Knowledge Definition in Product Model for
Control of Feature Definitions
László Horváth and Imre J. Rudas
Óbuda University, John von Neumann Faculty of Informatics, Institute of Intelligent Engineering Systems, Budapest,
Hungary
horvath.laszlo@nik.uni-obuda.hu, rudas@uni-obuda.hu
Abstract—This paper deals with recent results of a long
term research program in human intent driven and
knowledge based product modeling. The aim of the research
was grounding connection between situation and event
based generation of product features in current industrial
product models and the content based model extension in an
earlier result by the authors. In this paper, research is
focused on knowledge feature definition for adaptive feature
control. The authors considered their Virtual Engineering
Space concept as the utmost objective in automation of
engineering activities. Following this, knowledge definition
methods are analyzed, contextual connections of active
knowledge definitions in the context based product model
extension are placed in the communication, and control of
product features by adaptive actions is discussed.
I.
INTRODUCTION
This paper introduces a recent research in order to
establish communication between currently representative
industrial product model and content based adaptive
product model extension. This latter was developed and
published by the authors and covered human intent driven
and engineering objective based product feature decision
method. The possibility for the communication is
provided by a recent achievement of industrial product
models. This achievement is one of the main features of
integrated knowledge in product model. The new feature
ensures situation and event driven definition and
redefinition of product features in product model and it is
well-proven at leading industrial applications in car,
aircraft and other industries. The authors of this paper did
research in knowledge based product definition methods
for human intent model based driving of contextual
product feature definition using this advanced outside
knowledge communication surface of industrial product
modeling systems.
The basic motivation of the reported research is given
by the emerging knowledge based product development
by using of source identified and verified knowledge
property. Actual knowledge is integrated in product model
and accumulated in company and product specific
knowledge ware. Knowledge definitions become organic
features of product model.
In this paper, research is focused on knowledge feature
definition for adaptive feature control. The authors
consider their Virtual Engineering Space concept as the
utmost objective in automation of engineering activities.
Following this, knowledge definition methods are
analyzed, contextual connections of active knowledge
definitions in the context based product model extension
are placed in the communication, and control of product
features by adaptive actions is discussed.
II. VIRTUAL ENGINEERING SPACE
Development of engineering systems for the integration
of product related activities is aimed to achieve a
computer model that is capable for simulation of planned
or existing real world product characteristics. The result
will appear as an advanced model space for engineering
purpose. It was called and defined as Virtual Engineering
Space (VES) in [1]. VES will be a virtual equivalent of a
planned of existing physical space. Its name comes from
application of virtual space for engineering.
Term virtual space is still not well-defined. In [7], it is
emphasized that virtual activities and interactions have
characteristics that cannot be properly represented and
analyzed under the classical time-geographic framework
and extends classical time-geographic concepts to
accommodate the needs of representing and analyzing all
activities and interactions in a hybrid physical–virtual
space. This is a good example for extension of current
models towards virtual space and opening virtual space
towards physical world.
Virtual space theory and methodology applies results
from virtual reality systems. Paper [8] discusses impact of
virtual reality on quality of collaboration design processes.
It is focused on collaboration scopes and requirements,
participants’ behavior, and exploited interaction
modalities. A method is applied to synchronous and
remote collaboration. Authors emphasize that this
represents the most critical communication in industry.
The authors of this paper characterize VES as venue of
engineering and related research where intelligent
capabilities in knowledge related product modeling are
realized in order to represent construction, structure, and
inside and outside relationships of a product. In virtual
engineering, latest advancements assist accepting and
representation of intelligent content. Engineers
cooperating with model space must be in possession of
handling professional knowledge based product
information by using of the new human capability for
seeing and thinking in virtual space. Virtual engineering is
one of the most important development and application
areas of knowledge based methods and intelligent
informatics. In the industrial practice, virtual engineering
must apply company or personal owned knowledge that is
product specific, verified, and accepted for application.
Source identified and verified knowledge ware is
accumulated for later application in close connection with
product model.
Current product lifecycle management (PLM) systems
are approaching criteria of VES. The way to this
achievement is briefed in Fig. 1. The early CADD systems
realized the conventional engineering drawing based
drafting and design in computer environments. Multi
representational shape models applied different
representations for analytical wireframes and solids and
parametric surfaces in CAD/CAM/CAE systems. New
functionalities were the parametric surface based multi
axis machine tool control and the finite element analysis.
recycling, maintainability, environment, etc. The content
based method applies knowledge features for this purpose.
Figure 2. Virtual Engineering Space
Figure 1. Short chronology of achievements in product modeling
The first breakthrough was the standard STEP (Standard
for the Exchange of Product Model Data, ISO 10303) of
product model by the International Organization for
Standardization (ISO). It was aimed at information loss
free transfer of model files between product modeling
systems and characterized by implementation of product
model concept, application of object modeling, building
product model using generic and application resources,
and implementation using application protocols. It has
substantial effects on recent research, among others by
the introduction of engineer intent originated form feature
in product model. Authors of [9] integrated the
downstream activities of the design process and gave an
extended definition of life cycle features for product
model. They defined relationships between product
features for applications such as assembly, disassembly,
From the early 90’s, the currently applied product
object representations were founded. As a result of these
activities, shapes are modeled as series of modification by
form features and represented by Eulerian topology and
non uniform rational B-spline (NURBS) geometry. By
now, full feature based contextual object model and
situation and event based knowledge ware are key
characteristics of product modeling systems. Current
research efforts are mainly devoted to establish organized,
multiple intent and source considered knowledge based
decision and adaptive control of features. The authors of
this paper joined to this work. Successful national and
international projects have resulted widely published
results as building blocks for future VES. These results
included a possible definition and integration of
knowledge in product model in order to enhance modeling
capabilities for representation of human intent, skill, and
experience [2]. New intelligent human-computer
communication and human intent based product modeling
were conceptualized [3]. Information content was
modeled for product model in connection with model
object information for which it is defined [4] and new
content based product definition was established in virtual
space that represented and applied background knowledge
[5].
Concept of VES and its connection with currently
applied PLM modeling is outlined in Fig. 2). Current
representative product modeling technology is based on
feature driven object model, and description of product
objects by contextual features. Product object active
knowledge features constitute product model. Model
construction is served by procedures for the definition of
features. The authors of this paper proposed adaptive
control of the above features by organized, coordinated,
and scientifically grounded new knowledge features.
Feature definition in product model is explained in Fig.
3. Human who defines product features for the product
model controls construction of product model by sequence
of modification by features. Feature can be defined
directly, in context, or by active entities in the product
model. These entities represent contextual connections
and situation and event based knowledge.
by product feature. Component feature is defined in the
context of product and part features. Component feature is
a special instance of part feature.
Shape of a part is described as a solid by using of a
series of shape modifications by form features. Form
features are represented by unified Eulerian topology and
NURBS geometry. Engineer defines shape modification
features so that solid shape and B-rep representations are
defied in the context of form features. Parameters are
defined together with shape modification and product
features. At the same time, feature parameters are also
defined in the context of relationship feature definitions.
Finally, shape modification and product features are often
defined in the context of parameter definitions. Some
parameters are defined by measurement of the actual
geometrical representation of solid part body. Model is
always defined in the context of actual model space
parameters. Fig 4 shows that both unidirectional and
bidirectional contextual connections are apply.
Feature definition is subject of research in order to
enhance its content in various aspects. Feature is defined
in [10] in a mixed parametric and semantics way in order
to support function representations in product modeling by
handling geometry data on the basis of manipulated
expertise and assure communication between users. The
method proposed by the authors of this paper offers
possibility for representation similar and any other aspects
in knowledge based content definitions.
Figure 3. Definition of feature
III. ANALYSIS OF KNOWLEDGE DEFINITION METHODS
Industrial product modeling prefers simple tools for
knowledge definition in product model. This is necessary
both for engineer understandable methods and transparent
knowledge model. The reported research started with an
analysis of knowledge definition tools available in a
representative industrial model space. Following this, their
connections with the related product features were
analyzed.
A restricted and simplified model in currently
representative industrial model space is sketched in Fig. 4.
The model is full feature based so that any entity in the
product model is defined as modification feature.
Modification affections are realized through contextual
connections along arrows in Fig. 4. Product is described
Figure 4. Simplified representative product model
The analyzed knowledge definitions serve control of
product model features. It is essential that any automation
of engineering process is not allowed to decrease the right
of human to decide on results and the transparency of
product model to allow for this activity. Consequently, the
earlier closed expert system must be replaced by
transparent knowledge based product model. In order to
achieve this, one of the aims of the analyses was to reveal
human connections of knowledge definitions, product
features, and model construction and application
processes.
Because in the considered analysis environment the
product model is feature based, parameter and relation
definitions also feature based. Knowledge representations
should be analyzed for suitability for dedicated
engineering
activities,
parameters,
representation
capabilities, and integrability. Model representations serve
consistent generic knowledge definition.
Fig 5 outlines relationships in current product model
those may be suitable for the purpose of communication
surface between current product model and its adaptive
controlling extension. In this context, relationships serve
event and situation based definition of product features in
product model. For relationship definitions, the necessary
parameters of features must be defined. Parameter
definitions are applied in rules, checks, and formulas. Rule
is defined for parameters depending on situation while
check is defined to recognize situation. Rules and checks
also apply formulas and can be organized in sets for
dedicated purposes. Rules and checks serve actions for
given situations. Reaction is defined for event and results
predefined action. Normally, it drives rules and checks.
redefined in a recent research [6]. Their application has
been conceptualized according to the demand by
connection with situation and event based knowledge
ware entities in current industrial PLM systems.
Figure 6.
Contextual content entities
Figure 5. Relationships in current product model
IV. ACTIVE KNOWLEDGE DEFINITION
The authors of this paper conceptualized future research
for the proposed adaptive control of features by organized,
coordinated, and scientifically grounded new knowledge
features. These features were originally defined in [5] and
Many authors emphasize importance of application
expertise based knowledge in engineering. In the opinion
of author of [11], expertise is not simply a matter of
possessing talent, but it is the result of a dedicated
application to a chosen field and the strategies of experts
are usually regarded as being predominantly top-down and
breadth-first approaches.
Application of the new knowledge features for adaptive
control of product features can be followed in a contextual
chain (Fig. 6). Product object features in current product
model (Fig. 2) is controlled by adaptive action. Features
are grouped in spaces those map them to product features.
This method ensures multi-representative knowledge
connections to product features.
Adaptive action is generated and evaluated in decision
space and through contextual graph, product feature
contextual chain, and change affect zone (CAZ) content
feature contextual chain. Product feature contextual
definitions are behavior controlled through situations and
circumstances. This is the second stage of the content
feature contextual chain. Human intent for definition of
product features and their contextual connections is
communicated with the decision space through the related
behaviors. Intent is represented by entities in the third
stage of the content feature contextual chain including
method of feature definition, objective, and intent status.
Intent is defined in the context of authorized human
source. Entities characterize influence and authentication
of human. Influence space must be authorized for
controlling any knowledge entity in any space.
VI. IMPLEMENTATION IN PLM SYSTEMS
The proposed content driven method of knowledge
based product definition supposes existence of industrially
applicable PLM system with application programming
interface (API) for the communication of product
modeling with outside applications. Relevant system
elements are sketched in Fig. 8.
Extension feature manipulation serves the new content
based entities. It is a procedure library developed in user
environment. API makes access of relevant inside system
elements accessible. User interface for product definition
can be completed, engineers involved in group work
management can apply extension, and product model is
accessible for outside procedures. New entities can be
involved in product model.
V. CONTROL OF PRODUCT FEATURES
Primary aim of the authors of this paper with the
research in knowledge based adaptive control of product
features is to complete the currently applied relationship
and context driven product modeling systems. As product
models become increasingly complex and very large, high
number of relationship makes them hard to survey and
labyrinthine. The proposed method is being developed
towards knowledge based content representation that will
be suitable for survey relationships, track consequences of
requested modifications, and explain feature definition at
any time.
During construction of product model, engineer defines
request for modification of product by dedicated feature or
by method for the creation of feature. Feature may
describe objective, knowledge, or product object. In case
of objective, dedicated method generates knowledge
feature for the definition of product feature. Definition of
objective is mandatory. Other definitions may be passed to
authorized engineers. Request is completed and analyzed
for quality and consequence on existing and planned
features. The process is simplified by grouping features
around leading ones. Status of adaptive action controls
executability of an on-going product modification request.
When a decision gives pass for execution by change status
to executable (Fig. 7), adaptive action defines and
modifies given features. For this purpose, links are
available for the related content entities. Content entities
include knowledge for execution. Because the extended
product model is behavior driven, execution starts with the
behavior and recognizes situation and event. Circumstance
records features and parameters to be changed by the
requested, modified, and finally accepted methods.
Contextual graph, contextual chains, and contextual
definitions are available for the navigation within the
product model.
The above method must be prepared for very complex
engineering tasks such as modeling and control of turbojet
engines in [12].
Figure 7.
Content of adaptive action
Experimental implementation of the proposed modeling
extension is planned as an extensive research program in
the future. Leading industrial PLM system is under
installation to achieve user development environment that
will be suitable for this work at the Laboratory of
Intelligent Engineering Systems (LIES) of the Institute of
Intelligent Engineering Systems, John von Neumann
Faculty of Informatics, Óbuda University.
and the proposed content based extension. Relationships
in current product model, contextual content entities, and
content of adaptive action were analyzed considering an
organized approach.
Next future research will analyze knowledge definition
entities considering interfaces between current model and
extension features in order to establish control of product
features by adaptive actions. Feature principle makes it
possible to track the modification effect of a feature
product wide. In order to prepare this characteristic of the
proposed modeling, method for tracking of feature
modification effect is planned to conceptualize.
ACKNOWLEDGMENT
The authors acknowledge the financial support from the
research program for Research Groups at the Óbuda
University, Budapest, Hungary.
REFERENCES
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Figure 8.
Implementation using API
VII. SUMMARY AND FUTURE RESEARCH
An extension to currently representative product
modeling was conceptualized and methodically grounded
in the reported research. Main motivation is given by the
widely experienced moving of engineering activities into
extensive product modeling systems. These systems are
ready to accept knowledge from application environment
and place it in product model by using of engineer
understandable entities for situation and event based
generation of product features. The authors placed a new
content based functionality between engineer and feature
generation process. The result is a human intent initiated
request driven decision making on product features where
request definition includes knowledge or opens the way
for problem solving based knowledge definition. The
extension is intended as a contribution to product level
coordination of decisions on local level product features.
The modeling introduced in this paper is strongly and
organically tied to earlier results by the authors. The
contribution reported in this paper is constituted by the
following new findings. Some new connection and
connection driven definition related characteristics of
features were recognized and organized. Representative
product model was conceptualized considering knowledge
carrying features both in current industrial product models
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