validation in plm maropoulos ceglarek keynote cirp 2010
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Design verification and validation in product lifecycle
P.G. Maropoulos (1)a,*, D. Ceglarek (1)b
a Department of Mechanical Engineering, University of Bath, Claverton Down, Bath BA2 7AY, UKbWarwick Digital Laboratory, University of Warwick, Coventry, UK
1. Introduction
Globalisation coupled with product customisation and short
time to market have spearheaded new levels of competitionamong manufacturers. In CIRP, the needs for design adaptability
[1], the ability to develop products and services for the e-
commerce 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 func-
tionality 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 thescientific 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
development. The paper also defines the design application areas
in terms of products, processes and systems and reviews main-
stream 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. 1shows 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. 2shows 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.
CIRP Annals - Manufacturing Technology 59 (2010) 740759
A R T I C L E I N F O
Keywords:
Design
Validation
VerificationLifecycle management
A B S T R A C T
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 duringthe 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 analysisand 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 thecontext of their lifecycle.Industrialrequirements arehighlighted andresearch trends and
priorities identified.
2010 CIRP.
* Corresponding author.
Contents lists available atScienceDirect
CIRP Annals - Manufacturing Technology
j ourna l hom e pa ge : ht t p: / / e e s. e l se v i e r. com / ci rp/ de f a ul t . asp
0007-8506/$ see front matter 2010 CIRP.
doi:10.1016/j.cirp.2010.05.005
http://dx.doi.org/10.1016/j.cirp.2010.05.005http://www.sciencedirect.com/science/journal/00078506http://dx.doi.org/10.1016/j.cirp.2010.05.005http://dx.doi.org/10.1016/j.cirp.2010.05.005http://www.sciencedirect.com/science/journal/00078506http://dx.doi.org/10.1016/j.cirp.2010.05.005 -
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The following observations are valid in relation to the present
industrial practice for design verification and validation:
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 genera-
tion digital or virtual testing methods.
Complexity in design makes verification and validation even
more difficult to apply as part of the design process.
2.2. Scope of the keynote paper
2.2.1. A framework for design verification and validation
Fig. 3shows the scope of the new framework for engineeringdesign verification and validation which is lifecycle based, tracking
the progression of engineering designs across four key stages: (i)
from thepreliminary designstagethat sets therequirements,(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
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, manu-
facturing 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.
2.2.2. Keynote scope
Thescope forthis keynote is outlined in Fig. 4. The mainfocusof
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 designfrom 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. 1. Validation and verification requirements in the product lifecycle.
[
Fig. 2. Closed-loop validation and verification.
[
Fig. 3. A conceptual framework for design verification and validation.
P.G. Maropoulos, D. Ceglarek/ CIRP Annals - Manufacturing Technology 59 (2010) 740759 741
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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 thata 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 inter-
changeably [7,8], or in some cases there is reference to verifica-
tion, 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 targetmeasurement uncertainty has been met [17]. The definition of
validation is much less specific, referring to the adequacy of
requirements for an intendeduse. The verification definitionby the
International Organisation of Legal Metrology[18]is based on the
interpretation of theword accurate,and it clearly creates a direct
link with metrologyin the process of establishinghow differentthe
real artefact is from its modelling representation.
There are extensive definitions of verification and validation in
the contextof digital design and these definitions also cover aspects
of modelling and simulation. These include the IEEE Standard 610
[10] andthe definitions of theUS Departmentof Defence(DoD) [12],
as shown inTable 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 verificationand 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 aregivenby ISO9000 [16]. Asthe digital stages ofdesign
become increasingly important, the verification of the modelling
[
Fig. 4. Scope of the keynote paper.
Table 1
Definitions of verification and validation in the digital and physical domains.
Verification Validation
V&V processes in digital design phase 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 evaluating software during or at the
end of the development process to determine whether
it satisfies specified requirements[10]
The process of determining that a computational
model accurately represents the underlying
mathematical model and its solution[11]
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 that a computer model,
simulation, or federation of models and simulations
implementations and their associated data accurately
represent the developers conceptual description and
specifications [12]
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 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 an M&S implementation
and its associated data accurately represent the
developers conceptual description and specifications[13]
The process of determining that a model accurately
represents the developers conceptual description ofthe model and the solution to the model [14]
The process of determining the degree to which a model
is an accurate representation of the real world from theperspective of the intended uses of 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]
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]
V&V processes in physical world Confirmation, through the provision of objective
evidence, that specified requirements have been
fulfilled[16]
Confirmation, through the provision of objective
evidence, that the requirements for a specific intended
use or application 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]
Where the specified requirements are adequate for an
intended use[17]
Pertains to the examination and marking and/or
issuing of a verification certificate for a measuring
system [18]
Objective evidence that a process consistently produces
a result or product meeting its predetermined
requirements[19]
Confirmation by examination and provision of
evidence that the specified requirements have beenfulfilled[19]
Validation of requirements and specific assumptions is
the process of ensuring that the specified requirementsare sufficiently correct and complete so that the product
will meet applicable airworthiness requirements[20]
The verification process ensures that the system
implementation satisfies the validated requirements[20]
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and simulation aspects [10,12] will become increasingly applic-
able. The overall process for integrated digital and physical
prototype verification and validation is exemplified by SAE
Aerospace[20], seeFig. 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 preservingthe
designers intent and seamlessly utilising the associated informa-
tion and manufacturing practices in a heterogeneous manufactur-
ing environment. The transition of the designers intent from the
digital design specification to the actual product and associated
service realisation is illustrated in Fig. 5. Today,as eachphase ofthe
products 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 aglobal 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 suchas 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 designers 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 comprehensivechain-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, designers
intentregarding the size of theproductsfeature 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 characterisa-
tion of surface texture [28,29]. Srinivasan [30] identified the merits
of unifying and standardising ad hoc approaches practiced byindustry. 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 andshows
the functional requirements of the part as well as themethod forits
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
[
Fig. 6. Transition of designers intent to physical realisationthrough GPS guidelines.
[
Fig. 5. Verification in digital and physical world (adapted from Refs. [20,21]).
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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 partsas 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 bi-
directional 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 industrys 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 inwhich 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 integra-
tion 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 developedby
NIST, which later became a part of the international standard ISO
16100 for exchanging information between design and manufac-
turing process planning software systems for mechanical products
[43].
The need for comprehensive information regarding specific
manufacturing processes and the verification of components,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 stan-
dards have evolved in order to utilise and exchange the
information regarding manufacturing resources and their cap-
abilities in a digital environment [52]. For example, ISO 13584 [53]
with the acronym PLIB is a series of standards for the computer-
based 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 investin 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 correctcapture 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 character-
istics (KCs) and Design for X (DFX) techniques.
4.1. Product idea validation and market analysis
There are three key considerations that are applied in the early
stages of design: (1) to prioritise customer needs (CNs) in aquantitative 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
[
Fig. 7. Integration of designers intents within STEP framework.
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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. Buyukozkan 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 inFig. 8, a generic QFD process consists of four
phases in order to relate the voice of the customer to productdesign requirements (phase 1), andthen translate these intoparts
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 fordesign verification, by forming the setof
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 beviewed 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 validatedby considering bothlogical 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 ofa 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.
4.4. The use of key characteristics in early design
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 beenintroduced 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. Thorn-
ton [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 andTang [76] definedverification
parametersby prioritizingKCs.Whitney [77] proposeda KCoriented
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. Maropou-
los 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 designers awareness for a certain product lifecyclevalue 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.Theimpact of Design for Assembly (DFA) [77,83] on verification
is also direct. For instance, the part reduction of an electro-
mechanical 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 canbe supplied in therightorientationand do not tangle
with other parts [84]. This again increases process yield and
influences the sampling method deployed for assembly verifica-
tion data collection and analysis.
Design for Ergonomics is important in labour intensive
industries[85]and has a noticeable and positive effect on processverification, 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 verifica-
tion as a consequence of high repeatability set-ups.
[
Fig. 8.Four-phase process planning by QFD [63].
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Design for 6-sigma (DFSS) is a design activity that aims to
generate high capability, 6s processes, before production com-
mences. DFSS is usually deployed within QFDand is also referred to
as DefineMeasureAnalyseDesignVerify [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 applyingprocess 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. Designverificationand validation in the digital environment
Digital prototyping helps manufacturersto virtually simulatea
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 experimenta-
tion 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 productfunctionality. DMU is emerging as the core design collaboration
tool, aroundwhich different engineering teams verify the product
through its entire lifecycle, from production planning to func-
tional 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,
owingin a large part to the availabilityof 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-upwas built [90]. Using proprietary DMUsystems,
Boeing was able to reduce errors and rework on its777 airliner by7080%, 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 instrumentneedsto be an order of magnitudesmaller than thetolerance 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 toleranceproblems [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 experi-
mentally 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 tradi-
tional 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 becomea 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 under-
standing 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. Tolerance analysis [98].
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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 toler-
ance 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 Taguchis quality
loss function Cheng and Maghsoodloo [112] found that when a
components mean varies, only the quality loss associated with
that component will be changed; whereas when a components
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 pointfor
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.
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 identifiedthree 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 featuresare instantiated by specifying dimension parameters, location
parameters and application related attributes. Feature-based
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 theverification of
machined features, or complex assembly features.
Case [122] used methods associated with external approachdirections for features to enhanceprocess capability and Wong and
Wong[123]used volumetric machining features for part model-
ling 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 allowsrapidverificationof a designin 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 precedencegraphsfor assembly sequence planning
[125].Qiaoetal. [126] useda genetic algorithm methodto sequence
the machining operations for prismatic parts. Li et al. [127] andOng
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].
Thecommon problems andcharacteristics of theseCAPP approaches
for machining are one or more of the following:
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 arenot 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 thesearch space or loosesfeasible
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 istheoretically 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
Virtual or digital assembly modellingis a powerful andeffective
technology for the verification of assemblies during the digital
design phase. Assembly process planning (APP) is a core
component of virtual assembly modellingas 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-piecemixed-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. 10. Tolerance design methods [99].
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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 sum-
marised 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. 11illustrates 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 environ-ment. Unlike VR,the assembly designand planning using AR canbe
verified by manipulating the virtual prototypes in the real
assembly environment, which will decrease the possibility of re-
designing 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 beingdeployed 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 thatwill enhance industrys 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 compo-
nents that allow a virtually unlimited number of tooling
configurations. Ceglarek et al. [142]extended the N-2-1 fixturelayout 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
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 products dimensional quality.
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 processparameters 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 featurerecognition, (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 planningfor 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 theautomation 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. 11. The VADE usage scenario [134].
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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 planningand 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 produc-
tion 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. 12shows the
metrology framework, with the metrology process model posi-
tioned central to the integration of the design verification process
with the verification of assembly operations and the subsequent
deployment of measurement systems that support measurement-assisted automation. The framework explicitly recognises the need
to co-ordinate the digital verification aspects (left part ofFig. 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 metrol-
ogy 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 selectionA 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 genericmeasurement 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 realworld 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-lifestructuraldesign problem and turnsit 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
idealised mechanical system. The creation of an error-control[
Fig. 13. Virtual testing procedure.
[
Fig. 12. Overview of the theoretical framework for integrating measurement with assembly planning.
P.G. Maropoulos, D. Ceglarek/ CIRP Annals - Manufacturing Technology 59 (2010) 740759 749
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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 uncertain-
ties 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) dueto 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 representa-
tion 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 experi-
mental 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 beha-
viour 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 theverification 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 useof available resources. CFDhas resulted in
significant improvements to the design of compressor and turbine
blades [179], including the use of inverse design and multi-
objective 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-turbineinternal-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 shearstress, v2-f or the even
more substantial ReynoldsStress 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 formore 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 solidmodel 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
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 excep-
tion 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 inFig. 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 perfor-
mance, 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 inFig. 15.
[
Fig. 14.Isosurface of instantaneous vorticity over an F-18C aircraft at 308angle ofattack[185].
[
Fig. 15. Dimensional and shape characteristics of GPS standards [186].
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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 shapeverification[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
artefactsand these systemsare outlined in theenablingtechnologies
Section 6.4. Indirectdimensionalverification requires takinginferred
dimensions from something other than the part, for example by
measuring the jig that is used to assemblethe 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.
Thelevel of inspection required forany 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 5075% 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 theyhave 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 applic-
able for surface verification, as shown inFig. 16.
The production uncertainties of a free form surface, com-
pounded 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 ofautomotive panels is indicative of the build quality of the product.
The assembly methods used to minimise freeformsurface 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 partsposition 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.
Measurementof these features generally requires an ability to see
around corners or measure directly through opaque objects.
Possible approaches include; networks of line-of-sight instru-
ments; 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-of-
freedom 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 processesespecially for large volume applications [171] using models
[
Fig. 16. Examples of freeform surface verification applications.
P.G. Maropoulos, D. Ceglarek/ CIRP Annals - Manufacturing Technology 59 (2010) 740759 751
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7/26/2019 Validation in PLM Maropoulos Ceglarek Keynote CIRP 2010
13/20
created for laser-based spherical co-ordinate measurement
systems, such as laser trackers and laser radar [170,197]. Co-
ordinate 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].
6.2. Product testing and validation
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 inFig. 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 definemechanical 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
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 accurate2D 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 inTable 3.
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 inFig. 18and 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
Table 2
Selected materials parameters and associated test methods.
Property Parameter Test method
Strength (maximum, yield, etc.) s(MPa) Tension, compression,
flexure, etc.
Strain (maximum, yield, etc.) e Tension, compression,
flexure, etc.
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