaches together within a framework that incorporates a
comprehensive understanding of fixturing requirements and that
uses this understanding to drive the fixture design process.
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