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]

P.G. Maropoulos, D. Ceglarek/ CIRP Annals - Manufacturing Technology 59 (2010) 740759742


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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.



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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.



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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.

Validation in PLM Maropoulos Ceglarek Keynote CIRP 2010

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