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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, UKb Warwick Digital Laboratory, University of Warwick, Coventry, UK

1. Introduction

Globalisation coupled with product customisation and shorttime 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 designcomplexity [3] have been recognised. To be successful in theglobal market, manufacturing companies are increasinglyexpanding simulation models from product and process based(value chains) to service based (value networks) by focusing onlifecycle simulations and design for product variation [4] toobtain both quality of product and robustness of processes, andto enable the validation and verification of products andprocesses to 6-sigma. These methods are vital to reduce processfaults and facilitate efficient and effective engineering changes.Current validation and verification-based approaches mainlyfocus on product conformance to specifications, product func-tionality and process capability. However, even the most robustsystems can be subject to failures during product verification andvalidation.

This paper presents the concepts of validation and verificationin the product lifecycle by including analysis and review ofliterature and state-of-the-art in: (i) preliminary design, (ii) digitalproduct and process development; (iii) physical product andprocess realisation; (iv) system and network design; and (v)

development. The paper also defines the design application arin terms of products, processes and systems and reviews mstream methods and systems.

2. Motivation, scope and definitions of verification andvalidation methods and technologies

2.1. Motivation

The current product and production system requirementsinfluence the way products are developed and verified includ

� Mass customisation and personalisation.� Reconfigurability and flexibility of production systems.� Responsive factories.

Products and processes need to be designed, verifiedvalidated in a manner that is compatible with the above industrequirements. Fig. 1 shows a representation of validating produand processes after the digital modelling phase, clearly identifythe research questions and business drivers.

Validation in the digital space is a key objective and industrequirement that drives research and development. If this werbe feasible, the results would have been reduced lead timescritically, fewer failures and better perceived product qualitythe customers. Fig. 2 shows the closed-loop nature of the proc

A R T I C L E I N F O

Keywords:

Design

Validation

Verification

Lifecycle management

A B S T R A C T

The verification and validation of engineering designs are of primary importance as they dire

influence production performance and ultimately define product functionality and customer percept

Research in aspects of verification and validation is widely spread ranging from tools employed du

the digital design phase, to methods deployed for prototype verification and validation. This p

reviews the standard definitions of verification and validation in the context of engineering design

progresses to provide a coherent analysis and classification of these activities from preliminary desig

design in the digital domain and the physical verification and validation of products and processes.

scope of the paper includes aspects of system design and demonstrates how complex products

validated in the context of their lifecycle. Industrial requirements are highlighted and research trends

priorities identified.

� 2010 C

Contents lists available at ScienceDirect

CIRP Annals - Manufacturing Technology

journal homepage: http: / /ees.elsevier.com/cirp/default .asp

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complex product verification and validation.The paper starts with a summary of the scientific motivation for

the review of design verification and validation. The definitions ofverification and validation are then covered, including conceptsand definitions arising from ISO standards as well as software

uct

* Corresponding author.

Please cite this article in press as: Maropoulos PG, Ceglarek D. DeManufacturing Technology (2010), doi:10.1016/j.cirp.2010.05.005

0007-8506/$ – see front matter � 2010 CIRP.

doi:10.1016/j.cirp.2010.05.005

required for managing the lifecycle data capture for desvalidation. This ability presupposes:

� Integrated and holistic views of design in order to be ablevalidate in an integrated manner.� Digital modelling and representation ability for both the prod

and the process (function and specification testing).� A time horizon that includes the product lifecycle.

sign verification and validation in product lifecycle. CIRP Annals -

http://dx.doi.org/10.1016/j.cirp.2010.05.005


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he following observations are valid in relation to the presentstrial practice for design verification and validation:

ch activities are usually executed when the design process isost complete, during prototyping and first-off testing and

velopment. This results in frequent deviations from thequired form, dimensions or function, extending development

es and increasing the compliance cost.is problem is both procedural (stage or time of execution ofch activities and requirement for different skills) andeoretical (lack of robust verification and validation methodsr deployment during the digital design stages).e aim is to execute verification and validation as early asssible during the design process, by developing new genera-n digital or virtual testing methods.mplexity in design makes verification and validation evenore difficult to apply as part of the design process.

Scope of the keynote paper

. A framework for design verification and validation

ig. 3 shows the scope of the new framework for engineeringgn verification and validation which is lifecycle based, trackingprogression of engineering designs across four key stages: (i)

the preliminary design stage that sets the requirements, (ii) todigital design domain, (iii) the physical, product and processlopment and prototyping phase, and (iv) the consequent

gn of the production system and network for the realisation ofplex products and processes.roduct and process designs are developed in the digitalain and the final validation usually requires the execution of

sical trials to confirm the product properties, dimensions and

overall functionality at component, subsystem and completeproduct level. Processes are also validated at each one of theirphysical levels so as to provide the required physical attributes ofcomponents, sub-assemblies and the overall product. The systemand network design and development also includes a digital phaseand major considerations are confirmed by validating real systemperformance. Product lifecycle aspects are best exemplified byconsidering how complex products are validated in the context oflifecycle considerations. The framework shown in Fig. 3, puts acoherent structure to the multiplicity of digital analyses, manu-facturing processes and metrology technologies needed for theverification and validation of complex products in their lifecycle.These techniques and methods and their relevance to designverification and validation are analysed herein.

2.2.2. Keynote scope

The scope for this keynote is outlined in Fig. 4. The main focus ofthe paper is on product and process verification and validation.System perspectives are also included for completeness andlifecycle aspects are covered by reviewing standards and practicesin 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 asaerospace, marine and automotive.

2.3. Definitions of verification and validation

Verification and validation are the methods that are used forconfirming that a product, service, or system meets its respectivespecifications 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.

ase cite this article in press as: Maropoulos PG, Ceglarek D. Design verification and validation in product lifecycle. CIRP Annals -nufacturing Technology (2010), doi:10.1016/j.cirp.2010.05.005


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whether or not a product, service, or system complies withregulations, specifications, or conditions imposed at the start of adevelopment phase [5,6]. Validation, on the other hand, is a qualityassurance process of establishing evidence that provides a highdegree of assurance that a product, service, or system accomplishesits intended use requirements [5,6]. Verification and validationhave been defined in various ways that do not necessarily complywith standard definitions. For instance, journal articles andtextbooks 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 1shows definitions of verification and validation as provided byinternational and national bodies.

The definitions given by ISO 9000 [16] originate from thegeneral field of quality and focus on the provision of ‘‘objectiveevidence’’ that specified requirements have been fulfilled. The

verification process according to ISO is broadly defined,validation is focused on fulfilling an intended use or applicatThe Global Harmonisation Task Force, defines verification imanner compatible with ISO, and process validation is basedconsistent generation of results that satisfy predetermirequirements [19]. However, such generic definitions evoldue to the specific demands of application domains. For examin the field of metrology, the Joint Committee for GuidesMetrology defines verification on the basis that a ‘‘tameasurement uncertainty has been met’’ [17]. The definitionvalidation is much less specific, referring to the adequacyrequirements for an intended use. The verification definition byInternational Organisation of Legal Metrology [18] is based oninterpretation of the word ‘‘accurate’’, and it clearly creates a dilink with metrology in the process of establishing how differentreal artefact is from its modelling representation.

There are extensive definitions of verification and validatiothe context of digital design and these definitions also cover aspof modelling and simulation. These include the IEEE Standard[10] and the definitions of the US Department of Defence (DoD) [as shown in Table 1. The US Department of Navy [13] and theCommittee of AIAA [14] provide definitions for modellingsimulation software systems that are derivatives of those proviby the US DoD. The US Food and Drug Administration has gidefinitions of digital systems verification and validation [15], whexplicitly include references to the ‘‘consistency’’ and ‘‘correctnof the software. SAE Aerospace [20] and Sargent [21] reportevariety of design verification aspects, as shown in Fig. 5.

In summary, the generic definitions for design verificationvalidation are given by ISO 9000 [16]. As the digital stages of desbecome increasingly important, the verification of the model

Fig. 4. Scope of the keynote paper.

Table 1Definitions 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 mod

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 developer’s conceptual description and

specifications [12]


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 implementatio

and its associated data accurately represent the

developer’s conceptual description and specifications [1

The process of determining that a model accurately

represents the developer’s conceptual description of

the model and the solution to the model [14]


is an accurate representation of the real world from th

perspective 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 objectiv

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 been

fulfilled [19]

Validation of requirements and specific assumptions is

the process of ensuring that the specified requirements

are sufficiently correct and complete so that the product

will meet applicable airworthiness requirements [20]

The verification process ensures that the system

implementation satisfies the validated requirements [20]

Please cite this article in press as: Maropoulos PG, Ceglarek D. Design verification and validation in product lifecycle. CIRP Annals -Manufacturing Technology (2010), doi:10.1016/j.cirp.2010.05.005


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simulation aspects [10,12] will become increasingly applic-. The overall process for integrated digital and physicalotype verification and validation is exemplified by SAEspace [20], see Fig. 5, and the metrological practice governing

physical prototypes is given by VIM [17].

ternational standards related to product and processgn in the lifecycle perspective

nternational standards play an important role in preserving thegner’s intent and seamlessly utilising the associated informa-and manufacturing practices in a heterogeneous manufactur-

environment. The transition of the designer’s intent from thetal design specification to the actual product and associatedice realisation is illustrated in Fig. 5. Today, as each phase of theuct’s lifecycle is globally dispersed in supply and knowledgens [2], international standards are essential to deploydardised manufacturing execution protocols in order toblish an unambiguous definition ‘‘language’’ throughout aal supply chain and ensure consistent product performance inservice phase. Hence, the provisions of the most relevant touct and process verification and validation standards areysed herein.

Standards for representing product information

tion constructs known as ‘‘application protocols’’ in STEP and ‘‘GPSmatrix’’ in GPS.

Current GPS standards define global guidelines along withfundamental principles for capturing designer’s intent andexpressing design requirements. Product and process designcharacteristics such as size, angle, orientation and surface textureare considered as individual chains as shown in Fig. 6. Theinformation regarding each characteristic is categorised accordingto its relevance in the product lifecycle. Each category is called a‘‘link’’ within the GPS masterplan [22]. Thus, a comprehensive‘‘chain-link’’ matrix (Fig. 6) has resulted in a number of GPSstandards which address how product specific characteristics canbe represented and utilised throughout the design, manufactureand verification phases of the product. For example, designer’sintent regarding the size of the product’s feature is preserved in the‘‘size’’ chain of the GPS matrix.

Mathieu and Dantan [25] proposed to ISO a new model forGeometric Specification and Verification called ‘‘GeoSpelling’’ as abasis for GPS standards rebuilding. The merits of GPS standardshave been exploited in a variety of digital product designapplications such as coherent tolerancing process [26], evaluationof measurement uncertainty [27] and quantitative characterisa-tion of surface texture [28,29]. Srinivasan [30] identified the meritsof unifying and standardising ad hoc approaches practiced byindustry. GPS allows such unification and standardisation throughglobal guidelines described in the GPS masterplan [22]. Morerecent GPS standards [31] introduced the concepts of specificationuncertainty and correlation uncertainty that directly influencevalidation and verification.

A symbolic language called GD&T [23] has been developed fordescribing nominal geometry of parts and assemblies and

Fig. 6. Transition of designer’s intent to physical realisation through GPS guidelines.

. 5. Verification in digital and physical world (adapted from Refs. [20,21]).

omputer interpretable representation of product informationtilised within a variety of CAx applications for design

fication and validation. The majority of these standardsesent geometric information and evolved to cover othercts. Standards such as Geometrical Product Specification (GPS), ASME Y14.5: Geometric Dimensioning and Tolerancing&T) [23], STandard for Exchange of Product model data (STEP)have thus evolved for modelling and preserving other aspectsroduct related information such as tolerances, kinematics,

amics and manufacturing processes. For example, the STEP andstandards have evolved, providing product specific informa-

ase cite this article in press as: Maropoulos PG, Ceglarek D. Denufacturing Technology (2010), doi:10.1016/j.cirp.2010.05.005

allowable variation in the product design and verification phase.GD&T brings significant benefits in design and inspection activitiesas a correct GD&T representation captures design intent and showsthe functional requirements of the part as well as the method for itsinspection [23]. Arguably, the most important benefit of the GD&Tapproach lies in ensuring, at the design phase, that componentparts will assemble into the final product and function as intended[32]. Shen et al. [33] proposed a semantic GD&T representationmodel, named the ‘‘constraint-tolerance-feature-graph’’ that isclaimed to satisfy all tolerance analysis needs. Kong et al. [34]formulated an approach for the analysis of non-stationary



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tolerance variation during a multi-station assembly process withGD&T considerations. The application of GD&T for mechanicaldesign has gained widespread acceptance by industry [35].However, several organisations have attempted to implementthe method without a fundamental understanding of how thedesign process is impacted [36]. Poorly applied GD&T, ambiguousplus/minus location or orientation controls, and sometimes novariation specifications are commonly encountered [37]. The needto capture functional requirements and improve the design of partsas well as to consider the cost and quality issues defined by GD&Tmakes this subject an even more important element of mechanicalengineering design [38].

In summary, the GPS [22,31] and GD&T [23] standards are vitalfor the correct and efficient verification of mechanical engineeringdesigns. There are exciting new research opportunities arisingfrom the utilisation of these standards to automate the bi-directional relationships between design specifications, processcapability and measurement uncertainty.

The STEP project was launched with the objective of conservingthe manufacturing context and developing information bridgesbetween segregated CAx domains [24]. EXPRESS [39] is used tospecify requirements on information content as ‘‘it consists oflanguage elements that allow an unambiguous data definition andspecification of constraints on the data defined’’. The developmentof the STEP standard was governed by industry’s need to overcomeinteroperability problems. The standard established a neutral datafile format that is used for developing domain specific applicationsusing application protocols (APs). For example, AP 219 [40]provides information requirements for analysing the dimensionalinspection data and results of solid parts and assemblies. Fig. 7shows a selected set of application protocols that are vitallyimportant for the communication and sharing of data required indesign verification and validation of mechanical components.

3.2. Standards for representing manufacturing processes

A ‘‘process’’ in a manufacturing context is defined as acombination of activities that occur over a period of time inwhich objects participate [41]. The National Institute of Standardsand Technology (NIST) in the USA developed the ProcessSpecification Language (PSL) [42] to create a generic, neutraland high-level language for specifying processes and the integra-tion of multiple process-related applications. PSL uses the ontologybased Knowledge Interchange Format to specify concepts,terminology and relationships for processes. Similarly, a datamodel for representing manufacturing processes was developed byNIST, which later became a part of the international standard ISO16100 for exchanging information between design and manufac-turing process planning software systems for mechanical products[43].

The need for comprehensive information regarding specificmanufacturing processes and the verification of components,compelled practitioners to develop process specific international

standards such as DMIS [44], DML [45] and I++DME [46] forexchange of inspection process information and measuremresults in the production environment. Similarly, the BS EN8062 series [47] and the BS EN ISO 10135 [48] series of standawithin the GPS framework cover the requirements for castingmoulding processes. Another set of process specific standardthe ISO 14649 series [49], with parts corresponding to differprocesses; for instance, part 16 [50] for performing inspecoperations in a STEP-NC manufacturing environment.

3.3. Standards for representing manufacturing resources

A typical manufacturing system consists of a range of resousuch as machine tools, material handling systems, fixtures, robarms, and measurement systems [51]. Each resource has a distpurpose and thus provides specific capabilities that are utilisemanufacturing decision-making. A variety of international stdards have evolved in order to utilise and exchangeinformation regarding manufacturing resources and their cabilities in a digital environment [52]. For example, ISO 13584 [with the acronym PLIB is a series of standards for the compubased representation and exchange of part library data. PLIB is finter-operable with STEP [24]. Resource specific standards hevolved to satisfy business needs. For example, ISO 13399 [deals with the representation and exchange of cutting tool dand ASME B5.59-2 [55] is an information model for machine toMeasurement equipment related GPS standards [56,57] wdeveloped to describe the acceptance tests for co-ordinmeasuring machines and general requirements for GPS measuequipment respectively.

3.4. Standards for preserving design verification knowledge

International standards are used to preserve and seamletransfer context specific knowledge obtained through desverification, within a heterogeneous manufacturing environmBusiness sectors such as, aerospace manufacturing, defence, sbuilding and military equipment manufacturing intensively invin research and development activities and have a strrequirement to conserve and reuse knowledge acquired throthe design verification processes. Consequently, ISO 10303 AP[58] has been developed by aerospace and commercial reseaorganisations for associating engineering analysis data wgeometric data. ISO 10303 AP 237 deals with the exchangecomputational fluid dynamics (CFD) information, includproduct geometry, associated meshes defining the computatiodetails and CFD boundary conditions [59].

4. Verification and validation in the early stages of designcapture intent and confirm requirements

The early design stages are vitally important for the corcapture of technical and lifecycle requirements arising frunderstanding and interpreting market needs. Verificationinherent in methods deployed during these important early staalthough this is not always appreciated by designersmanufacturing practitioners. This section outlines methodsdesign idea validation and quality function deployment (QFDwell as the more technical aspects of ensuring that consistenc

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terms of key design objectives is maintained using key characistics (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 estages of design: (1) to prioritise customer needs (CNs) iquantitative manner based on market analysis; (2) to selectbest design schema; and (3) to improve communication atlevels of the organisation. Methods such as matrix prioritisaand analytical hierarchy process [60] are applied to help



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rprise determine where to invest the development resourceschieve maximum payoff.he traditional way is to analyse CNs systematically and tosform them into the appropriate product features. However, itfficult to assess the performance of the transformation process

an accurate quantitative evaluation. Buyukozkan et al. [61]ented a fuzzy group decision-making approach to better align

with objectives of product development in QFD. Thisritisation of customer needs creates a set of criteria that is

for validating the final product i.e., assessing whether therprise is building the right product, service or system.

Quality function deployment

FD is a customer-driven methodology for product design andlopment that underpins quality systems and has foundnsive applications in industry via the development of atiplicity of tools and systems that aid an enterprise inerstanding the voice of the customer [60]. QFD efficientlyslates CNs into design requirements and parts deployment. As shown in Fig. 8, a generic QFD process consists of fourses in order to relate the voice of the customer to productgn requirements (phase 1), and then translate these into partsacteristics (phase 2), manufacturing operations (phase 3),production requirements (phase 4) [63]. During early design,first and second phases of the four QFD phases are

lemented [63] and part characteristics are defined. Inmary, QFD is critical to design validation as it translatesomer needs into part characteristics and production controlscan then be used for design verification, by forming the set ofria against which product and process compliance can bessed.

Functional decomposition and flow analysis

he verification and validation process of a function can beed as functional decomposition and flow analysis which aim

reak overall functionalities down to functionally independentfunctions as finely as possible [64]. A functional structure canalidated by considering both logical and physical dependenciesconfirming matching inputs and outputs among sub-functions. Several flow analysis methods such as bond graph and Petri

[66] and modularity methods such as function structure

4.4. The use of key characteristics in early design

Variability in production and measurement procedures canresult in lower than expected quality levels, compromised productperformance and increased rectification costs. Key characteristics(KCs) are being used to help identify and reduce important rootcauses of variability [70]. Research focused on KCs has had asignificant impact in improving product and process performancein the context of the lifecycle [71,72]. KC methodologies have beenintroduced into the product development practices of world-classcompanies [73]. Thornton [74] categorised product related KCsaccording 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 inaircraft and automotive production by establishing a direct linkbetween KCs and the type of inspection process used forverification.

The use of KCs for manufacturing planning during early designenhances process verification. Dai and Tang [76] defined verificationparameters by prioritizing KCs. Whitney [77] proposed a KC orientedmethod for assembly planning by selecting the necessary partfeatures, tools and machine capabilities. Wang and Ceglarek [78]developed a KC based methodology for quality-driven sequenceplanning. Suri et al. [79] introduced a technique based on keyinspection characteristics to enhance process capability. Maropou-los et al. [80] proposed the use of aggregate product models as amethod for the early integration of dimensional verification andprocess planning for complex product design and assembly.Maropoulos et al. [81] outlined the verification and validationrelated benefits arising from the integration of measurement andassembly using a digital enterprise framework that links keyelements of the product, process and resource models.

4.5. Design for X

Design for X (DFX) is an umbrella term used to denote designphilosophies and methodologies which aim to improve designs byraising the designer’s awareness for a certain product lifecyclevalue or characteristic represented by ‘X’ [82]. The designconsiderations applied in DFX have a direct relationship to theverification methods for the ‘‘X’’ objective.

Design for Manufacture (DFM) [77,83] includes a wide range ofdesign rules and guidelines defined from the perspective ofimproving the manufacturability of parts. For example, the designguidelines for end milling stipulate that milled features should bedesigned in such a way so that the end mill required is limited to3:1 in length to diameter ratio; the reason being that longer endmills are prone to chatter that deteriorates surface quality.Applying this DFM guideline will impact directly on end millingprocess capability in terms of surface quality and this will influencethe process verification procedure, such as the sampling methoddeployed and the method of surface roughness measurement.

The impact of Design for Assembly (DFA) [77,83] on verificationis also direct. For instance, the part reduction of an electro-mechanical sub-assembly as a consequence of applying DFA mayresult in more complex parts that have additional features. Thiswill directly change the inspection plan in terms of the number,type and sequence of measurement operations, the measurementpoints per operation and the selection of the measuring device.

Fig. 8. Four-phase process planning by QFD [63].

istic method [67], design structure matrix [68] and modulartion deployment [69] are applicable to the verification andation of functional structures.

n an era of increasing product sophistication, engineeredems are likely to become more complicated, increasing thetional requirements [3]. Suh [3] defined complexity as thesure of uncertainty in achieving the functional requirements ofmplex system and outlined how axiomatic design can be usededuce design complexity while satisfying the functionalirements within given constraints. As such, axiomatic designenhance the functional validation of designs.


Also, DFA for automated assembly stipulates design methods sothat parts can be supplied in the right orientation and do not tanglewith other parts [84]. This again increases process yield andinfluences the sampling method deployed for assembly verifica-tion data collection and analysis.

Design for Ergonomics is important in labour intensiveindustries [85] and has a noticeable and positive effect on processverification, as controls and displays are re-designed so thatreadings cannot be misinterpreted. Design for changeover is vitalin high variety environments [86] and improves process verifica-tion as a consequence of high repeatability set-ups.



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Design for 6-sigma (DFSS) is a design activity that aims togenerate high capability, 6s processes, before production com-mences. DFSS is usually deployed within QFD and is also referred toas ‘‘Define–Measure–Analyse–Design–Verify’’ [87]. This is anexplicit reflection of the inherent ability of DFSS to enhance theverification and validation of processes.

There are considerable research challenges in developing newmethodologies that link DFSS with KCs, so that key productfeatures and dimensions are specified and evaluated by applyingprocess capability criteria. Such methods would need to be directlyintegrated with the definition of GD&T, so that datum points, keydimensions, inspection methods and process capability areinterlinked in an unambiguous manner.

5. Design verification and validation in the digital environment

Digital prototyping helps manufacturers to virtually simulate aproduct and its associated lifecycle phases such as, productmanufacture, assembly and functionality, before the product isphysically realised. This gives manufacturers an excellentopportunity to visualise and anticipate aspects of the physicalperformance of a design with less reliance on costly physicalexperimentation. Physical prototyping and testing is still arequirement, especially for complex products. However, the clearcurrent industry trend is toward reducing physical testing byreplacing suitable aspects by virtual testing and verification. Thedigital verification results are compared with the experimenta-tion results; this validates and certifies computational codeembedded in a digital prototype. Thus, a validated digitalprototype can be utilised for verifying the physical performanceof the product manufactured in the globally dispersed supplychain.

5.1. Digital mock-up

A digital mock-up (DMU), sometimes referred to as a virtualprototype, is essentially a digital simulation of a physicalprototype and is increasingly used for the verification of productfunctionality. DMU is emerging as the core design collaborationtool, around which different engineering teams verify the productthrough its entire lifecycle, from production planning to func-tional testing, maintenance and recycling [88,89]. Multipleengineering teams can now operate in parallel, working on thesame DMU, and this facilitates the enterprise wide application ofconcurrent engineering practice. Recently, the usage of DMU hasincreased, mainly among aerospace and automotive companies,owing in a large part to the availability of more robust models andenhanced computing resources. For instance, the ChryslerCorporation, used DMU to reduce automobile development cycleby half, while resolving 1200 potential issues before the firstphysical mock-up was built [90]. Using proprietary DMU systems,Boeing was able to reduce errors and rework on its 777 airliner by70–80%, saving 100,000 design hours and millions of dollars [90].Similarly, Airbus is also increasingly exploiting the advantages ofDMU [91].

For complex engineering products, the use of DMU is notwithout problems, the largest of which is ensuring data qualitybetween all of its suppliers, customers and design offices. Forinstance, data loss when transferring from one CAD format to

5.2. Tolerance analysis and optimisation

The primary function of tolerance setting is to balanceproduct functionality with economic factors [97]. Excessively ttolerances will add cost due to more complex processing stawhereas inadequately wide tolerances will result in insufficquality and costly rework. Tolerances are vitally important inprocess of dimensional verification of mechanical partsassemblies as the uncertainty of the measurement instrumneeds to be an order of magnitude smaller than the tolerance vaHistorically, tolerances are decided on the basis of legacy pracwithin a company and as Maropoulos et al. [81] suggest, mtolerances are set based on process capability and not on the stof tolerance build-up during assembly. A review of tolerancmethods by Singh et al. [98] identifies the main academicindustrial practices dealing with tolerancing as belonging to eit‘‘tolerance analysis’’ or ‘‘tolerance synthesis’’. In essence, toleraanalysis attempts to estimate the assembly tolerance stackwhile synthesis considers the assembly and product requiremeand distributes the assembly tolerances accordingly [99].

5.2.1. Modelling assembly tolerances

Dantan and Qureshi [100] describe statistical tolerance analas a 2D method that computes the probability that the productbe assembled and will function under a given set of tolerances.assembly response function can be expressed as a function ofindividual and independent component dimensions [101].shown in Fig. 9, there are two basic approaches to toleraanalysis, the worst-case method and the root sum square met[98]. The worst-case method assumes that the tolerances artheir respective extremities and the stack-up is consisteaccumulative (i.e., there is no tolerance cancellation). Thispessimistic estimate, but due to its simplicity it is still relevtoday; however it can only be employed in one-dimensiontime [102]. The root sum square (RSS) method conversely givrather optimistic assembly tolerance estimate, as it is a simstatistical model based on the normal distribution. As before,RSS method is only suited to single dimensional toleraproblems [103].

A more advanced method that is somewhat more indicativtolerance stack-up in the physical world, is the Spotts modiapproach [104]; this is essentially an average of the worse-casethe RSS model. The ‘‘correction factor’’ approach is also expmentally based, based on scaling the RSS to make it a more realifigure. However, this method has particular limitations iftolerances/dimensions in the stack-up vary greatly and/or arsmall quantities [98].

More complex assembly response functions and non-nortolerance distributions can cause difficulties when using trtional analytical techniques as a high number of samplerequired to create an accurate estimation of the assemresponse. In such cases, Monte Carlo Simulation (MCS) has becoa viable solution. MCS can be applied when the assembly respofunction cannot be expressed analytically as a linear modelalso when dealing with the effects of tolerance stack-up witkinematic systems [105]. In the ‘‘kinematic’’ approach [106],tolerance chain is treated as a kinematic loop, with the undstanding that the movements of the links are actually smdisplacements within prescribed tolerance zones. This appro

ce-

Fig. 9. Tolerance analysis [98].

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 capabilitiesto simulate functional performance using functional mock-upmethods, and (ii) the solid foundation of international standards.The existing STEP (ISO 10303) standard captures adequatelygeometric data, while data pertaining to history based modelling[92], assembly [93], and kinematics linkages are less wellrepresented [94]. ISO 10303-105 [95] is a good base for kinematicstructure representation and supports case studies for machinetool modelling [96].


involves modelling the small displacements using small displa



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t torsors [107] and modelling the effects that local smalllacement have on the remote functional requirement usingbian transforms [108]. Desrochers et al. [109] proposed aed Jacobian-torsor model for statistical or worst-case toler-analysis or synthesis [110].

. Digital tolerancing methods and tolerance optimisation

ptimizing tolerances aims to maximise the functionalormance and economic factors associated with tolerances.economic factor is often expressed in a quality loss function] and in most applications the Taguchi loss function is used.

indaluri et al. [97] consider the quality loss from thepective of the customer and the manufacturing and rejections by the manufacturer. When incorporating Taguchi’s qualityfunction Cheng and Maghsoodloo [112] found that when aponent’s mean varies, only the quality loss associated withcomponent will be changed; whereas when a component’s

ance shifts, the optimal allowance, tolerance costs, and qualityes associated with each component will be affected. Tolerancemisation methods are classed as either deterministic orhastic; the former considers the nominal values of designables with respect to given input values, using a single point foruation, whereas the latter consider the statistical variation ofdesign variables [113,114].omputer Aided Tolerancing systems can provide a simulation

form for modelling the effects of tolerance setting within aufacturing process or assembly [115,116]. Tolerance analysissynthesis are considered within a DMU to include aspects of

rance build-up and assembly clashes [117]. Tolerance designhods have been summarised by Singh et al. [99] as shown in10, including traditional and advanced methods.

Features for machining CAD/CAM/CAPP verification

n the last two decades, extensive research efforts in variousents of CAx integration using feature technology have been

rted especially for the integration of CAD and CAM. Salomons. [118], and Subrahmanyam and Wozny [119] have identifiede major approaches of feature technology namely; interactiveure definition, automatic feature recognition and design byures.n interactive feature definition, features are defined withan assistance after creating the geometric model. Automatic

ure recognition involves the comparison of the geometricel with pre-defined generic features. Many approaches for

ure recognition have been reported; Lin et al. [120] extractedufacturing features present in a feature-based design model,

le ElMaraghy and ElMaraghy [121] introduced the concept oftional and manufacturing features.resently, the design – by – features approach has become the

technology for product modelling. Feature definitionsplates) are placed in the feature library, from which featuresinstantiated by specifying dimension parameters, locationmeters and application related attributes. Feature-based

design has made a direct and very positive impact on partverification as helped to codify and standardise both themanufacturing processes and the inspection methods used fortypes of features, thus improving design verification. Research isstill required to provide coherence in relating inspection systemsand methods to processes, especially in cases where there is a widerange of measurement options available, such as the verification ofmachined features, or complex assembly features.

Case [122] used methods associated with external approachdirections for features to enhance process capability and Wong andWong [123] used volumetric machining features for part model-ling in their feature-based design system. Several feature-baseddesign systems are reported with a focus on prismatic machiningprocess. In the case of machining, feature-based design allows thecorresponding definition of ‘‘standardised’’ machining processesthat are proven in terms of process capability. This is of majorsignificance, as it allows rapid verification of a design in terms of itsmodelling entities and the corresponding machining process.

Feature-based methods had a profound effect on computerautomated 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 operationsand the identifying of precedence relationships. Laperriere andElMaraghy used precedence graphs for assembly sequence planning[125]. Qiao et al. [126] used a genetic algorithm method to sequencethe machining operations for prismatic parts. Li et al. [127] and Onget al. [128] tried to solve the process planning problems bycombining the non-traditional optimisation techniques, namelygenetic algorithm and simulated annealing. Azab and ElMaraghyused quadratic assignment for reconfiguring process plans [129].The common problems and characteristics of these CAPP approachesfor machining are one or more of the following:

� Feature recognition is used in most of the approaches. Hence, thefeature-based databases of commercial software are not utilised.� After recognition, the features (mostly design oriented) are

converted into application (manufacturing) features using aknowledge 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, severalmachines 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-levelentities, namely operations.� The set-ups were optimised with respect to the tool approach

directions. This in turn reduces the search space or looses feasibledesign points.

To conclude, process planning research has not as yet reachedthe maturity of key methods to focus on verification and validationin an integrated manner. The feature recognition approach istheoretically the most generic approach to process planning but itpartly negates the design and process standardisation andverification benefits of feature-based design.

5.4. Virtual assembly modelling and simulation

Virtual or digital assembly modelling is a powerful and effective

Fig. 10. Tolerance design methods [99].


technology for the verification of assemblies during the digitaldesign phase. Assembly process planning (APP) is a corecomponent of virtual assembly modelling as it deals with assemblyconstraint identification, equipment selection and sequencegeneration [130]. Wang and Ceglarek [131] proposed an assemblysequence planning method which comprises: (1) sequencegeneration for predetermined line configurations using k-piecemixed-graph representation of assembly; (2) dimensional qualitymodel of variation propagation for assembly processes withcompliant parts; and (3) evaluation of sequences based on themultivariate process capability index.



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Using Virtual Reality (VR), the 3D digital mock-up of theproduct can be manipulated with the assistance of VR interactivedevices. It, therefore, attracted great interest from researchersdealing with assembly planning. The advantages of applyingvirtual engineering for assembly process planning were sum-marised by Jun et al. [132]. From the concurrent engineeringperspective, it is preferable to implement the assembly anddisassembly process in a virtual environment at an early stage ofdesign, when only the geometric forms are determined and thefunctions can still be defined [132,133].

The Virtual Assembly Design Environment (VADE) was createdto demonstrate the potential and the challenges involved in thedesign and manufacturing processes [134]. Fig. 11 illustrates theusage scenario of VADE. The VADE system allows the user toperform assembly processes by hand and assembly tools on thevirtual product with the import data from a parametric CADsystem. By maintaining a dynamic correlation with a CAD system,the design information created during the virtual assembly processis updated at the end of using VADE.

Banerjee et al. [135] studied the effectiveness of VR in assemblyplanning by comparing: blueprints, a non-immersive desktop VRenvironment and an immersive projection-base VR environment.The results showed that the completion time of the assemblyprocess was approximately halved by utilising VR. An AugmentedReality (AR) based human-computer interface was developed byOng et al. [136] to provide an immersive and intuitive environ-ment. Unlike VR, the assembly design and planning using AR can beverified by manipulating the virtual prototypes in the realassembly 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 theadvanced engineering industries, like aerospace and automotive,for the design of assemblies and their integration with the designof tooling and the associated jigs and fixtures. Commercialsoftware systems allow the seamless integration of product,process and resource models [137]. The data generated duringassembly tolerance analysis can be utilised by tool designers todefine appropriate tooling tolerances. Such systems are also beingdeployed within ITER – the nuclear fusion project – to model themanipulation of cassette tooling, the loading of which is robotcontrolled [138]. Additionally, the digital mock-up of tooling cansimulate accessibility issues and lines of sight for an opticalmeasurement system [139].

Digital fixturing is a key enabling technology for low costtooling that will enhance industry’s capability for batch production

Ceglarek [144] proposed a GA and low-discrepancy samptechnique-based optimal design space reduction methodoptimise the locator positions in a multi-station assembly syswhile ensuring the robustness of the fixturing system in termthe product’s dimensional quality.

5.4.2. Stream-of-variation modelling and design synthesis

Stream-of-Variation Analysis (SOVA) is a mathematical moto describe the relation between final product quality and procparameters of complex multistage assembly [145,146]. SOVApredict potential downstream assembly problems, basedevaluations of the design using a large array of process variabBy integrating product and process design in a pre-producsimulation, SOVA can head off individual assembly errorscontribute to an accumulating set of dimensional variations, whultimately result in out-of-tolerance parts and products. Oncthe ramp-up stage of production, SOVA can compare predicmisalignments with actual measurements to determine the degof mismatch in the assemblies and diagnose the root causes oferrors [145,146].

Individual design tasks must be integrated in order to optimthe design of the entire system. Phoomboplab and Ceglarek [1proposed a design synthesis method based on a hybrid desstructure matrix which integrates design tasks with desconfigurations of key control characteristics, especiallydimensional management in multistage assembly systems.method can generate design tasks sequences to minimsimulation time as well as benchmark design task sequenceterms 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 procesnow a vital element of integrated design and manufacturing [1Computer Aided Inspection Planning (CAIP) systems have bdeveloped to accomplish the measurement planning task byfollowing generic procedures: (1) CAD interface and featrecognition, (2) determination of the inspection sequence offeatures of a part, (3) determination of the number of measupoints and their locations, (4) determination of the measupaths, and (5) simulation and verification [149]. The stages of Cfor Co-ordinate Measuring Machines (CMMs), are definedestablish the best sequence of inspection steps, the detainspection procedure of each feature, feature accessibilityprobes, probe path planning and collision checking, generatingCMM control commands, and the post-processing of measudata such as statistical and cost analysis [150].

The first generation of inspection planning systemsdeveloped by Hopp [151] and ElMaraghy and Gu [152]. Autominspection planning for dimensional and geometric inspectionstwo distinguished levels: macro- and micro-level plann[153,154]. Subsequently, Lee et al. [155] divided the plannprocess into two steps: global inspection planning that is focuon the generation of an optimum inspection sequence and linspection planning that is focused on minimizing errors and timthroughout the measurement process.

Research in CAIP falls into two categories: (a) tolerance-driinspection process planning and (b) geometry-based inspec

Fig. 11. The VADE usage scenario [134].

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and customisation of products [140]. As an extension from theestablished methods of rapid prototyping (RP) from a DMU to aphysical mock-up, a range of rapid tooling applications are beingdeveloped [141]. An alternative to rapid tooling is to employreconfigurable tooling; this generally requires modular compo-nents that allow a virtually unlimited number of toolingconfigurations. Ceglarek et al. [142] extended the ‘‘N-2-1’’ fixturelayout design methodology by introducing a movability restraintcondition which is essential for material handling fixture design.Kong and Ceglarek [143] addressed a fixture workspace synthesismethod for reconfigurable assembly systems. Phoomboplab and


process planning [148]. The former considers inspectionsfeatures with allocated tolerance requirements while the laaims to conduct an entire geometry inspection by comparingobtained complete geometric description of a part or product wthe design model. The geometry-based CAIP systems theoreticoffer a more coherent inspection process but at a high timecost [148]. Recent research has been carried out aiming atautomation of the inspection features reorganisation, by extracfrom the CAD model directly. Similar research concerning featclustering, probe accessibility and orientation analysis dominaresearch interest for CMM-based inspection planning carried



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imaiem and ElMaraghy [156], Zhang et al. [157] and Hwangl. [158].

ith the rapid development of artificial intelligence andledge-based techniques, Expert Systems, Neural Networks

Fuzzy Logic were used to automate the measurement planningess. The expert system developed by Moroni et al. [159]les the problem of selecting touch probes and generating thesurement configurations. Lu et al. [160] and Hwang et al. [158]loyed artificial neural network techniques to obtain themum inspection sequence while Beg and Shunmugam,162] achieved the same objective utilizing Fuzzy Logic on a

matic part inspection process. Mohib et al. [163] usedledge rules to select the most appropriate probe type and

mised the planned inspection tasks using a hybrid laser/CMMomplex geometries.

. Metrology process modelling for verification planning

rocess modelling is an essential technology for designuation and process planning based on the codification ofneering knowledge and analytical methods [164,165]. There isrcity of metrology process models for measurement planningthis may be due to the traditional industrial perception of

rology simply being a verification step, rather than being anntial element of the production process [166]. Moreover, neweless metrology systems have been integrated with produc-and assembly, enhancing the need for developing a processel to codify their capabilities [80,81].aropoulos et al. [166] proposed a theoretical framework for

development of metrology process models for integratinguct design with assembly planning, based on the Digitalrprise Technology methodology [167,168]. Fig. 12 shows therology framework, with the metrology process model posi-ed central to the integration of the design verification process

the verification of assembly operations and the subsequentoyment of measurement systems that support measurement-ted automation. The framework explicitly recognises the need-ordinate the digital verification aspects (left part of Fig. 12),those that involve the physical deployment of measurement

pment for product and process verification (right part of12) [166,168].ndustry requires the definition of new research projectsessing the development and evaluation of integrated metrol-

spectrum of physical scale and accuracy requirements for whichsuch systems need to be selected covering industrial productionfrom small parts (measured in millimeters) to large, complexproducts such as aircraft, ships, and wind turbines [166,171,172].New techniques such as absolute length measuring interferometryand six-degrees-of-freedom probes are frequently combined withmore traditional systems such as CMMs to cover the dimensionaland shape verification needs of modern products [171,172]. Theselection process needs to be based on metrology process modelsand employs multiple criteria with a key requirement being thedefinition and minimisation of measurement uncertainty[163,171]. Cai et al. [168,173] proposed an approach for largevolume metrology instruments selection based on measurabilitycharacteristics (MCs) analysis. Inspired by the concept of qualitycharacteristics, MCs can be used for instrument selection on thebasis of measurement capability, cost and technology readiness.Muelaner et al. [174] proposed an approach employing a datafiltering technique for instrument selection and Cuypers et al.[175] specify the task requirements and part restrictions beforeselecting instruments manually.

There are exciting, new research challenges in genericmeasurement systems modelling and capability derivation thatare essential for instrument selection and measurement planningwithin CAIP. Research is also needed for the integration of CAIPwith 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 thecost and time of certification of structural systems has pushed theacademic and industrial world toward the development of VirtualTesting 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 andreplace physical testing by virtual FEA testing, procedures must beput in place to demonstrate that the virtual tests are able toreplicate actual tests and to generate the necessary confidencewithin the design and certification communities.

The first stage of FEA is the ‘‘idealisation process’’ which takesthe real-life structural design problem and turns it into an idealised

Fig. 12. Overview of the theoretical framework for integrating measurement with assembly planning.

Fig. 13. Virtual testing procedure.

and assembly methods and systems that offer superiortional and orientation accuracy, with in-built verificationbility. Such systems must be fully compliant with relevantdards and best practice guides including; ISO GUM [169],E B89.4.19 [170] and STEP (ISO 10303) [24].

. Measurement and inspection equipment selection

vitally important stage in the digital verification planning isidentification and selection of inspection equipment. This

ely refers to measuring systems deployed for dimensional ande validation of parts and assemblies. There is a very wide


mathematical model, the Finite Element Model (FEM). The secondstage involves selecting appropriate finite elements, mesh layoutsand solution algorithms to define the structural behaviour of theidealised mechanical system. The creation of an error-control



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procedure to facilitate the user of the FEA in solving structuraldesign problems has been extensively studied. Other methods forcreating error-free FE models may involve the use of sensitivityanalyses [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 thataffect the accuracy of the virtual testing. Such issues are morepronounced for structures made of newly developed materialssuch as hybrid materials, fibre reinforced plastics (composites) dueto their high dimensional variability of products. This is becomingan important issue for thick large-scale structures wheremeasurement of residual stresses and distortion are challengingtasks. To solve these issues, upstream 3D digital measurement andquality control techniques need to be employed in a synergisticmanner 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 goodpredictions in the elastic regime, they have not previouslyachieved predictive accuracy in the presence of damage andfracture. This limitation is starting to be overcome by newsimulation strategies, which combine advances in the generalityand physical realism of damage formulations with new experi-mental techniques for probing the physics of failure at the micronand nanometer scales. These research advances are makingpossible high-fidelity virtual tests, where the mechanical beha-viour of a structure up to ultimate failure is computed throughsimulations 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 tosubstantial computing resources, computational fluid dynamics(CFD) is now becoming established as a viable tool for computeraided engineering and design, in spite of uncertainties thatcontinue to surround the topics of automated mesh generation,solution sensitivity to mesh size and distribution, and theverification and realism of turbulence models. CFD software offersincreasingly sophisticated (and computationally demanding)analysis features such as free-surface modelling, fluid-structureinteraction (FSI) and large eddy simulation (LES).

The turbomachinery and aircraft industries have made use ofCFD for many years to study flows around smooth-shapedaerodynamic surfaces. Calibrated physical models are used forthese flows using highly structured ‘‘curvilinear’’ (body-fitted)meshes to make best use of available resources. CFD has resulted insignificant improvements to the design of compressor and turbineblades [179], including the use of inverse design and multi-objective optimisation techniques [180], with the attention of theindustry and researchers now turning ever more assiduously toimproving the use of valuable compressor bleed air in gas-turbineinternal-air cooling systems [179,181].

In aircraft design, the requirement to carry out large-scalecomputations of complete aircraft configurations motivated thedevelopment of empirical ‘‘one-equation’’ models of turbulence forcomputational economy [182]. Following a period in whichturbulence models tended to move toward more complicated,multiple-equation closures (such as shear–stress, v2-f or the even

derived from medical imaging). This also lends itself to boundsurface adaptation in response to the flow, a process knownsculpting. Similar modelling of the interface between flexmembranes or solid surfaces and the forces exerted on them bfluid medium is the basis of FSI, where finite element modelcan be integrated with CFD to calculate structural deformatioresponse to varying fluid dynamics loads.

LES offers the prospect of less reliance of solutions on the oincomplete representation of flow physics using turbulemodels. In LES, an unsteady turbulent flow is simulated inthree-dimensional and time-accurate detail, with only the exction of very small-scale (so-called ‘‘sub-grid’’) energy dissipaprocesses. The matching of LES techniques to more traditiomodelling methods in low turbulence research, such as near woffers the prospect of high-fidelity ‘‘numerical experiments’’ beconducted replacing the need for large-scale physical testing.unsteady information provided by the LES technique also leitself naturally to the unsteady aerodynamics of separated flofor example around wind turbine blades or around aircraft at vhigh angles of attack as shown in Fig. 14, as well as providingfluctuating pressure information that is vital for controlunsteady 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 prerequisof rapid product development, physical prototyping techniqwere prevalent in industry and have influenced product permance, quality and competitiveness in the global markets. Phystesting is still an expected industry practice, frequently linkeproduct certification. For example, aerospace products undestrict testing to pass certification criteria and automomanufacturers are required to test their prototypes followcombustion and safety standards. Moreover, physical tesgenerates valuable knowledge and data that can be utilisedenhance the design of future products or variants.

6.1.1. Dimensional and shape verification and validation

Component verification is the process of assessingconformance of key features and characteristics of a manufactucomponent to the specifications prescribed by the proddesigners, as these are captured by the GD&T notations.scope of this paper is according to the GPS standard [186],prescribes the surface, geometric and dimensional characteris

Fig. 14. Isosurface of instantaneous vorticity over an F-18C aircraft at 308 ang

attack [185].

Fig. 15. Dimensional and shape characteristics of GPS standards [186].

more substantial Reynolds–Stress models), the robustness andrelative economy of one-equation models, such as Spalart andAllmaras [182], is enjoying a return to more widespread favour,and developments of such models to account for more complicatedflow situations are now being proposed and introduced [183].

An important issue with the handling of complex geometriessuch as car body surfaces is the efficient translation from solidmodel geometry (CAD) representations into a form suitable forautomated mesh generation for CFD. Dawes [184] proposes atightly integrated approach in which a pre-defined mesh also actsas the surface geometry detection mechanism (using algorithms


involved in verification, as shown in Fig. 15.



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esigners define tolerances on core models that are intended toribe the maximum allowable variation from the nominal size.rances do not include any allowance for, or knowledge of, thesurement uncertainty of the equipment used to verifydimensions. The standard ISO 14253 [187] makes it clear thatonus is on the supplier of the measurement data to guaranteeconformance to specification (tolerance) of the measurements,that the data takes account of measurement uncertainty.here are several ways of carrying out dimensional and shape

fication [171] including direct or indirect measurements, andsuring either all the parts (100% inspection) or a selection ofs. Direct measurements are taken off the part itself by deployingrology systems suitable for the physical size and scale of thefacts and these systems are outlined in the enabling technologiesion 6.4. Indirect dimensional verification requires taking inferredensions from something other than the part, for example bysuring the jig that is used to assemble the part. Verification maybe inferred statistically through controlling and measuring theess, as outlined in Section 6.3, and this can bring significant costfits through improvements to process capability.he level of inspection required for any given feature is dictated

he risk of non-conformance. Depending on the industry sector,gn risk is driven by performance, safety and fit. Process andection risks are dictated by the capability of the process andection systems. Due to the criticality of aerospace components,-risk features will always be subject to 100% inspection.ures that can be effectively controlled by validating theufacturing process can be subjected to a reduced inspection

me, typically yielding a 50–75% reduction in final inspection, reducing measurement time per part.

freeform surface, also known as a complex or sculptedace, is classified in ISO 17450-1:2007 [186] as a complexure with no invariance degree. Existing technologies forsuring free form surfaces are detailed by Savio et al. [188].ogrammetry and laser scanning are mature technologies for

ace characterisation with measurement accuracies of 5 parts in[189] and 1 part in 104 respectively. Structured light devicesless mature technologies with accuracy 1 part in 105 but they

potential for achieving higher accuracy than laser lineners due to the fundamental limits imposed by speckle effects,191]. This is where a hybrid system [163] would bentageous. While the ISO GPS standard allows profile

rances on freeform surfaces like straightness [192], roundness] and cylindricity [194], there is no standard for the

fication of freeform surfaces. Multiple instruments are applic-for surface verification, as shown in Fig. 16.

he production uncertainties of a free form surface, com-nded by the edge trimming and the assembly processes thatform surfaces typically are involved in, eventually manifest

selves in gaps, steps and interferences between the surfaces.and flush problems on a fluid dynamic device, such as anaft wing, are detrimental to its performance and the fit ofmotive panels is indicative of the build quality of the product.

The assembly methods used to minimise freeform surface interfaceproblems can be classified as follows;

� Build to nominal: the assembled product tolerance is met bysimply making the key features of the parts as accurately aspossible. Typically used for small products with features that canbe accurately produced.� Measure and adjust: the assembled product tolerance is met by

measuring the interfaces and adjusting some of the parts’position and/or orientation to minimise interface problems. Forlarger parts which can be difficult and expensive to produce totight tolerances (such as door panels in the automotive industry),the position and orientation may be manipulated manually orautomatically 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 otherside using the measured data. For very large freeform shapessuch as wings and wind turbine blades, it is very difficult andexpensive to produce parts to tight tolerances. It is oftenpreferable to tailor parts to fit the specific physical assembly byproducing 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 easilyprovide line-of-sight access, as occurs commonly in clutteredassembly environments and complex and enclosed products.Measurement of these features generally requires an ability to ‘‘seearound 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, oncomponents hidden by other components); and six-degrees-of-freedom probing. A key issue with networks of line-of-sightinstruments is closing the metrological loop and includingsufficient common points from one instrument to the next, soas to minimise error buildup.

6.1.3. Measurement equipment deployment

Production metrology begins with the set-up of systems andcontinues through the in-process measurement and metrologyenabled automation [80,81]. Metrology must be seen as amanufacturing process and Muelaner et al. [174] developed amethod for measurement planning and instrument deployment.

Specification of the environmental conditions in which themeasurement is to be carried out should include the averagetemperature, temperature gradients, pressure, humidity andcarbon dioxide content [197]. Accuracy, properly defined asmeasurement uncertainty [169], is a key performance indicatorfor metrology. Much work has already been carried out to modelmeasurement 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 measurementsystems, such as laser trackers and laser radar [170,197]. Co-ordinate measurements may be calculated from a number ofangular measurements obtained using cameras, theodolites, andiGPS [198]. Calculating the measurement is a complex task, sincemeasurement uncertainty impacts on part rejection rates[173,174] and the accuracy of manufacturing processes.

Decision rules for proving conformance or non-conformancewith 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 upperspecification limit (USL) while a measurement result must beaccompanied by an estimate of measurement uncertainty (U)[169]. Product conformance can be proven by a measurementresult 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 astructural component is predicated on key stages of developmentwhich are summarised in Fig. 17. As already described in Section5.6.1, the output of FEA modelling depends on the construction ofaccurate meshed or meshless continua and the correct assignmentof materials properties. In many cases such materials propertyinformation is available from materials textbooks [200] or in theform of software [201] but if new materials or bespoke compositematerials are to be used, materials evaluation is needed to definemechanical properties.

Using a range of test coupon geometries, materials evaluationperforms the dual role of firstly confirming the correct selection ofmaterials and secondly providing materials properties for FEmodelling. Mechanical tests are published by standards bodies suchas ASTM International and BSI British Standards. The mechanicaltesting of fibre composites is given by Hodgkinson [202]. Somematerials parameters and materials tests are given in Table 2.

Materials tests will determine elastic properties and the onsetof yield and will determine whether a linear or a non-linear FE

model is required to model the mechanical behaviour of partkey feature of the measurement of materials parameters iseffective use of instrumentation. Strain measurement devices sas strain gauges, extensometers and lasers are well knowntechniques such as Electronic Speckle Pattern Interferom(ESPI), Holographic Interferometry and Digital Image Correla(DIC) [203] provide more accurate 2D and 3D information on stdistributions around stress concentrations.

An obvious method of evaluating products and componentto perform static structural tests in tension, compression and shto destruction. Performance under cyclic load (fatigue), consstress (creep) and constant strain (stress relaxation) will allowdetermination of parameters such as fatigue life (consamplitude and complex load or strain), fatigue limit, crcompliance and stress relaxation modulus. The observationunderstanding of fracture is achieved by the application of optelectron and atomic force microscopy. Non-destructive evalua(NDE) includes a plethora of techniques, often used to locdefects. Some NDE methods are summarised in Table 3.

6.2.2. Flow related physical verification and validation

The validation of CFD analysis deals with the assessmentcomparison between computational and experimental res[14,204] as shown in Fig. 18 and this generates valuable dataimproving the convergence of Large Eddy Simulationexperimental tests. The key parameters in CFD validation tdeal with the aerodynamic forces that consist of three focomponents (lift, drag, side force) and three moments (pitchyawing, rolling). The static aerodynamic forces and momentsbe measured indirectly by integrating the surface pressdistribution [204] or directly by strain gauge balance, intespring balance and load cell. The unsteady aerodynamic forcesmoments acting on a maneuvering air vehicle [205] canmeasured by using strain gauge balance and load cell.

The external flow structure of an air vehicle can be illustraqualitatively by flow pattern images and quantitativelymeasuring flow velocities. Qualitative flow patterns can

Table 2Selected 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.

Young’s modulus, stiffness E, cij (GPa) Tension, compression,

flexure, etc.

Dynamic stiffness Edyn (GPa) Vibration, time of flight

Shear strength t (MPa) Torsion, shear, tension

Table 3Non-destructive evaluation techniques.

NDE method Principle of operation

Acoustic emission Detection of stress waves from defects in

materials

C-scan Ultrasonic detection of sub-surface defects

Eddy current Monitoring of metallic structures under a

magnetic field

Dye penetrant Colour change of dyes in cracks based on

capillary action

Infrared thermography IR camera measures thermal profile of structu

Photothermal imaging Pulsed light generates radiation from

sub-surface defects

Laser vibrometry Laser beam Doppler shift detects vibrations

and defects

Shearography Sheared laser-generated image acts as a

reference image of a surface. Application of loa

or heat reveals defects

Acoustography Ultrasonic imaging process

Fig. 17. Mechanical design, verification and validation of products.

Shear–strain g Torsion, shear, tension

Shear modulus, stiffness G, cij (GPa) Torsion, shear, tension

Elastic compliance Sij (m2 N�1) All of the above

Poisson’s ratio nij Tension, compression

Work of fracture gf (J m�2) Pendulum and drop

impact

Critical strain energy release rate Gc (J m�2) Fracture mechanics

tests

Critical stress intensity factor Kc (Pa m1/2) Fracture mechanics

tests

Thermal expansion coefficient a (K�1) Dilatometer

Glass transition temperature Tg (K) DSC, DMTAFig. 18. Flow validation process [14].



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ined by using flow visualisation techniques such as; lighttering particles, dye visualisation, smoke wire, tuft-gridhod and oil-film method. The Laser-Induced Fluorescentnique can visualise the flow pattern in a 2D plane of a 3Dfield [206]. Quantitative data of the flow structures can be

ined by measuring flow velocities using pitot tubes (onecity) and five hole probes (three velocities) for steady velocitysurement at one point. Fluctuating velocities can be measuredusing thermal anemometers (intrusive) and laser dopplercimetry (non-intrusive). Particle tracking velocimetry andicle image velocimetry are capable of obtaining velocityrmation on a 2D plane and volumetric three-componentcimetry has been applied successfully in capturing the wholemetric flow information [207,208].

Physical process verification and validation

he formal manufacturing process verification involves thees of inspection, analysis, testing and demonstration. Processation is a means of ensuring that manufacturing processes areble of consistently producing a finished product of theired quality and it typically involves the following formal

hods; fault inspection, dependability analysis, hazard analysis,oducibility analysis and risk analysis [11]. Process validation isucted in the context of a system including design control,ity assurance, process control, and corrective and preventiven [19].

. Statistical process control and Taguchi’s robust design

ithin the field of statistical process control (SPC), a largeber of techniques [209] have become established with the goalproving the quality of manufactured products through the

ction of variability. SPC uses empirical evidence and statisticalysis to identify quality problems. All processes contain someoidable random variability with random causes referred to in

as chance causes. Avoidable sources of variability such as faultsachinery, operator errors or defects in materials are referred to

ssignable causes. A primary objective in SPC is to detect whereesses are out of statistical control so that assignable causes can

dentified and eliminated.aguchi’s robust design objective is to reduce the outputation from the target by reducing the sensitivity to noise, suchanufacturing variations and deterioration over time [210]. Theoach uses the ‘‘loss model’’ because it actually fits a losssure in a signal-to-noise (S/N) ratio format. The idea is toimise S/N through design of experiments. The focus is toease the robustness of the system’s performance.

. Six sigma and root cause analysis

eveloped at Motorola in the early 1980s, 6-sigma is a businessess methodology that enhances customer satisfaction fromucts or services by improving manufacturing processes [211].gn for Six Sigma (DFSS) is a methodology utilizing tools,ing and measurements to enable the design of products andesses that meet customer needs and can be produced at sixa quality levels [87,212].o control dimensional variations during manufacturing,ient six sigma fault root cause diagnosis is critical forroving the quality and productivity of processes [144,213].

follows normal distributions. However, these approaches areinsufficient in the case of an ill-conditioned system. An EnhancedPiecewise Least Squares approach was proposed by Ceglarek et al.[219] to diagnose the six sigma root causes associated withproduct variation.

6.4. Enabling verification technologies

The physical scale and shape of the component and theaccuracy of the required measurement tasks are key determinantfactors for the selection of verification methods and technologies.Fig. 19 shows a classification of digitisation methods fordimensional verification and validation.

Broadly speaking, contact methods are suitable for small tomedium size components, of <1 m3 volume, while non-contactmethods can be applied for much larger parts. There is merit incombining both contact and non-contact methods in one hybridmeasurement system as demonstrated by Mohib et al. [163]. Overthe past ten years there has been a rapid growth of large volumemetrology systems that can deploy contact or non-contactmethods. Another classification of these systems relates to theirconfiguration [220]; centralised systems have one main unit (suchas a laser tracker), while distributed systems have more than oneunit (such as the infrared GPS) that work together for measure-ment of the same point. The result of any measurement isinevitably affected by a number of systematic and non-systematicerrors which contribute to the overall value of measurementuncertainty as shown in Fig. 20. Therefore, regardless of the scale,the dimensional measurement results need to be accompanied bythe statement of uncertainty as defined by GUM [169].

7. Verification of systems and networks

The design of manufacturing systems is carried out usingcriteria related to flow of materials and values of quality, cost anddelivery, as dictated by just-in-time methods. Such considerationsare beyond the scope of this paper that focuses on the use ofdiscrete event simulation (DES) and radio frequency identification(RFID) for manufacturing system verification.

7.1. Discrete event modelling and simulation

Manufacturing systems are designed by considering a variety ofparameters such as material flow, resource allocation andutilisation that define performance within the factory and the

Fig. 19. Digitisation methods for dimensional verification.

Fig. 20. Contributing factors to measurement uncertainty [223].

arek and Shi [214] proposed a diagnostic approach involvingle faults in a single assembly fixture and this work wasnded by Ding et al. [215], using the state space modellingnique. In order to overcome problems related to an ill-itioned system, Rong et al. [216] have proposed unrotated

ular Value Decomposition and matrix partitioning techniques.and Hu [217] proposed designated component analysis forensional fault diagnosis by pre-defining a set of fault patternsd designated components. Apley and Lee [218] proposedpendent component analysis to model the fault variationern with the assumption that no more than one error source



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supply chain. DES is widely used for the design of systems. DESallows the dynamics of complex manufacturing systems to beverified without physical implementation. Simulation is identifiedas the second most widely used technique in the field of operationsmanagement after modelling [221]. DES utilises a multi-statemathematical model of the system where events happen in achronological sequence, are instantaneous and change the state ofthe system [222].

DES environments with 3D capabilities have been developedleading to the concept of the ‘‘digital factory’’ [224]. The resultsfrom continuing research into the field of DES have beenimplemented in industry to the extent where almost every majormanufacturing enterprise uses this technique to verify facilityconfiguration, throughput times, material inventory issues andlogistics in the digital design phase, or to evaluate improvementideas before their physical deployment. Johansson et al. [225]conducted a survey and reported the need to improve thecoherence and reliability of data provided via DES to enhancetheir use by industry for verification and validation of activities.

7.2. RFID methods for the verification of production logistics

RFID technology uses tags for responding to radio frequency(RF) signals by transmitting a constituent data, readers for sendingand receiving RF signals and software to process data. RFIDs haveseen rapid adoption in the manufacturing, service and logisticsindustries and this section outlines the use of RFID technology forsystem design verification and validation. RFID sensors are aneffective means of collecting and processing real-time data frommanufactured parts, products, processes and resources, thuscreating a traceable, real-time view of the production systemand the supply chain, allowing the verification of productionschedules and logistics [226].

For modelling large/complex systems, DES systems requiremodelling assumptions regarding the behaviour of elements of thesystem (statistical distributions, etc) and the inputting of a largeamount of data. The quality of DES system output is a function ofthe correctness of these assumptions and input data. The use ofRFIDs in conjunction with DES can dramatically improve thequality of DES decision-making by the provision of verified inputdata regarding key behaviours of the real system.

Due to the quality and quantity of real-time RFID data, there isextensive potential to utilise such data for the active adaptationand reconfiguration of a system as reported by Huang et al. [227]and the creation of wireless kanbans with embedded RFIDs asoutlined by Zhang et al. [228]. RFID technology finds widespreadexploitation in supply chain management and logistics forimproving decision responsiveness and reducing supply chaincost via the provision of verified data [229,230].

7.2.1. Managing information loss in product manufacture

Jun at al. proposed a framework for the utilisation of RFIDs inthe product lifecycle [231]. Here the main hypothesis is that duringthe digital phases of design and planning, the data and knowledgeis usually captured and codified at acceptable levels usingcommercial CAx systems. As product development transits fromthe digital to physical phase the information flow become less andless complete and the wideranging applications of RFIDs canenhance the information capture and utilisation during product

common themes. Stark [233] introduces PLM by stating that‘‘the activity of managing a company’s products all the way actheir lifecycles in the most effective way’’. Ameri and Dutta [2evolved the definition further by arguing that PLM is a ‘‘knowlemanagement solution which supports processes throughoutproduct lifecycle within the extended enterprise’’. AbramoviciSieg [235] published details of a major PLM survey in whichfindings included the maturity of PLM interaces with CAD andcorresponding maturity in capturing product design data as shoin Fig. 21. The trend clearly demonstrates the consideraprospects available for improving verification during the physstages of product lifecycle, by improving the rate of capturingre-using relevant data using PLM. As reported by Jun et al. [2the increasing use of RFIDs will impact positively on PLM dcompleteness.

As lifecycle management covers the complete period frproduct concept definition to disposal, it generates a compelcontext in which to analyse the sharing and exchange of dbetween the plethora of CAx systems and the impact of respecstandards [236] as shown in Fig. 22. It can be seen from Fig. 22STEP has a dominant position in terms of PLM data exchangePeak et al. [237] and Ming et al. [238] argue that XML and Ubased STEP are promising technologies for improving Pinteroperability. Despite all the activity in developing ostandards, Gielingh [239] points out that the uptake of ostandards, in general, has been very poor and that one of the mreasons is that meaning is often lost in data translation. Thiskey research area for PLM.

8.2. Verification and validation of complex products in the contex

the lifecycle

Complex engineering products, like automobiles and commcial aircrafts, require a set of verification and validation stagessatisfy respective legislative requirements governing their usethe increasingly demanding nature of customer aspirationswithin a cost competitive package. In addition, the produthemselves are highly complex and designed by large engineeteams spread across many countries and organisations – facthat, when combined with the exacting requirements, necessia formal and robust design and development methodologyterms of verification be employed.

Fig. 21. A conceptual framework for PLM strategy development [235].

Fig. 22. Current standards and their coverage [236].

manufacture, product service and recycling [231]. This ability canarguably enhance the design of production systems and networksand improve new product designs by the utilisation of service data.

8. Methods for the lifecycle verification of complex products

8.1. Enabling technologies and standards for product lifecycle

management

There are many definitions of Product Lifecycle Management(PLM). While no single definition has emerged [232], there are



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he V model of verifying product development, as shown in23, is capturing the aerospace recommended practice for thelopment of civil aircraft and systems according to ARP4754adard [20]. Broadly speaking, the left side of the V model showstop-down requirements development and validation starting

the product and cascading down to systems and discretes, the design of which corresponds to the very bottom of the Vel. The right side of the V model represents the bottom upess of verification that starts by verifying the design of discretes by evaluating whether the respective requirements havemet, and proceeding with the verification of systems and the

plete aircraft [20].he same verification process has been adopted for thelopment of complex automotive systems, like power-trains.gnificant common aspect is the verification of functionalirements as captured by QFD. For complex systems, QFD needse augmentation where the customer responses are complex, ase subjective assessment of vehicle acceleration. Pickering ande [240] describe an automated method to analyse data fromeability tests of existing vehicles in order to generateelations between the subjective driver ratings and objective

data. This allows objective assessments of power-trainormance to ensure that requirements are fulfilled. Formercial aircraft, the process depicted in Fig. 23 has durationveral years and involves the use of a raft of engineering andare design systems and methods with the process being

aged using PLM systems across the enterprise. For instance,verification of stress requirements will involve use of FEA atlevel and the flow performance will be verified using CFD and

as outlined in Sections 5.6.1 and 5.6.2 respectively.

ey future requirements and trends

PLM and international standards

ne of the major trends in PLM will be to attempt to builduct and process knowledge earlier in the product lifecycle;is termed as ‘‘frontloading’’. In Srinivasan’s review [35] of

dards for product geometry specification, verification, andange it was observed that standards have developed rapidly

e the advent of the digital enterprise. However, some of thedards, especially the open standards, have poor uptake withinstry [239]. Furthermore, Zheng et al. [73] found that a keyrity is to provide feedback to ‘‘close the gap’’ between thesical and digital world in the context of PLM. Integration iscted to become easier over time with the increasing emphasis

verified GD&T [166,168,173]. Other trends include the expansionof Design for ‘X’ to include measurability and the application ofnew design guidelines [166]. These are examples of ‘‘frontloading’’by building in measurement process knowledge early into thelifecycle.

Measurement uncertainty is being measured, but not usedadequately. Recently, there have been considerable efforts inevaluating the uncertainty of different measurement techniques[241], and software tools are emerging to allow predictions ofmeasurement uncertainty to be made [242]. However, typicallythis information is only used within ‘islands’ of automatedinspection processes. Measurement uncertainty is expected tobe accounted for early in the product lifecycle. Research inimproving measurement simulation is ongoing to make these kindof environments easier for designers to use [243].

9.3. Verification modelling and planning

Metrology is integral to manufacturing processes, but itsdevelopment in terms of measurement modelling and planningis embryonic when compared to processes and additional researchis needed in metrology process modelling [166]. In the modernproduction environment, metrology is becoming tightly integratedwith the manufacturing processes and such integration canprovide valuable information about process capability andimprove the design of future products [81]. For example, newmeasurement techniques allow many devices to be taken to thepart [171], on-machine measurement is more common [241], ormeasurement is used to facilitate assembly [80]. However,measurement and manufacturing process planning are still notsufficiently interlinked [166,241] and considerable verificationbenefits will arise from their integration.

9.4. Early design verification in the digital domain

A key future trend is the requirement for early designverification. It is well documented that early design phasesaccount for a large percentage of lifecycle costs. This is especiallytrue for complex engineering products and for such applicationsthe early verification of components and the correspondingfunctional verification of systems are critically important tasks.The challenges are significant, including; methods to deal withverification using low design data-intensity, enhance the scope offunctional verification with the development of integratedfunctional mock-up, and techniques for integrated product andprocess verification.

Fig. 23. The V model for the verification of complex engineering products (adapted from Refs. [20,240]).

pen standards for data exchange.

GD&T and measurement uncertainty

he use of GD&T is widespread in industry [35], but is notsted for measurability. Although it is normal for manufactur-process capability to be considered during the design stages,GD&T that is applied rarely takes account of measurementesses and their capabilities. Research is ongoing to address thises, including measurement instrument selection that is carriedfrom early design, allowing the setting of measurability-


10. Concluding comments

This paper analysed methods and techniques for designverification and validation, especially focusing on mechanicalengineering products of meso to large-scale, and the correspond-ing manufacturing processes. There is clear evidence that digitaldomain design verification and validation is a high industrialpriority and there is evident research focus in such methods, aswell as considerable coverage via international standards. Physicalproduct and process verification and validation remain important



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requirements, especially for complex products that requirecertification, such as aerospace.

There is a gradual, but clear development of new measurement,inspection and verification modelling and planning methods, tounderpin design verification both at the digital phase and thephysical testing of products and processes. Such methods areunderpinned by new enabling technologies and the trend for theintegration of metrology with production processes.

The development of enhanced PLM capabilities, in terms ofcodifying and capturing post-design verification data and knowl-edge, will be vitally important for the successful adoption andimplementation of new design verification and validation methodsby manufacturing industry.

Acknowledgements

The authors would like to gratefully acknowledge the supportand contribution of many colleagues, from CIRP and otheracademics, in the development of this paper. Contributions werereceived from; Prof Luc Mathieu, Prof Luc Laperriere, Prof HodaElMaraghy, Prof Torsten Kjellberg, Prof Robert Wilhelm, ProfGunnar Sohlenius, Prof Stephen Newman, Prof Rainer Stark, DrAydin Nassehi, Dr Martin Ansell, Dr Michele Meo, Dr MichaelWilson, Dr Alicia Kim, Dr Zhijin Wang and Dr Chris Brace. Last, butnot least, we would like to note that we are especially grateful to DrParag Vichare, from the University of Bath, whose contribution inrelation to the preparation of this keynote paper has beenoutstanding.

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