DET 2011 7TH INTERNATIONALCONFERENCEONDIGITAL ENTERPRISE TECHNOLOGY 28 - 30 September 2011 Athens Hosted by:Laboratory for Manufacturing Systems and Automation Director Professor G. Chryssolouris Department of Mechanical Engineering and Aeronautics University of Patras GREECE 7TH INTERNATIONAL CONFERENCE ONDIGITAL ENTERPRISE TECHNOLOGY PROCEEDINGS OF THE 7TH DET 2011 28 - 30 SEPTEMBER 2011 - ATHENS ed. by:Professor G. Chryssolouris Professor D. Mourtzis

This work is subjected to copyright. All rights are reserved, whether the wholeor part of the material isconcerned,specificallythoseoftranslation,reprinting,reuseofillustrations,broadcasting, reproduction by photocopying machine or similar means and storage in data banks. Preface Dear Colleagues: Welcometothe7thDigitalEnterpriseTechnologyInternationalConference(7thDET2011).Digital Enterprise Technology (DET) can be defined asthe collection of systems and methods forthedigitalmodelingandanalysisoftheglobalproductdevelopmentandrealization process,inthecontextoflifecyclemanagement.TheaimofDET2011istoprovidean internationalforumfortheexchangeofleadingedgescientificknowledgeandindustrial experience. Anumberoftechnicalpapersarepresentedaddressingawidevarietyoftopics,including EnterpriseModelingandIntegrationTechnologies,ManufacturingSystemsandProcesses Simulation, Enterprise Resource Planning, Supply ChainManagement, Digital Factory, Real-timeDecisionMakingandDecisionSupportSystems,ComplexSystemModelingand Analysis,e-Businessande-Commerce,LeanProductionandAgileManufacturing,Flexible andReconfigurableManufacturing,ConcurrentEngineering,LogisticsandManufacturing Data Management, Virtual Reality andManufacturing, Web Services and Manufacturing, Life CycleDesignandManufacturing,EnergyefficientandGreenmanufacturingprocesses, Environmentallysustainableproductionsystems,CollaborativeManufacturingand Engineering,RapidManufacturing,ReverseEngineering,AdvancedMetrology,Engineering Education and Training We wish to thank all of you for your participation, and we hope that you find this conference to be an enriching experience. Professor G. Chryssolouris Professor D. Mourtzis Laboratory for Manufacturing Systems and Automation (LMS) Chairs, 7th DET 2011 7th DET 2011 Committees CHRYSSOLOURIS GLMS / University of Patras, Greece Conference Chairman MAROPOULOS P.University of Bath Conference Series Chairman MOURTZIS D.,LMS / University of Patras, GreeceConference Designated Chairman International Program Committee (IPC) BERNARD A.Ecole Centrale de Nantes, France BOER C.ICIMSISUPSI, SwitzerlandBYRNE G.University College Dublin, Ireland CARPANZANO E.ITIA-CNR, Italy CUNHA P.Instituto Politecnico de Setubal, Portugal DENKENA B.Leibniz, Universitaet Hannover, Germany DUFLOU J.Katholieke Universiteit Leuven, Belgium DUMUR D.Ecole Superieure d Electricite, France FISCHER A.TECHNION, Israel GALANTUCCI L.Politecnico di Bari, Italy HUANG, G. Q.University of Hong Kong INASAKI I.Chubu University, Japan JOVANE F.Politecnico di Milano, Italy KIMURA F.Hosei University, Japan KJELLBERG T. Royal Institute of Technology, Sweden KOREN Y.University of Michigan, USA KUMARA S.Pennsylvania State University, USA LEVY G.Inspire AG, Switzerland MAROPOULOS P.University of Bath MONOSTORI L.Hungarian Academy of Sciences, Hungary NEE A. National University of Singapore, Singapore ROY R. Cranfield University, UK SANTOCHI M.University of Pisa, Italy SCHOLZ-REITER B.University of Bremen, Germany SCHOENSLEBEN P.ETH Zurich, Switzerland SELIGER G.Fraunhofer-Berlin, Germany SHPITALNI M.TECHNION, Israel SUH P. N.KAIST, Korea SUTHERLAND J.Purdue University, USA TETI R. University of Naples Federico II, Italy TICHKIEWITCH S.Laboratoire G-SCOP, France TOLIO T.ITIA-CNR, Italy TOMIYAMA T.Delft University of Technology, Netherlands TSENG M.Hong Kong University of Science and Technology, Hong Kong VAN BRUSSEL H.Katholieke Universiteit Leuven, Belgium VAN HOUTEN F.University of Twente, Netherlands WERTHEIM R.Fraunhofer IWU, Germany WESTKAEMPER E.University of Stuttgart, Germany National Organizing Committee (NOC) MOURTZIS D. LMS / University of Patras, GreeceChairman MAKRIS S.LMS / University of Patras, Greece MAVRIKIOS D.LMS / University of Patras, Greece MICHALOS G.LMS / University of Patras, Greece PANDREMENOS J.LMS / University of Patras, Greece PAPAKOSTAS N.LMS / University of Patras, Greece SALONITIS K.LMS / University of Patras, Greece STAVROPOULOS P.LMS / University of Patras, Greece Table of Contents P01: Economical Analysis for Investment on Measuring Systems M. X. Zhang, P.G. Maropoulos, J. Jamshidi, N. B. Orchard 01 P02: Measurement Assisted Assembly and the Roadmap to Part-to-Part Assembly J. Muelaner, O.Martin, A.Kayani, P.G. Maropoulos 11 P03: Digital Factory Economics Volkmann, J.; C.Constantinescu 20 P04: CORBA Based architecture for feature based Design WITH ISO Standard 10303 Part 224 D. Kretz, J. Militzer, T. Neumann, C. Soika, T. Teich 29 P05: Integrated Dimensional Variation Management in the Digital Factory J E Muelaner, P.G. Maropoulos 39 P06: Integrated Large Volume Metrology Assisted Machine Tool Positioning Zheng Wang, P.G. Maropoulos 47 P07: HSC Machining Centre Basic Errors Prediction For Accuracy Control J. Jedrzejewski, W. Kwasny 57 P08: Decision-making for Metrology System Selection based on Failure Knowledge Management Wei Dai, Xi Zhang, P.Maropoulos, Xiaoqing Tang 65 P09: Comprehensive Support of Technical Diagnosis by Means of Web Technologies M. Michl, J. Franke,. C. Fischer, J. Merhof 73 P10: Metrology enhanced tooling for aerospace (META): A live fixturing Wing Box assembly case study O. C. Martin, J. Muelaner, Z. Wang, A. Kayani, D. Tomlinson, P. G. Maropoulos, P. Helgasson 83 P11: Planning Software as a Service - A new approach for holistic and participative production planning processes R.Moch, E.Mller 93 P12: Beyond the planning cascade: Harmonized planning in vehicle production S. Auer, W. Mayrhofer, W. Sihn 101 P13: Dynamic wavelet neural network model for forecasting returns of SHFE copper futures price Li Shi, L. K. Chu, Yuhua Chen 109 P14: A novel tool for the design and simulation of Business Process Models J. Pandremenos, K. Alexopoulos, G. Chryssolouris 117 P15: Virtual Reality enhanced Manufacturing Systems Design 125 Xiang Yang, R. Malak, C. Lauer, C. Weidig, B. Hamann, H. Hagen, J. C. Aurich P16: Real Options Model for Valuating China Greentech Investments Qin Han, L. K. Chu 134 P17: User - Assisted Evaluation of Tool Path Quality for complex milling processesC. Brecher , W. Lohse 142 P18: Implementation of kinematic mechanism data exchange based on STEP Yujiang Li, M. Hedlind, T. Kjellberg 152 P19: A framework of an energy-informed machining system Tao Peng, X. Xu 160 P20: Development of a STEP-based collaborative product data exchange environment Xi Vincent Wang, X. Xu 170 P21: Life cycle oriented evaluation of flexibility in investment decisions for automated assembly systems A. Kampker, P. Burggrf, C. W. Potente, G. Petersohn 178 P22: The role of simulation in digital manufacturing applications and outlook D.Mourtzis, N.Papakostas, D.Mavrikios, S.Makris, K.Alexopoulos 187 P23: A software concept for process chain simulation in micro production Bernd Scholz-Reiter. J. Jacobi, M. Lutjen 204 P24: An Inventory and Capacity-Oriented Production Control Concept for the Shop Floor based on Artificial Neural Networks B. Scholz-Reiter, F. Harjes, O. Stasch, J.Mansfeld 213 P25: A multi-agent-enabled evolutionary approach to supply chain strategy formulation Ray Wu, David Zhang 221 P26: Development of an assembly sequence planning system based on assembly features Hong Seok Park, Yong Qiang Wang 227 P27: Multi-objective Optimization for the Successive Manufacturing Processes of the Paper Supply Chain M. M. Malik, J. Taplin, M. Qiu 237 P28: Performance of 3-D Textured Micro- Thrust Bearings with Manufacturing Errors A.G. Haritopoulos, E.E.Efstathiou, C.I. Papadopoulos, P.G. Nikolakopoulos, L. Kaiktsis 247 P29: A multilevelreconfiguration concept to enable versatile production in distributed manufacturing S. Minhas, M. Halbauer, U. Berger 257 P30: Virtual Factory Manager of Semantic Data G. Ghielmini, P. Pedrazzoli, D. Rovere, M. Sacco, C.R. Bor, W.Terkaj, G.Dalmaso, F.Milella 268 P31: Agile manufacturing systems with flexible assembly processes S. Dransfeld, K. Martinsen, H. Raabe 278 P32: A virtual factory tool to enhance the integrated design of production lines R. Hints, M. Vanca, W. Terkaj, E.D. Marra, S. Temperini, D. Banabic 289 P33: Methodology for monitoring and managing the abnormal situation (event) in non-hierarchical business network A. Shamsuzzoha, S. Rintala, T. Kankaanp, L. Carneiro, P. Ferreira P. Cunha 299 P34: Implementation and Enhancement of a Graphical Modelling Languages for Factory Engineering and Design C. Constantinescu, G. Riexinger 307 P35: Approach for the development of logistics enablers for changeability in global value chain networks B. Scholz-Reiter, S. Schukraft, M.E. zsahin 315 P36: Automation of the three-dimensional scanning process based on data obtained from photogrammetric measurement R Konieczny, A. Riel, M. Kowalski,W. Kuczko, D. Grajewski 322 P37: Recommending engineering knowledge in the product development process with Shape Memory Technology R. Thei, T. Sadek, S. Langbein 330 P38: Kinematic structure representation of products and manufacturing resources M. Hedlind, Yujiang Li, T. Kjellberg, L. Klein 340 P39: Multi-agent-based real-time scheduling MODEL for RFID-enabled ubiquitous shop floor T. Qu, George Q. Huang, YF Zhang, S.Sun 348 P40: Frequency Mapping for Robust and Stable Production Systems R. Schmitt, S. Stiller 356 P41: An automated business process optimisation framework for the development of re-configurable business processes: a web services approach A. Tiwari, C. Turner, A. Alechnovic, K. Vergidis 363 P42: Virtual Rapid Prototyping Machine E. Pajak, F. Gorski, R. Wichniarek, P. Zawadzki 373 P43: Manufacturing systems complexity an assessment of performance indicators unpredictability K. Efthymiou, A. Pagoropoulos, N. Papakostas, D. Mourtzis, G. Chryssolouris 383 P44: A Fuzzy Criticality Assessment System of Process Equipment for Optimized Maintenance Management Qi H.S., N. Alzaabi, S. Wood, M. Jani 392 P45: Realising the Open Virtual Commissioning of Modular Automation Systems Xiangjun Kong, B. Ahmad, R. Harrison, Atul Jain, L. Lee, Y.Park 402 P46: Web-DPP: An Adaptive Approach to Planning and Monitoring of job-shop machining operationsLihui Wang, Mohammad Givehchi 411 P47: Simulation Aided Development of Alternatives for Improved Maintenance Network A. Azwan, A. Rahman, P. Bilge, G. Seliger 421 P48: A framework for performance management in collaborative manufacturing networks P. S. Ferreira, P. F. Cunha 430 P49: Risk management in early stage of product life cycle by relying on Risk in Early Design (RED) methodology and using Multi-Agent System (MAS) L. Sadeghi,M. Sadeghi 440 P50: Challenges in digital feedback of through-life engineering service knowledge to product design and manufacture T. Masood, R. Roy, A. Harrison, S. Gregson, Y. Xu, C. Reeve 447 P51: A digital decision making framework integrating design attributes, knowledge and uncertainty in aerospace sector T. Masood, J.Erkoyuncu, R.Roy, A. Harrison 458 P52: Product To Process Lifecycle Management in Assembly Automation Systems B. Ahmad,I. U. Haq, T. Massod, R. Harrison, B.Raza, R.Monfared 467 P53: Digital Factory Simulation Tools for the Analysis of a Robotic Manufacturing Cell A. Caggiano, R. Teti 478 P54: Diverse noncontact reverse engineering systems for cultural heritage preservation T.Segreto, A. Caggiano, R.Teti 486 P55: A Two-phase Instruments Selection System for Large Volume Metrology Based on Intuitionistic Fuzzy Sets with TOPSIS method Bin Cai, J. Jamshidi,P.G. Maropoulos, P.Needham 494 P56: Viewpoints on digital, virtual, and real aspects of manufacturing systems. H. Nylund, P.Andersson 504 P57: Virtual Gage Design for the Effective Assignment of Position Tolerances under Maximum Material Condition G. Kaisarlis, C. Provatidis 512 P58: Dimensional management in aerospace assemblies: case based scenarios for simulation and measurement of assembly variations P. Vichare, O. Martin, J. Jamshidi, P.G. Maropoulos 522 P59: Evaluation of Geometrical Uncertainty Factors during Integrated Utilization of Reverse Engineering and Rapid Prototyping Technologies 532

G. Kaisarlis, S. Polydoras, C. Provatidis P60: Knowledge capitalization into Failure Mode and Effects Analysis G. Candea, C. Candea, C. Zgripcea 542 P61: Robust design optimization of energy efficiency: Cold roll forming processJ. Paralikas , K. Salonitis, G.Chryssolouris 550 P62: An Analysis of Human-Based assembly processes for immersive and Interactive SimulationL.Rentzos, G.Pintzos, K. Alexopoulos., D. Mavrikios, G. Chryssolouris 558 P63: PROTOTYPE designing with the help of VR techniques: The case of aircraft cabin L.Rentzos, G. Pintzos, K. Alexopoulos, D. Mavrikios, G.Chryssolouris 568 P64: Knowledge management framework, supporting manufacturing system design K. Efthymiou, K. Alexopoulos. P. Sipsas, D. Mourtzis, G. Chryssolouris 577 P65: A manufacturing ontology following performance indicators approach K. Efthymiou, D. Melekos, K. Georgoulias, K. Sipsas, G. Chryssolouris 586 P66: Structuring and applying production performance indicators G. Pintzos, K. Alexopoulos, G. Chryssolouris 596 P67 A Web-based Platform for Distributed Mass Product Customization: Conceptual Design D. Mourtzis, M.Doukas,F. Psarommatis 604 P68: Machining with robots: A critical review J. Pandremenos, C. Doukas, P. Stavropoulos, G. Chryssolouris 614 P69: A pushlet-based wireless information environment for mobile operators in human based assembly lines S. Makris, G. Michalos, G. Chryssolouris 622 P70: RFID-Based Real-time Shop-Floor Material Management: An AUTOM Solution and A Case Study T. Qu, H. Luo, Y. Zhang, X. Chen, G. Q. Huang 632 Proceedings of DET2011 7th International Conference on Digital Enterprise Technology Athens, Greece 28-30 September 2011 1 ECONOMICAL ANALYSIS FOR INVESTMENT ON MEASURING SYSTEMSMaria Xi Zhang Department of Mechanical EngineeringThe University of Bath, UK X.Zhang@bath.ac.uk Prof. Paul Maropoulos Department of Mechanical EngineeringThe University of Bath, UK P.G. Maropoulos@bath.ac.uk Jafar Jamshidi Department of Mechanical EngineeringThe University of Bath, UK J.Jamshidi@bath.ac.uk Nick Orchard Manufacturing Technology Rolls-Royce Plc, UK Nick.Orchard@rolls-royce.com ABSTRACT Metrology processes contribute to entire manufacturing systems that can have a considerable impact onfinancialinvestmentincoordinatemeasuringsystems.However,thereisalackofgeneric methodologiestoquantifytheireconomicalvalueintodaysindustry.Tosolvethisproblem,a mathematicalmodelisproposedinthispaperbystatisticaldeductivereasoning.Thisisdone through defining the relationships between Process Capability Index, measurement uncertainty and toleranceband.Thecorrectnessofthemathematicalmodelisprovedbyacasestudy.Finally, severalcommentsandsuggestionsonevaluatingandmaximizingthebenefitsofmetrology investment are given. KEYWORDS Metrology,Measurement,EconomicalAnalysis,ProcessCapabilityIndex,StatisticalProcess Control. List of Abbreviations and Annotations DenotationMeaning CpProcess capability index. Standard deviation. USLUpper specification limits. LSLLower specification limits. TTolerance band, T = USL LSL. Subscript 1True value of the manufacturing process. Subscript 2Observed value determined by measurement. Subscript mMeasuring system. U/TUncertainty-Tolerance ratio. 1. INTRODUCTION Highvolumeandlargescaleassembliesand fabricationswithcomplexsurfacesareincreasingly 2 engagedinthecapitalintensiveindustries,suchas theaerospace,automotiveandshipbuilding (Maropoulosetal.2008).Themetrologyhascome toplayanimportantrolein themodern manufacturingsystems.Themetrologyprocess, whichemploysthemeasuringandinspection activities,willliaiseallthephasesofproduct lifecyclefromdesign,manufacturing,in-serviceto after-marketmaintenance(Caietal.2008, Kunzmannetal.,2005). Hence,theeconomical analysisonpurchasingnewmeasuringsystemsand metrologyprocessesisexpectedtodeliverhigh ratesofreturnbythemanufacturers,whoarealso normallytheheavyinvestors(Quatraro2003, RenishawPlc2008,Swann1994).However,inthe currentindustry,thereisalackofgeneric methodologies to evaluate the economical value that themetrologyprocessbringstoentire manufacturingsystems(Schmittet al2010,Schmitt etal.2010).Thereareafewcomprehensive methodologies,whichcanproperlyquantifythe economicalbenefitsoftheinspectionprocesses deliveringtocomplexmodernproduction environments (Kunzmann et a., 2005). The aim of this paper is to quantitatively estimate thevaluethatthemetrologyprocessesaddtothe manufacturingsystem.Toachievetheaim,we firstly discuss whether or not the inspection process canbringeconomicbenefitstothemanufacturers andtheinvestorsbyabriefliteraturereviewas backgroundstudyinSection2.Thena mathematicalmodelissuccessfullyestablishedto definetherelationshipsbetweenProcessCapability Index(Cp),measurementuncertainty(U)and toleranceband(T) inSection3,followedbyacase studytoprovethecorrectnessofthemathematical model(Section0).Finally,inSection4,the conclusionisgiventhatthemetrologyprocessis economicallybeneficialtomodernmanufacturing systems. 2. BACKGROUND STUDY Itisnowwell-agreedthattheproductlifecycleis builtupbyseverallinkedphases,includingdesign, manufacturing,in-serviceandafter-market maintenance(Figure1).Thecustomers requirementsarecapturedinthedesignphase,and thespecificationsoftheproductsaredefined. Meanwhilethesuppliersarecalledinandthe supply chain is established. Then the CAD model of theproductissenttothemanufacturingphase, wherethemachining,inspectionandassembly activitiesareengagedleadingtothephysical products.Theproductsaredistributedandserviced inthemarket,andrequiremaintenanceand overhaul service at certain times during their service periods(Zhengetal.2008).Duringthisloop,the metrologyisthefundamentaltooltogain informationandknowledgeinallphasesofthe productlifecycleestablishinglinksbetweenthese separate phases (Maropoulos et al. 2008, Schmitt et al 2010 ). Therefore, the metrology plays a vital role inthequalitycontrolprocesstoguaranteethe productconformance.Metrologyincreasesthe productivityoftheengineeringeconomics, particularly in robust engineering projects. Figure 1. Roles of Metrology in Product Lifecycle Historicallyconcernsonmetrologyeconomics startedinthe1970s,whencomputeraided measurement techniques were increasingly regarded asameanstocontrolindustrialmanufacturingand qualityofallkindsofproducts(Osanna2002).In 1977, Peters raised the questions of why measure, what to measure and how much the measurement pays(Peters1977).Hewasconcernedwiththe macro-economiccontributionofmetrologyin industrializedsocieties.Buthedidnotspecifythe details of questions related to the micro-economical analysis. In 1993, Quinn, former director of Bureau InternationaldesPoidsetMesures(BIPM),stated thatmeasurementandmeasurementrelated operationshavebeenestimatedtoaccountfor between3%and6%oftheGDPofindustrialized countries.Heconcludedthattheeconomicsof metrologywouldincreasefurtherduetothe continuousincreaseofhighaccuracymachining requirementsandonlinemeasurement implementations(Quinn,1993).Sincethen, investigationsonthemacro-economicalimpactof metrologyhavebeenconductedworldwide particularlyacrosstheUK(Swann,2009),EU (LArrangement2003),US(Tassey1999,Tassey 3 1999)andJapan(McIntyre1997)bynational authoritative organizations.Ontheotherhand,themicro-economicsof metrologyhasbeenthesubjectofacademic researchandindustrialapplicationsfordecades. Someresearchesfocusedonimprovingthe efficiencyandaccuracyofCMMcalibration proceduresthustosavetimeandmoney.The temperaturedistributionalongthemachining surfacewastheoreticallymodelledand experimented to fast guide the groove depth in laser machiningprocesses(ChryssolourisandYablon, 1993).TheerrorsourcesofCMMmeasurement wereidentifiedanddeliberatelyanalyzed(Savio, 2006).Thismethodprovidesatime-efficiency methodtoquickcalibrateCMMperformance.In aerospaceandautomotiveindustries,wherelarge andcomplexpartsareindemands,thedata samplingtechniquesforinspectionoffree-form surfaceshavebeendevelopedminimizing inspectioncostsandtime(Chryssolourisetal, 2001). Recently,someresearchesbegantofocuson identifyingthecostsandbenefitsofmetrologyas partoftheentireindustrialmanufacturingsystem forrobustengineeringproject.Whenconsidering themicro-economicalvaluethatprocessesaddon themanufacturingsystems,itisnecessaryto comparethecostsoftheprocessesagainstthe benefitstheygenerate(Greeff,2004).Thesame appliestothemetrologyprocesses.Itisrelatively easy to calculate the cost of metrology using several establishedguidelines(Semiconductor2004, Semiconductor2004).However,thebenefits analysisoftheinvestmentonthemetrology equipmentandprocessesremainsproblematicand challenging.Thisrequiresdetailedconsiderations onfuzzyaspects,suchasstabilityofproduction, measurementuncertainty,samplesizeandcostof risks(Schmitt2010).Andthisisalsowhatour research concentrates on. Tosolvetheaboveproblem,amathematical modelispresentedinthispaperbypreliminarily observingthecalculatingmethodtoquantifythe benefitsofmetrologyprocess.Animportant parameterforstatisticalprocesscontrol(SPC),the Cp, is introduced during the mathematical reasoning toreflectthecosteffectivenessofmetrology. Hence,inthenextsection(Section3),a mathematicalmodelrelatedtoCp,UandTis establishedbasedonstatisticalanalysistechniques and logical reasoning steps. 3. MATHEMATICAL MODEL Cpisameasurementparametertoindicatethe abilityofaprocesstoproduceoutputswithin specificationlimitsresultingfromthestatistical analysisontheproductqualityoftheentire manufacturingsystem(IST,2010).SinceCpis derivedfromthemathematicalstatistics,inthis sectionwefirstlygiveabriefintroductiononthe normaldistribution,whichisfrequentlyusedin statisticsandstatisticalprocesscontroltorepresent the mathematical model of the Cp. Then the sum of twonormaldistributionsisdiscussed.Finally,the mathematical model is applied to the manufacturing environment,wheretherelationshipbetweenCp,U and nominal T is defined. 3.1.TERMSRELATEDTONORMAL DISTRIBUTION Normaldistributionisoneofthemostfrequently used mathematical models in probability theory and statisticalanalysis.Itisacontinuousprobability distributionthatdescribes,atleastapproximately, anyvariablethattendstoclusteraroundthemean. AsshowninFigure2,thegraphofnormal distributionisbellshapedwithapeakvalueatthe mean(Patel1982).Inprobabilitytheory,the functionwhichdescribestherelativelikelihoodof theoccurrenceofthesecontinuousrandom variables at the mean value in the observation space is called probability density function (PDF).Figure 2. Normal distribution Thesimplestcaseofanormaldistributionis known as the standard normal distribution, of which PDF is described as 22121) (xe x= Inmoregeneralcases,anormaldistributionis derived from exponentiating a quadratic function:c bx axe x f+ +=2) ( Thisprovidestheclassicalbellcurveshapeof normaldistribution.Todescribethefunction expediently,ratherthanusinga,bandc,itis usually assumed that: 4 ab2 = anda 212 = . Bychangingthenewparameters,thePDFis rewritten in a convenient standard form as ) (121) (222) (2 = = xe x fx Equationclearlyshowsthatanynormal distributioncanberegardedasaversionofthe standard normal distribution that has been stretched horizontally by a factor and then shifted rightward by a distance (as shown in Figure 2). In statistics, theparameteriscalledbias,whichspecifiesthe positionofthebellcurvescentralpeak.The parameter 2iscalledthevariance,whichindicates howconcentratedthedistributionisclosetoits meanvalue.Thesquarerootof2iscalledthe standarddeviationandisthewidthofthedensity function. Therefore,anormaldistributioncanbedenoted as ) , (2 .WhenarandomvariableXis distributednormallywithmeanandstandard deviation , it is expressed as: ) , ( ~2 X 3.2.DETERMININGTHERELATIONSHIP BETWEEN CP1, CP2 AND (U/T) Aprocessisusuallydefinedasacombinationof tools,materials,methods,andpeopleengagedin producingameasurableoutput,e.g.aproduction line for machined parts. Therefore all manufacturing processesaresubjectedtostatisticalvariability whichcanbeevaluatedbystatisticalmethods (Altman,2005).Tomeasurethevariabilityofthe manufacturingprocess,theprocesscapabilityis definedandexpressedintheformofCptoreflect howmuchnaturalvariationaprocessexperiences relativetoitsspecificationlimits.Thisallowsfora comparisonbetweendifferentprocesseswith respect to quality controls (Greeff, 2004). Figure 3. Process capability TheCpstatisticsisdefinedbasedonthe assumptionthatmeasuringresultsofthe manufactured parts are normally distributed (Figure 3).Assumingatwosidedspecification,ifand arethemeanandstandarddeviation,respectively, ofthenormaldata and USL,LSLare theupperand lower specification limits respectively, and then the process capability indices are defined as follows: 6LSL USLCp= for non-bias process, or)3,3min( LSL USLCpk = forbiasprocess (Montgometry, 1996).Inordertoclarifytheproblem,thisresearchhas taken 6LSL USLCp= (fornon-biasprocess)for considerationanddiscussion.Butthemethodology developedfromtheresearchcanbegeneralizedto thebiasmanufacturingprocesswithslight modifications (Please refer to Future Work).Asmentionedinprevioussection,thetruevalue ofCpthatisobservedafterthemeasuringprocess canpossiblybeunderestimatedduetothe interventionofthemeasurementuncertainty.For the convenience of understanding, we analogize the process capability reduction as the traffic lights to easilyrevealgo/no-godecisionmakinginreal manufacturingenvironment(Figure4).Ifa productionmanagerreceivesaprocesscapability evaluation result of a lower than expected Cp value, he/shereceivesaredlightmeaninghe/sheshould stop the manufacturing, or purchase a more accurate machinetoolorprocess.TheunsatisfactoryCp valuemaybecausedbyUfromtheinspection process(flashingasyellowlight)insteadofthe machiningprocessitself(greenlight).Asthe measurementinstrumentswithloweruncertainties are generally cheaper than high precision machining tools, it is more economical to investment in proper 5 measurementinstrumentsormetrologyprocesses. Thisprovidesthemanufacturingsystemwitha green light. Figure 4. Influence of U on Cp. Sothenextstepofourworkaimsto quantitativelyevaluatehowmuchtheobservedCp valueisreducedbythemeasurementuncertainty, anddeterminethetrueoriginalvalue.The mathematicalrelationshipsbetweenthetrueCp value, observed Cp value and Uncertainty-Tolerance ratio are deduced by mathematical transformations. Itisassumedthattheoutputsofthe manufacturing process are normally distributed, and well centred to the mean value, that is to say there is no bias (=0). From the definition of Cp, it follows that1 116 6 T LSL USLCp== and2 226 6 T LSL USLCp== thus116pCT= and226pCT= Assuming U is within 2, then2Um = Giventhesumofnormaldistributionprovedby Equation (A1) in Appendix A: 2 2122 m + = InsertingEquations,andintoEquation ,

2 2122)2( )6( )6(UCTCTp p+ = DividedbyTatbothsidesoftheEquationwe achieve: 2 2122)21( )61( )61(TUC Cp p + = ThemathematicalrelationshipbetweenCp1,Cp2 andU/ThasbeenidentifiedinEquation.The successfulquantificationofthereductionvalueof Cp is due to measurement uncertainty. Note that U/T valueisapopularparameterfortheinspection engineersselectingmeasuringsystems;therefore, Equationcanbeutilizedwhenmakingrational investmentdecisionsonpurchasingnewmachining or metrology systems. 4. METHOD VERIFICATIONInordertodemonstratethecorrectnessofthefinal result(Equation),aseriesofcorrelationcurves wereusedtocreateexplicablyreflectingthe correlationbetweenCp1,Cp2andU/Tbydata acquisitioning and processing method.6 Table 1. Data collection to acquire the true Cp value (Cp1) Fordatacollection,itisassumedthatthereare fivemanufacturingsystems,ofwhichthetrueCp values (Cp1) are 2.00, 1.75, 1.50, 1.25, 1.00 and 0.75 respectively.Giventhefactthattheinspection engineersconsiderU/Tasanimportantreference parameterformeasurementinstrumentselection, FtheU/TinEquationistreatedasavariable continuously varying between 0 and 1, which means thatthemeasuringsystemsselectedbythe inspectionengineerschangedfromtheperfect measurementmachines(withoutincurringany uncertainty)totheworstsituation.Finally,the observedCpvalues(Cp2)foreachcaseofthe instrumentselection(changingU/Tvalues)under variousmanufacturingsystems(Cp1)areacquired viaEquation .Thisrevealshowthe manufacturingsystemanditscapabilityindexhave been influenced by the measurement uncertainty. Followingthemethoddescribedabove,apartof the data collection and acquisition is listed in Table 1. Then the curve is constructed as shown in Figure 5,whichinexplicablyrevealshowtheprocess capabilitydriftsdownduetotheinfluenceofthe measurementinstrumentselectionassociatedwith the changes in the measurement uncertainties.00.20.40.60.811.21.41.61.822.20 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1U/TCp2Cp1=2Cp1=1.75Cp1=1.5Cp1=1.25Figure 5. Influence of U/T on Cp. 5. DISCUSSION5.1.CONSISTENTWITHISOSTANDARD AND NPL GOOD PRACTICE GUIDEThe result illustrated in Figure 5 has confirmed that the Cp of a manufacturing system is underestimated duetotheUintroducedintheinspectionand verification process, as discussed in Section 2. More importantly,theconclusionfromthisresultis consistentwithISO14253(ISO,1998)andNPL Good Practice Guide (Flack and Hannaford, 2006). ISO14253providesclearguidelinesaboutthe needtoallowfortheuncertaintyofthe U/TCp1=2.00Cp1=1.67Cp1=1.5Cp1=1.33 021.6666671.51.333333 0.011.9857541.6583951.4939621.329087 0.021.9447751.6343011.4762751.316588 0.031.8817751.5963771.4481441.296516 0.041.8030461.5474611.4113311.269899 0.051.7149861.4907121.3678821.237969 0.061.6230691.4291551.3198581.202031 0.071.531411.3653871.2691371.163341 0.081.4427751.3014481.2173021.123029 0.091.3588161.2388241.1655961.082046 0.11.2803691.1785111.1149411.041158 0.111.2077151.1211211.0659771.000951 0.121.1407921.0669741.0191120.96185 0.131.0793321.0161850.9745810.924145 0.141.0229680.968730.9324870.888021 0.150.9712860.92450.8928410.853579 0.160.9238690.8833320.8555940.820859 0.170.8803140.8450340.8206530.789854 0.180.8402470.8094050.7879040.760528 0.190.8033230.7762440.7572190.732822 0.20.7692310.7453560.7284640.706665 0.210.7376910.7165560.7015090.681978 0.220.7084540.6896720.6762250.658679 0.230.6812980.6645440.6524890.636684 0.240.6560230.6410260.6301850.615913 0.250.6324560.6189840.6092080.596285 0.260.6104370.5982980.5894560.577726 0.270.5898290.5788570.5708380.560165 0.280.5705070.5605610.5532680.543534 0.290.552360.5433180.536670.527772 0.30.5352880.5270460.5209720.512821 0.310.5192020.5116710.5061070.498624 0.320.5040230.4971250.4920180.485134 0.330.4896790.4833460.4786470.472303 0.340.4761040.4702770.4659460.460088 0.350.4632410.4578690.4538690.448449 0.360.4510370.4460730.4423720.437349 0.370.4394430.4348480.4314180.426755 0.380.4284160.4241550.420970.416634 0.390.4179160.4139590.4109960.406958 0.40.4079090.4042260.4014660.3977 7 measurementinstrumentsbyreducingthesizeof theacceptancebands,andthereforearguesthat thereisconsiderableinterestinhavingaccessto measurementinstrumentswithloweruncertainty (ISO,1998).Onestepfurther,NPLGoodPractice GuideNo.80(FlackandHannaford,2006) illustrates the impact of the U on the manufacturing process(Figure6).Itdefinesprocesstolerance as anintangibletolerancevariancethatisdeveloped fromthenominaltolerancerange,butisnarrowed down as the U expands.Figure 6. Impact of U(Flack and Hannaford, 2006).,Tothispointwehavefurtherdevelopeda mathematicalmethodwhichquantitativelyand directlydisclosestheimpactofUontheprocess capability. 5.2.WHAT DO THE CURVES INDICATE?The series of curves in Figure 5 put forward several suggestions on how U impacts on the manufacturing system. Firstly,asdiscussedinSection0,itcanbeseen thattheobservedCpvalue(Cp2)ofthe manufacturing system reduces as U increases. If we followthecommonruleofmetrologywhereUis considered to be 1/10 of the tolerance (U/T=10) for afinely-controlledprocess(Chengetal.2009),it canbereadfromFigure5thata6 quality controlledprocessofwhichthetrueCpvalue(Cp1) equals 2, will be underestimated at Cp =1.28 due to theinterventionofuncertaintiesfrommeasurement andinspectionprocesses.Thisresultindicatesthat theprocesscapabilitycanbeimprovedby purchasingnewmeasuringsystems.Asnormallya moreaccuratemeasurementinstrumentischeaper thanamorecapablemachiningtool(Kunzmannet al,2005),investinginnewmeasuringsystemsand new inspection processes can be more economically viable than purchasing new machining tools. Secondly,itcanbeseenthatthehighertheCp valuethatthemanufacturingsystemhas,themore sensitivethemanufacturingsystemswillreact towardsthemeasurementinstruments.Thisis consistentwiththeproductionengineerscommon viewthatahighlycapablemachiningsystem requiresahighlyaccuratemeasuringsystemto inspectandverifythequalityofthefinalproducts. Italsosuggeststhatgenerallythehighlycapable machiningsystemsareworth the investmentwitha highly accurate measuring system.Thirdlyandthemostimportantly,ourworkhas demonstratedthattheeconomicalvalueofthe investmentonthemeasurementequipmentandthe inspectionprocessescanbequantitativelydefined bythemeansofthemeasurementuncertainty.As theresearchcontinues,anaccuratemeasurement instrumentcanpossiblybeevaluatedbymodelling thedistributiondensityofthevaluesoftheparts featuresandtheeffectofUonCpvalue, contributingtodecisionmakingformore complicatedproductionenvironments.The investmentsonthemetrologyprocessescan generate direct value by reducing the scrap rate and productioncost,thusitbringseconomicalbenefits for manufacturing systems. 6. CONCLUSION AND FUTURE WORKThemetrologyprocesswhichinvolves measurementandinspectionactivitiesplaysan increasinglyvitalroleinthehighvalueadded manufacturing industries. The heavy investors, such as the manufacturers and venture capitalists, believe thatproperinvestmentsinmorecapablemetrology equipment andprocessesyieldahighrateofreturn in capital intensive industries. Thispaperreviewsthestateoftheartmetrology and dimensional measurement techniques within the currentmanufacturingindustries.Thequestionis howtoevaluatethevalueaddedbymetrology processes on the manufacturing systems. To answer thequestion,amathematicalmodelwas successfullyestablishedbystatisticaldeductive reasoningprocedures,whichdefinedthe relationshipsbetweenCp,Uandtoleranceband, followed by a case study to verify the mathematical model. It is concluded that the metrology process is economicallybeneficialtomodernmanufacturing systems. Finally, several suggestions and comments oneconomicalandproductiveinvestmentin metrologysystemswereconcludedbasedonthe mathematicalmodelderivedanddatacollected during the case study. Infuturework,aneconomicalevaluationmodel forinvestinginmeasurementequipmentwillbe developedbasedonthemathematicalmodelinthis paper. It will concern the question that how much it willcostifamanufacturermakesanerrorinthe 8 uncertaintyzonesaroundthetolerancelimits.In otherwords,howmuchitcoststoscrapapartthat isactuallyintolerance,andhowmuchitcostsifa non-conformingpartisallowedtoreachthe customer.Thesedecisionmakingstrategieswillbe developed by statistical analysis methodologies, and willdeploytheriskmanagementtechniqueswhich arecommonlyusedinhighvalueaddedcapital investment. 7. ACKNOWLEDGEMENTS Theworkreportedinthispaperhasbeen undertakenaspartoftheEPSRCInnovative Manufacturing Research Centre at the University of Bath(grantreferenceGR/R67507/0)andhasbeen supported by a number of industrial companies. The authorsgratefullyacknowledgethissupportand express their thanks for the advice and support of all concerned. 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Dance,D.L.,Costofownershipformetrologytools NIST,ProceedingsofSemiconductorMetrology Workshop, Gaithersberg MD, USA, 30/01/1995. Flack,D.R.andHannaford,J.,Fundamentalgood practiceindimensionalmetrology,Measurement Good Practice Guide No. 80, NPL, United Kingdom, 2006. Gindikin,S.G.Distributionsandconvolution equations,GordonandBreach,NewYork,USA, 1992. Gohberg,I.C.andFeldman,I.A.,Convolution equationsandprojectionmethodsfortheirsolution, TranslationsofMathematicalMonographs,Vol.41, 2006, pp.120-125. Greeff,G.,PracticalE-manufacturingandsupplychain management, Newnes, New York, USA, 2004. Hratch G. S. and Robert L.W., Impact ofmeasurement andstandardsinfrastructureonthenationaleconomy and international trade, Measurement, Vol. 27, No. 3, 2000, pp. 179-196. ISO,ISO14253-1:1998,GeometricalProduct Specifications (GPS) -- Inspection bymeasurement of workpiecesandmeasuringequipment--Part1: Decisionrulesforprovingconformanceornon-conformance with specifications, 2005. 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NIST/Sematech EngineeringStatisticsHandbook,Retrieved: 01/06/2010, http://www.itl.nist.gov/div898/handbook/pmc/section1/pmc16.htm,Zheng,L.Y.,McMahon,C.A.,Li,L.,Ding,L.and Jamshidi,J.,Keycharacteristicsmanagementin productlifecyclemanagement:asurveyof methodologiesandpractices,Proceedingsofthe InstitutionofMechanicalEngineers;PartB;Journal ofEngineeringManufacture,Vol.222,No.8,2008, pp. 989-1008. Zill,D.G.andCullen,M.R.,Advancedengineering mathematics ,Jones&BartlettPublication,New York, USA, 2006. APPENDIX A. SUM OF NORMAL DISTRIBUTIONS Inprobabilitytheory,thecalculationofthesumof normal distributions is based on the distributions of therandomvariablesinvolvedandtheir relationships. Conclusivelyspeaking,thesumofthetwo independentnormaldistributionsisalsonormally distributed,withthebiaswhichisthesumofthe two biases, and the variance which is the sum of the twovariances(i.e.,thesquareofthestandard deviationisthesumofthesquaresofthestandard deviations).Theabovecanbeexpressedin mathematical formulae as:if) , ( ~2 X and ) , ( ~2 Y, and they are independent, then ) , ( ~2 2 + + + =Y X Z (A1) Theabovepropositionisprovedusing convolutionalproofmethod(Gohbergand Feldman, 2006). Inmathematicsanalysis,convolutionisa mathematicaloperationontwofunctionsf(x)and g(x),producingathirdfunctionthatistypically viewed as a modified version of one of the original functions(Gindikin,1992).Inordertodetermine the sum of the normal distributions, according to the totalprobabilitytheorem(Mendenhalletal.2005), the probability density function of z is =xyZ Y X Zdxdy z y x f z f ) , , ( ) (, , X and Y are independent, therefore = . ) , | ( ) ( ) ( ) (xyZ Y X Zdxdy y x z f y f x f z f

) , | ( y x z fZ istriviallyequalto )) ( ( y x z + , whereisDirac'sdeltafunction(ZillandCullen, 2006), therefore + = . )) ( ( ) ( ) ( ) (xyY X Zdxdy y x z y f x f z f (A2) asitwaspreviouslyassumedthatZ=X+Y,we substitute (z-x) for y in Equation (A2): =xY X Zdx x z f x f z f ) ( ) ( ) ((A3) which is recognized as a convolution of fX with fY. ThereforethePDFofthesumofthetwo independent randomvariables Xand YwithPDFs f and g respectively is the convolution + = du u x g u f x g f ) ( ) ( ) )( * ( First, byassumingthe twobiases andarezero, the two PDFs are transformed as22221) ( xe x f=and22221) ( xe x g=. The convolution becomes 10 duu x uconsduu x uconsduu x uconsdu e e x g fu x u)2) ) ( (exp( ] [)2) (2exp( ] [)2) (exp( )2exp( ] [)21( )21( ) )( (2 22 2 2 2222222222) (22222 + + + + + = = = = (A4) whereconsisshortforconstant,andthebelowis the same. Continuing the integral calculation from Equation (A4): dux uxconsduxx uconsduu x ucons + + + + + + =+++ + = + 2 222 222 22 222 222 222 222 22 22 2 2 22) )( (exp) ( 2exp ] [) ( 2 2) )( (exp ] [)2) ) ( (exp( ] [ (A5) Notethattheresultofintegral du A u ) ) ( exp(2+ doesnotdependonA (This canbeprovedbyasimplesubstitution: w=uA, dw=du, and theboundsof integration remain and+).FinallyfromthelastpartofEquation (A5),weobtaintheconvolutionofPDFsf(x)and g(x) as below

] [) ( 2exp ] [ ) )( (2 22consxcons x g f + = (A6) whereconstantmeansnotdependingonvariable x.Equation (A6) simply reveals that the sum of the twoPDFscanbeviewedasaconstantmultiple of+) ( 2exp2 22 x.Inotherwords,thesumof normallydistributedrandomvariablesisalso normallydistributedwiththenewvariance of ) (2 2 + . Therefore,theinitialpropositioninEquation (A1) hasbeenproved.InSection3.2,theequality relationship in Equation (A1) will be utilized to link the relationships between Cp, U and T. Proceedings of DET2011 7th International Conference on Digital Enterprise Technology Athens, Greece 28-30 September 2011 11 MEASUREMENT ASSISTED ASSEMBLY AND THE ROADMAP TO PART-TO-PART ASSEMBLY Jody Muelaner The University of Bath jody@muelaner.com Amir Kayani Airbus in the UK amir.kayani@airbus.com Oliver Martin The University of Batho.c.martin@bath.ac.uk Prof Paul Maropoulos The University of Bath p.g.maropoulos@bath.ac.uk ABSTRACT Cycletimesandproductioncostsremainhighinaerospaceassemblyprocesseslargelydueto extensivereworkingwithintheassemblyjig.Other industriesreplacedthesecraftbasedprocesses withpart-to-partassemblyfacilitatedbyinterchangeableparts.Duetoverydemandinginterface tolerancesandlargeflexiblecomponentsithasnotbeenpossibletoachievetherequired interchangeabilitytolerancesformostaerospacestructures.Measurementassistedassembly processescanhoweverdelivermanyoftheadvantagesofpart-to-partassemblywithoutrequiring interchangeableparts.Thispaperreviewsassemblyconceptssuchasinterfacemanagement,one-wayassembly,interchangeability,part-to-partassembly,jiglessassemblyanddeterminate assembly.Therelationshipbetweentheseprocessesisthendetailedandtheyareorganizedintoa roadmap leading to part-to-part assembly. KEYWORDS Part-to-Part,MeasurementAssistedAssembly,Interfacemanagement,Fettling,Shimming,One-Way Assembly, Determinate Assembly 1. INTRODUCTION Traditionallytheproductionoflargeaerospace assemblieshasinvolvedtheinefficiencyofcraft production;craftsmenfettlingorshimmingpartsto fit and carrying out a wide variety of highly skilled operations using general purpose tools. Reliance on monolithic jigs has also meant this approach has not resultedinflexibilitysincethejigsarehighly inflexible, costly and have long lead times. Itcouldbesaidthattheproductionoflarge aerospaceassembliescombinestheinefficiencyof craftproductionwiththeinflexibilityoftheearly formsofmassproduction.Thisisclearlyanissue, butwhyissuchaninefficientmodeofproduction used?Itisnotduetoalackofcompetence, awarenessoftheissuesorwillingnesstoembrace newtechnologies;theaerospaceindustrybenefits fromaccesstomanyofthebestmindsin engineeringandiswellknownforutilizingthe latest technologies in many areas. The root causes are the difficulties in maintaining veryclosetolerancerequirementsoverlarge structuresandthelargenumberofdifferent operationsforrelativelylowproductionvolumes. Issues related to maintaining high tolerances are the biggestchallenges;thelightweightaerostructure hasflexiblecomponents;interfacesareoften imprecise especially for composite components and itisverydifficulttodrillpatternsofholesin different components which will match and lock the assembly into its correct overall form.The traditional solution to these issues is to use a monolithicjigwhichholdsflexiblecomponentsto theircorrectfinalformastheassemblyisbuilt-up, interfacegapsarethenmeasuredinthejigsothat 12 shims can be fitted and holes are drilled through the stackofcomponents.Itisthennecessarytobreak theassemblyaparttodeburholes,cleanandapply sealantbeforethefinalassemblytakesplace (Pickettetal.1999;MuelanerandMaropoulos 2008).Thisprocessresultsinadditionalprocess steps, inflexibility due to reliance on monolithic jigs andinefficientcraftbasedproductionduetohigh levelsofreworkingin-jig.Additionally,thevariety ofoperationsatlowvolumescombinedwiththe hightolerancesrequiredmakesitverydifficultto automateprocesses.Furtherincreasingthenumber ofcraftbasedprocesses requiredwhilemaintaining closetolerancesmeansthatwhereautomationis used,itisgenerallybasedoninflexiblegantry systems. Therehasinrecentyearsbeenagreatdealof interestinmovingawayfromtheinefficienciesof thetraditionalbuildprocessandconceptssuchas Part-to-PartAssembly,One-WayAssembly, PredictiveShimming,MeasurementAssisted AssemblyandDeterminateAssemblyarebeing discussed in the literature. The precise definition of thesetermsisnotalwaysclearandakeyobjective of this paper is therefore to provide clear definitions of some commonly used terms. 2. INTERFACE MANAGEMENT Interfacemanagementinvolvesprocesseswhich seektoensurethatanyclashesandgapsbetween componentsaremaintainedwithinacceptable limits.Itisthereforethekeytoensuringthe structural integrity of an assembly. Where interfaces cannotbemanagedthroughinterchangeable components it often results in inefficient craft based in-assembly fitting processes. Thestandardapproachtointerfacemanagement employedinhigh-volumemanufacturingisto producecomponentstosufficientlytighttolerances tofacilitateinterchangeabilitywhilemaintaining acceptableinterfaceconditions.Thealternativeto interchangeability is to create bespoke interfaces by makingadjustmentstotheformofcomponents. Suchadjustmentsmaybeadditive(shimming)or subtractive(fettling).Itshouldbenotedthatwhere bespoke interfaces are used to manage the interfaces itisstillpossibletohaveinterchangeableparts withintheassembly.Forexamplearibmaybean interchangeablepartbutnotbeproducedto interchangeability tolerances and shims then used to achieve interface management. Bespoke interfaces, whether created by fettling or by shimming, may be produced using traditional in-assemblyreworkingprocessesorusing measurement assisted predictive processes.Inthetraditionalapproachcomponentsarepre-assembled,assemblytoolingisoftenusedatthis stage to control the form of the assembly. Any gaps andclashesaremeasuredinthispre-assembly condition, the assembly is broken apart, components are fettled or shims are produced and the structure is reassembled. In measurement assisted assembly (MAA), or the predictiveapproach,componentsaremeasuredpre-assemblyandthismeasurementdataisusedto determine cutting paths for predictive fettling or the manufactureofpredictiveshim.Thecomponents andpossiblyalsotheshimscanthenbeassembled as though they were interchangeable. The various options for interface management are illustratedintheformofaVenndiagraminFigure 1. Figure 1 Venn Diagram for Interface Management Theinterfacebetweencomponentstypically involvesdirectcontactbetweenthesurfacesof componentsandalsohole-to-holeinterfacesinto whichfastenersareinsertedtojoincomponents together.Theaboveclassificationofinterface managementmaybeappliedtohole-to-hole interfacesaswellastotheinterfacesbetween surfaces of components. Forexample,inthecaseofaninterchangeable assembly all holes are pre-drilled in components. In thetraditionalin-assemblyfittingapproachto producingbespokeinterfacesfirstanyfettlingor shimmingiscompletedandthenholesaredrilled through the stack of components. The pre-assembly generallythenneedstobebroken,deburredand clearedofswarfbeforesealantcanbeappliedand the final assembly carried out. Bespokeholeplacementscanalsobeproduced usingapredictiveapproachinwhichholesare first placed in one component prior to any assembly and toatoleranceinsufficientforinterchangeability. Theholepositionscanthenbemeasuredandholes inthesecondcomponentplaced.Inthecaseofthe secondcomponentholesmustbelocatedto interchangeabilitytolerances(Muelanerand Maropoulos2010).Itmayinitiallyseemthatthis approachoffersnoadvantageoverfull interchangeability since holes must be placed in the secondcomponenttointerchangeabilitytoTheadvantageisgainedhoweverwhenalarge componentisjoinedtooneormoresmaller components. It is then possible to place holes in the largecomponentwhichrequiringahighlevelof accuracy. The accurate holes are placed in the small components which is a relatively easy task.2.1. INTERCHANGEABILITY (ICY)Interchangeability(ICY)istheabilityof componentstofittooneanotherwithoutrequiring anyreworking(interfacemanagement).An interchangeablepartcanthereforebetakenfrom oneassemblyandplacedintoanotherassembly without changing the form of the part.Lowcost,highvolumemanufacturingtypically depends on interchangeability but it is generally not possibletoachievetherequiredtolerancesfor majority of aircraft structure interfaces2.2. SHIMMING Shimminginvolvesaddingadditionalspacersor packersnormallyreferredtoasshimstoan assembly in order to fill gaps between components. Asexplainedabove,traditionallythisinvolves measuringactualgapsinapre-assembledstructure usingfeelergauges,producingshimsandreassembling with the shims in place. This traditional in-assembly fitting process may require a number of iterations before all gaps are within tolerance.InthecaseofpredictiveshimmiGray2009)componentsaremeasuredbeforebeing assembled.Inthisstatetheinterfacesurfacesare fullyvisiblemeaningthatratherthansimply determininggapsusingfeelergaugessurfaceprofilecanbecharacterizedusing3D scanningtechnology.Itisthenpossibletoproduce shimswhichmorefullyconformtothesurface profile of components.The major advantage of this approach is that preassemblyisnotrequiredandthereforeoneassembly is facilitated.2.3. FETTLING Traditionally fettling is carried out inwaytotraditionalshimmingoperations.Itis howeveralsopossibletocarryoutpredictive fettlinginwhichcomponentsaremeasuredand bespoke interfaces created before assembly.Predictivefettlingwasusedto interfacebetweenthewingboxribsandtheupper coverontheAdvancedLowCostAircraft Structures (ALCAS) lateral wing box demonstrator. Inthisprocessmeasurementsofthecoverprofile 13 .Itmayinitiallyseemthatthis approachoffersnoadvantageoverfull interchangeability since holes must be placed in the secondcomponenttointerchangeabilitytolerances. Theadvantageisgainedhoweverwhenalarge componentisjoinedtooneormoresmaller components. It is then possible to place holes in the largecomponentwhichrequiringahighlevelof accuracy. The accurate holes are placed in the small ponents which is a relatively easy task. ITY (ICY) istheabilityof componentstofittooneanotherwithoutrequiring anyreworking(interfacemanagement).An interchangeablepartcanthereforebetakenfrom neassemblyandplacedintoanotherassembly without changing the form of the part. Lowcost,highvolumemanufacturingtypically depends on interchangeability but it is generally not possibletoachievetherequiredtolerancesforthe structure interfaces. Shimminginvolvesaddingadditionalspacersor packersnormallyreferredtoasshimstoan assembly in order to fill gaps between components. traditionallythisinvolves assembledstructure usingfeelergauges,producingshimsandre-assembling with the shims in place. This traditional assembly fitting process may require a number of iterations before all gaps are within tolerance. shimming(Kayaniand componentsaremeasuredbeforebeing assembled.Inthisstatetheinterfacesurfacesare fullyvisiblemeaningthatratherthansimply usingfeelergauges,thefull surfaceprofilecanbecharacterizedusing3D scanningtechnology.Itisthenpossibletoproduce shimswhichmorefullyconformtothesurface The major advantage of this approach is that pre-isnotrequiredandthereforeone-way Traditionally fettling is carried out in-jig in a similar waytotraditionalshimmingoperations.Itis howeveralsopossibletocarryoutpredictive aremeasuredand bespoke interfaces created before assembly. Predictivefettlingwasusedtomaintainthe interfacebetweenthewingboxribsandtheupper ontheAdvancedLowCostAircraft Structures (ALCAS) lateral wing box demonstrator. easurementsofthecoverprofile wereusedtogeneratemachiningpathsforthe fettling of rib feet. The rib feet were then machined using a standard 6-axis industrial robot mounted on agantryoverthewingboxrobotwasgreatlyincreasedthroughtheapplication ofclosedloopcontrol photogrammetry systemInatraditionalmanualmachiningprocesshigh accuracyisachievedbyinitiallycuttingfeatures oversized,measuringthemandthenusingthesemeasurementstoguidethefurtherremovalof materialinaniterativeprocess.Asimilarbutfully automatedprocesswasusedinwhichtherobot initiallymaderoughingcutsoftheribfeet, measurementsweremadeandthesewereusedto apply corrections to the finishing cutprocessisillustratedin 2011). Figure 2 ALCAS Rib Foot Fettling ProcessTheregistrationofpointclouddatafrommultiple instrumentlocations(importantinenablingthistypeofpredictive process. 2.4. DRILLING Whereinterchangeabilitytolerancescannotbe achieveditisnecessarytoplaceholesatbespoke positions in such a way that patterns of hmatchsoastoallowclosefittingfastenerstopass throughbothcomponents. achievethisistodrillthroughbothcomponentsin the pre-assembly state. Thereareanumberofdisadvantagestothis approach,aerospaceassembliesoftencontain thousandsofholesandthedrillingoftheseholes representsasignificantpercentageofthecostof building an airframe (Bullen 1997theseoperationswithinthecapitalintensivebottle neck to production - which is the main assembly jigwereusedtogeneratemachiningpathsforthe The rib feet were then machined axis industrial robot mounted on agantryoverthewingbox.Theaccuracyofthe lyincreasedthroughtheapplication closedloopcontrol withfeedbackprovidedbya photogrammetry system.Inatraditionalmanualmachiningprocesshigh accuracyisachievedbyinitiallycuttingfeatures oversized,measuringthemandthenusingthese measurementstoguidethefurtherremovalof materialinaniterativeprocess.Asimilarbutfully automatedprocesswasusedinwhichtherobot initiallymaderoughingcutsoftheribfeet, measurementsweremadeandthesewereusedto to the finishing cut. The complete processisillustratedinFigure2(Muelaneretal. ALCAS Rib Foot Fettling Process Theregistrationofpointclouddatafrommultiple (Mitraetal.2004)maybe importantinenablingthistypeofpredictive Whereinterchangeabilitytolerancescannotbe achieveditisnecessarytoplaceholesatbespoke positions in such a way that patterns of holes closely matchsoastoallowclosefittingfastenerstopass throughbothcomponents.Thetraditionalwayto achievethisistodrillthroughbothcomponentsin assembly state.Thereareanumberofdisadvantagestothis approach,aerospaceassembliesoftencontain thousandsofholesandthedrillingoftheseholes representsasignificantpercentageofthecostof Bullen 1997). By carrying out theseoperationswithinthecapitalintensivebottle which is the main assembly jig 14 - the cost of drilling these holes is greatly increased. Furthermore, when a stack of components is drilled throughitisoftennecessarytobreaktheassembly tocleananddeburbeforere-assembling,adding costly additional operations. Orbitaldrilling(Kihlman2005)mayremovethe needtobreak,cleananddebur,andtherefore facilitateaone-wayassemblyprocess.Itwillnot howeverremovetheneedtodrillthrough componentsorfacilitatepart-to-partassemblyand therefore although some process steps are removed, drillingmuststillbecarriedoutwithinthebottle neck of the jig. Measurementassisteddeterminateassembly (MADA)hasbeenproposedasapotential predictiveapproachtoholeplacement.Inthis approach holes are first placed in large components to relatively slack tolerances. The hole positions are thenmeasuredandbespokeholesareaccurately placedtomatchinthesmallercomponents,thisis illustratedinFigure3(MuelanerandMaropoulos 2010). Figure 3 MADA Predictive Hole Placement 3. PART-TO-PART ASSEMBLY Part-to-partassemblyis anassemblyprocesswhere anyinterfacemanagementisconductedpre-assemblyallowingarapidone-wayassembly process.Part-to-partassemblymaythereforebe seenasthekeyrequirementforanefficientbuild process. Afullpart-to-partassemblyprocesswould involveeitherinterchangeablecomponentsor predictivefettling,shimmingandholeplacement beingcarriedoutpriortoassembly.Currentlypart-to-partassemblyiscommonlyachievedthrough interchangeabilitybutachievingthisusing predictive processes is relatively unknown. Figure 4 shows the Venn diagram used in section 2withtheinterfacemanagementtechniqueswhich arecompatiblewithpart-to-partassemblyclearly identified. It shows that both interchangeability and theuseofpredictiveprocessestoproducebespoke interfacesarecompatiblewithpart-to-part assembly,whiletheuseofin-assemblyfitting processestoproducebespokeinterfacesisnot compatible with one-way assembly. Figure 4 Venn Diagram Showing Compatibility of Interface Management Techniques with Part-to-part assembly 15 4. ONE-WAY ASSEMBLY One-way assembly is a process in which once parts areassembledtheyarenotremovedfromthe assembly;thereisnorequirementtopre-assemble, breakandreassemble.Onewayassemblyisa precondition for part-to-part assembly. Figure 5 Venn Diagram Showing One-Way Assembly in Relation to Part-to-Part Assembly and Interface Management In order to achieve one-way assembly the following conditions must be met:- Preassembly to measure gaps before carrying out interface management must not be required and therefore any bespoke interfaces which may be required for interface management must involve a predictive measurement assisted process where component measurements are used to predict gaps. Any hole drilling operations must not require de-burring between components or the breaking apart of assemblies to remove swarf. There must be sufficient confidence that an assembly will be right first time that sealant can be applied the first time components are assembled. Themajordifferencebetweenone-wayassembly and part-to-part assembly is therefore that in a one-way assembly some drilling through of components intheassemblyispermittedprovidedthisdoesnot require that the assembly is broken for cleaning and deburring.5. MEASUREMENT ASSISTED ASSEMBLY Thetermmeasurementassistedassembly(Kayani andJamshidi2007)isusedtorefertoanyprocess wheremeasurementsareusedtoguideassembly operations.Thisincludesbutisnotexclusiveto predictive interface management processes in which measurementsofremotepartsinterfacesareused to fettle or shim another component either before or duringassembly.Italsoincludesthetrackinginto positionofcomponentsusingmeasurementand processeswhereautomationoperatesunderclosed loopcontrolwithfeedbackfromanexternal metrology system. 5.1. ASSEMBLE-MEASURE-MOVE Anassemble-measure-move(AMM)processisone inwhichacomponentisapproximatelypositioned withinanassembly,itspositionisthenmeasured anditismovedintothecorrectposition.Thisis generallyaniterativeprocessinwhichcontinuous feedback is used to track a component into position. Generally this is not compatible with a fully part-to-part assembly process since once a component is located using an assemble-measure-move process it will then be necessary to drill through to fasten it in position.Itisofcoursepossibletoenvisagea processinwhichacomponentisfastenedinto positionusinganadjustableclampingarrangement butinpracticeforaerospacestructuresthisis unlikely. Thistechniqueisofinterestbecausealthoughit does not fully work within the goal of a part-to-part assemblyprocessitdoesallowtheaccurate placementofcomponentswithoutrequiring accurateassemblytooling.Itisthereforeauseful technique for certain difficult components within an assemblyinordertoreducetoolingcomplexityor asaget-outinaprimarilydeterminateassembly. Thesetechniquesareusedwithinfinalaircraft assemblyatwhichstagethestructureislargely interchangeable and determinate (explained below).6. ASSEMBLY TOOLING Assembly tooling is used to hold components and in the case of jigs to guide assembly machinery, during the assembly process. In the case of jigs and fixtures itincorporateshighlyaccuratecomponentlocators allowingthetoolingtodeterminetheformofthe emerging assembly. In the case of work holding it is thecomponentsthemselveswhichdeterminethe formoftheassembly(determinateassembly)and thereforethetoolingdoesnotrequireanyaccurate locators. The various forms of assembly tooling and associatedassemblymethodsaresummarizedin Figure 6 and described in detail below. 16 Figure 6 Assembly Tooling and Associated Assembly Methods 6.1. JIGS AND JIG BUILT STRUCTURES Traditionally aerospace structures are jig built; both overallformoftheassemblyandthepositionof assemblyfeaturessuchasholesaredeterminedby thejigwhichcontrolscomponentlocationandthe positioningofassemblymachinery.Ajigis thereforeaformofassemblytoolingwhich comprisesaccuratelocatorsforbothcomponents and assembly machinery. Itfollowsfromthesedefinitionsthatajigless assemblyisaprocesswhichdoesnotmeetallof theseconditions.Aprocesswhere fixturesareused to locate components therefore controlling the form of the assembly may be regarded as jigless provided thetoolingdoesnotalsocontrolmachinery positioning. 6.2. FIXTURES AND JIGLESS ASSEMBLY Jigless assembly within an assembly fixture follows essentiallythesameprocessasforajigbuilt structure. Components are still assembled within the tooling which controls the form of the assembly and in-assembly fitting processes are carried out. Thekeydifferenceisthatanassemblyfixtureis generallyverymuchsimplerthananassemblyjig sinceitisonlyrequiredtolocatecomponentsand notalsolocatemachineryforfettlinganddrilling. These functions are instead generally carried out by automationsuchasdedicateddrillingrobots equippedwithvisionsystems(Hoganetal.2003; Calawaetal.2004;Hempsteadetal.2006)or standardflexiblerobotswithexternalmetrology control(Summers2005;Muelaner,Kayanietal. 2011).Thereforeinjiglessassemblyalthoughalarge numberofoperationscontinuetobecarriedoutat late stages of the assembly process, these operations arecompletedmoreefficientlyandthesimpler toolingmeansthatlesscapitalisbeingtiedupin these operations. 6.3.ASSEMBLE-MEASURE-MOVEUSING WORK HOLDING TOOLING Asdiscussedabovetheassemble-measure-move techniqueisprobablynotsuitableforthecomplete assembly of an airframe but is a useful technique for certaincomponents.Itisessentiallyaformof fixture built assembly in which the fixture is a robot operatingunderclosedloopcontrolfromalarge volume metrology instrument. 6.4. DETERMINATE ASSEMBLY (DA) USING WORK HOLDING TOOLING Adeterminateassemblyisoneinwhichthefinal formoftheassemblyisdeterminedbytheformof itscomponentparts.Thelocationofcomponents interface features such as contacting faces and holes willthereforestronglyinfluencethefinalformof the assembly. It is often assumed that a determinate assembly must be made up of interchangeable parts but this is not necessarily the case since determinate assemblycanbeachievedusing,forexample, measurementassisteddeterminateassembly,see below. 6.4.1.DETERMINATEASSEMBLYWITHKING HOLES Kingholesareholeswhichareplacedspecifically tofacilitatedeterminateassembly.Inthisapproach alloftheholeswhichwillfinallybeusedtofasten componentsarenotplacedduringcomponent manufacturebutjustafewholesplacedinthe componentstofacilitateadeterminateassembly. Oncethecomponentshavebeenjoinedtogether usingthekingholestheactualstructuralholesare 17 drilledthroughthecomponentstackinthe conventional way. If required then the assembly can be broken apart, cleaned, deburred and reassembled. The king holes can also be drilled under size so that once the other structural holes have been drilled and temporaryfastenersfittedtothemthekinghole fasteners can be removed and full size holes drilled though to replace the king holes. Determinateassemblyusingkingholesis thereforeanintermediatesteptowardstheadoption of a fully part-to-part determinate assembly. 6.3.2.MEASUREMENTASSISTEDDETERMINATE ASSEMBLY (MADA) Measurementassisteddeterminateassemblyisa processinwhichmeasurementassistedpredictive processesareusedtocreatebespokeinterfaces.In general large components are measured and smaller bridging components are machined to interface with thelesswelldimensionallycontrolledlarger components. Thisallowsallinterfacemanagementtobe carriedoutatthecomponentmanufacturingstage and for a fully part-to-part assembly process to then take place. 6.3.3.DETERMINATEASSEMBLYWITH INTERCHANGEABLE PARTS Wheresufficienttolerancescanbeachievedfor fully interchangeable parts then this will lead to the minimumnumberofprocessstepsanda fullypart-to-partanddeterminateassembly.Thisisthe ultimate goal for any assembly process. 6.4. COMPATIBILITY WITH PART-TO-PART Anyformofassemblytoolingandassociated method,fromatraditionaljigbuiltapproachtoa fullydeterminateassembly,canbemadetobe compatiblewithone-wayassemblyifpredictive fettlingorshimmingiscombinedwithdrilling techniqueswhichdonotrequiredeburringor cleaning.Theonlyassemblymethodswhicharefully compatiblewithpart-to-partassemblyareMADA anddeterminateassemblywithinterchangeable parts. Figure 7 Compatibility of Assembly Tooling and Associated AssemblyMethods with One-Way and Part-to-Part Assembly 6.4. RECONFIGURABLE TOOLING Reconfigurabletoolinginvolvesconstructing assemblytoolingfromstandardcomponentswhich canbereadilyadjustedorrebuilttoaccommodate designchangesornewproducts(Kihlman2002). Thiscanbethoughtofasbeingsimilarto scaffolding.Itsolvestheproblemsofinflexibility inherentinrelianceonjigbuiltandfixturebuilt assemblyprocesses.Itdoesnothoweveralleviate issuesassociatedwithinterfacemanagement operationsandinparticulardrillingbeingcarried out at a late stage in assembly. 7.THEROADMAPTOPART-TO-PART ASSEMBLY Part-to-partassemblyinvolvescarryingthe maximumpossiblenumberofoperationsduring componentmanufacturing.Thismeansthattimeis notspentworkingoncomponentswithinthefinal assemblywhereahighlevelofcapitalexpenditure 18 is then tied up in these operations and a bottle neck to production exists. Itistheinterfacesbetweencomponentsurfaces andmatingholeswhichultimatelydeterminethe formofanyassembly,whetherithasbeenbuilt withinanassemblyjigorasadeterminate assembly.Part-to-partassemblyimpliesthatall holesandinterfacingsurfaceshavebeenprocessed totheirfinalformbeforeassemblytakesplace.It thereforefollowsthatthereisnopointinusingan assemblyjigorfixtureforapart-to-partassembly since it would have no influence on the form of the assemblyonceitwasreleasedfromthejig.Itis thereforepossibletostatethatachievingtruepart-to-partassemblywillrequireadeterminate assembly. There are two approaches identified as facilitating a fullypart-to-partassemblyprocess;MADAand determinateassemblyusinginterchangeableparts. Sincethekingholeapproachtodeterminate assemblyinvolvesthethroughdrillingofholes during assembly it is not fully compatible with part-to-part,itcouldhoweveractasanimportant intermediatesteptowardspart-to-partassembly usingMADA.Similarlypredictiveshimmingand fettling processes may be initially developed within ajiglessassemblyprocessandactasintermediate steps towards part-to-part assembly using MADA. Theultimateapproachtopart-to-partassembly throughthedeterminateassemblyof interchangeablepartswillultimatelybefacilitated throughdesignformanufacturewhichallows reducedcomponenttolerancesandmachinetool developmentwhichallowstightertolerancestobe produced. Althoughprocessesandtechnologiessuchas reconfigurable tooling, assemble-measure-move and orbitaldrillingmaybringimportantbenefitsinthe short term they are not seen as directly contributing to the development of part-to-part assembly. Thewayinwhichthevariousprocessesand technologiesdiscusseddoordonotcontributeto thedevelopmentofpart-to-partassemblyis illustrated in Figure 8 Figure 8 The Roadmap to Part-to-Part Assembly CONCLUSIONS Conceptssuchasinterfacemanagement,one-way assembly,interchangeability,part-to-partassembly, jiglessassemblyanddeterminateassemblyhave beenexplained.Therelationshipbetweenthese processeswasdetailedanditwasshownthat predictiveshimming,predictivefettling,designfor manufactureandtheuseofkingholeswillbeof particularimportanceinenablingpart-to-part assembly.Thesemethodswillhaverelevanceto otherindustriesbeyondtheaerospaceapplications discussedwherebespokeinterfacesarealso required.Examplesofsuchapplicationsinclude steelfabrication,boatbuildingandtheconstruction of power generation machinery. 19 ACKNOWLEDGEMENTS ThanksisgiventoCarlMasonofCMEngineering ServicesLtdforsupportindefiningthetermsused in this paper. REFERENCES Bullen,G.N.(1997)."TheMechanization/Automation ofMajorAircraftAssemblyTools."Production and Inventory Management Journal: 84-87. Calawa,R.,S.Smith,I.MooreandT.Jackson(2004). HAWDEFiveAxisWingSurfaceDrilling Machine.AerospaceManufacturingand AutomatedFasteningConferenceand Exhibition.St.Louis,Missouri,USA,SAE International. Hempstead,B.,B.ThayerandS.Williams(2006). Composite Automatic Wing Drilling Equipment (CAWDE).AerospaceManufacturingand AutomatedFasteningConferenceand Exhibition.Toulouse,France,SAE International: 1-5. Hogan, S., J. Hartmann, B. Thayer, J. Brown, I. Moore, J. Rowe and M. Burrows (2003). Automated Wing DrillingSystemfortheA380-GRAWDE.SAE AerospaceAutomatedFasteningConference andExhibition-2003AerospaceCongressand Exhibition.Montreal,Canada,SAE International. 112: 1-8. Kayani,A.andI.Gray(2009).ShimforArrangement AgainstaStructuralComponentandaMethod of Making a Shim, Airbus UK Ltd. Kayani,A.andJ.Jamshidi(2007).Measurement AssistedAssemblyForLargeVolumeAircraft WingStructures.4thInternationalConference onDigitalEnterpriseTechnology.P.G. Maropoulos. Bath, United Kingdom: 426-434. Kihlman,H.(2002).AffordableReconfigurable AssemblyTooling-AnAircraftDevelopment andManufacturingPerspective.Departmentof MechanicalEngineering.Linkping,Linkping Universitet: 111. Kihlman, H. (2005). Affordable Automation for Airframe Assembly.DepartmentofMechanical Engineering.Linkping,Linkpings Universitet: 286. Mitra,N.J.,N.Gelfand,H.PottmannandL.Guibas (2004).Registrationofpointclouddatafroma geometricoptimizationperspective. Eurographics/ACMSIGGRAPHsymposiumon Geometry processing, Nice, France. Muelaner,J.E.,A.KayaniandP.Maropoulos(2011). MeasurementAssistedFettlingofRibFeetto MaintainCoverInterfaceontheALCASWing BoxDemonstrator.SAE2011Aerotech Congress & Exposition. Toulouse, France, SAE. Muelaner,J.E.andP.G.Maropoulos(2008).Large ScaleMetrologyinAerospaceAssembly, Nantes, France. Muelaner,J.E.andP.G.Maropoulos(2010)."Design forMeasurementAssistedDeterminate Assembly(MADA)ofLargeComposite Structures." Journal of the CMSC 5(2): 18-25. Pickett,N.,S.Eastwood,P.Webb,N.Gindy,D. Vaughan and J. Moore (1999). A Framework to SupportComponentDesigninJigless Manufacturing.AerospaceManufacturing TechnologyConference&Exposition. Bellevue, Washington, SAE International: 1-4. Summers,M.(2005).RobotCapabilityTestand DevelopmentofIndustrialRobotPositioning SystemfortheAerospaceIndustry.AeroTech Congress&Exhibition.Grapevine,Texas,SAE International: 1-13. Proceedings of DET2011 7th International Conference on Digital Enterprise Technology Athens, Greece 28-30 September 2011 20 DIGITAL FACTORY ECONOMICS Johannes W. Volkmann1 1 Institute of Industrial Manufacturing and Management, University of Stuttgart Johannes.Volkmann@iff.uni-stuttgart.de Carmen L. Constantinescu1,2 2 Fraunhofer Institute for Manufacturing Engineering and Automation IPA Carmen.Constantinescu@ipa.fraunhofer.de ABSTRACT Most factories already have a more or less adequate grasp on what a Digital Factory is, but hardly realisethepossiblebenefitsthatanimplementationmayachieve.Still,especiallyforSMEs,the evaluationofsituationbasedimplementationscenariosofdigitaltoolsinthecontextofaDigital Factory is an insurmountable challenge and often keeps potential users from a further investigation. Themainchallengeisthelackofacceptedandappropriatemethodsandtoolstoevaluatethe economicefficiency,effectivenessandthereforetheexpectedbenefitsoftheemploymentof integrated digital tools. This evaluation needs to address a selection of digital tools and needs to be scalabletobeabletoevaluatedifferentalternativeimplementationscenarios.Thispaperpresents thefoundationsandthefirststepsaimingatthedevelopmentofascalablemethodologyforthe evaluation and therefore the selection of suitable digital tools. KEYWORDS Digital Factory, Digital Tools, DF, Economics, Process Modeling 1. INTRODUCTION Due to the increasing globalisation of production in thelastdecade,manyfactoriesmodernisedor replacedtheirmethodsanddigitaltoolsusedfor factoryplanningandthecontinuousoptimisation. Mostofthesesystemshavebeenheavilyadapted andarenowanintegralpartofthedigitalfactory. Consideringalifecycleofthemethodsanddigital tools,replacingthemisessentialintheupcoming years(Buchtaetal,2009).Duetothecrisis,these replacementsoftenhavebeenhaltedandthe existingsystemsoftenarebehindthepossibilities, thecurrentlyavailabletechnologiesmayprovide. Withtheendofthecrisisfactoriesareonceagain concentrating on the challenges they face, especially theincreaseinnecessaryinterconnectionsforthe informationanddataexchangeaswellasthe amountofdatathatisgeneratednow.Considering thesechallenges,keepingcontroloftheconstant planningtasksandatthesametime,keepingthe planningprocessesflexibleandfastisdifficultto achieve.Oneofthepossibleanswerstogetcontrol ofthiscomplexityandaddressthesechallengesis the usage of methods and digital tools in the context ofaDigitalFactory.Theyaimatkeepingthe factorycompetitive,byintroducinganewdepthof control. There is a plethora of tools to choose from, allbringingtheirspecificfunctions,risksand implicationstothetable.Selectingthesuitable methodortooltofitintotheexistingsystemsand addressingthespecificneedsofonefactoryis difficult,thereforethereisstillalotofpotentialto berealised,especiallyinsmallandmedium enterprises (SME). Thispaperpresentsthefoundationsandthefirst stepsaimingatthedevelopmentofascalable method for the evaluation and selection of situation-suitable methods and digital tools in the context of a digital factory.2. MOTIVATION Theplanningprocessesinafactoryarebecoming increasinglycomplex.Ontheonehandtheproduct worldchangesatanincreasingpace,creatingthe necessityofflexibleproductionsystems.Aflexible productionsystemisdefinedbyitscapabilityand ease to accommodate changes in the system (Karsak andTolga,2000).Itenablesaproductionwhichis 21 costeffective,butatthesametimeabletohandle highlycustomisedproducts(GuptaandGoyal, 1989). On the other hand the globalisation increases thecomplexityoftheplanningitself.Withthe analogytotheproductlifecycle,Westkmperetal (2006)statedthenewparadigmtheFactoryisa product.Thisinducedaholisticapproachtothe lifecycleofafactory.Thedifferentphasesofthe planning processes have been ordered in the factory lifecycleapproach(Constantinescuand Westkmper,2008).Itdividesthephasesofa factoryslifeintofourgroups,strategy,structure, process and operation (Figure-1). Maintenance and EquipmentManagement ProductDevelop-mentSite and Network Planning Investment and PerformancePlanning Buildings,Infrastructure andMedia Planning Internal LogisticsandLayout PlanningProcess,Equipment andWorkplacePlanning Ramp-upand ProjectManagement Factory OperationDigitalFactory Fraunhofer IPA Figure 1 The factory life cycle management phases (Constantinescu et al, 2009) The aforementioned groups are ordered and defined accordingtotheirgranularityoftheinformation usedintheaccordingphase.Insidethosefour groups, ten phases in total are identified to cover the whole life of a factory, ranging from the investment andperformanceplanningtotheoperationandthe final dismantling (Westkmper, 2008). Thereisaplethoraofdifferentmethodsand digitaltoolsavailabletooffersupportforthese phases(Figure-2).Thegroupsarecreatedand scaledconsideringtheincreasinggranularityofthe data along the factory life cycle. The first phases are in the group strategy, generally supported by tools likemanufacturingresourceplanning(MRP)or production planning and scheduling (PPS). The second group structure covers the planning phasesofthefactorylifecyclefromthesiteand networkplanninguptopartsoftheprocess planning.Thethirdgroupprocessdetailsthe informationfurtherandcoversmostoftheprocess planningaswellastheequipmentplanning.Italso includestheergonomyandworkplaceplanning. Thegroupoperationincludesalldetailed informationandreachesfromtheramp-uptothe demantlingandrecycling.Someofthesemethods and digital tools are specifically designed to support veryspecifictasksinsinglephases,whileothers addressawholephaseoreventhecombinationof differentphases.Thiscreatesacomplexityin selectingthesuitablemethodsanddigitaltoolsto address the challenges arising in a specific factory. FEMCAE / CADCAPPCAP / CAQProcesssimulationMESPDEVIBNCAM / NCMRP / PPSFDMCAO/PLM/FLM Fraunhofer IPALegendMRP - Manufacturing Resource PlanningPPS - Production Planning and controlSystemFEM - Finite Elements methodCAE - Computer aided engineeringCAD - Computer aided designMES - Manufacturing execution systemPDE - Production data acquisitionVIBN - Virtual plant commissioningCAM Computer aided manufacturingNC Numerical controlCAPP Computer aided production planningCAP Computer aided productionCAQ Computer aided qualityFDM Factory data managementCAO Computer aided organisationPLM Product lifecycle managementFLM Factory lifecycle management Figure 2 Digital tools supporting the factory life cycle management (Westkmper, 2008) A factor that is most of the time even increasing the complexityoftheselectionprocessarethelegacy systems.Mostofthefactoriesdoalreadyuse methodsanddigitaltoolsatleastforsomeoftheir planning tasks. These are called legacy systems and havetobeconsideredintheselection.They increasethecomplexityasthereareseveral possibilitiesonhowtheycaninfluenceanew selection.Theycaneitherbecompletelyreplaced, beimprovedtobeabletohandlenewchallenges (e.g. using new software modules or integrating new interfaces)ortheycanbeintegratedwithnew, complementarysystems.TheydefinetheAs-is situation,influencingthesystemselectionthrough e.g.theexistingdataandtheexistingplanning workflow. Considering interfaces to legacy systems canbecrucialinordertoensureasmooth introduction,acceptanceamongsttheusersandthe long term usability. 22 Thepotentialbenefitsofusingmethodsof industrialengineeringanddigitaltoolsinthe contextofadigitalfactoryarenotinquestionand acknowledgedbymostfactories(Bierschenkatal, 2004). It is generally accepted that there are relevant potentialstoberealised,butespeciallySMEsoften stoptheirapproachtothesetechnologieswhen encounteringthedifferencesintryingtoevaluate thepotentialbenefitsandselecttheidealmatchfor theirspecificsituation.Themethodsanddigital toolsusedfortheplanningofthefactorieshavea hugeimpactonthevalueaddingoperationofa factory and their initial as well as their maintenance costscanbesubstantial.Thisinduces,thatatrial and error approach is not an option. Tosynchronizethethreegoalsofthefactory planningactivitiesdecreaseoftime,moneyand increase of quality is at first difficult to see, using adigitalfactory.Consideringtheinitialcosts, showingthedecreaseofplanningcostsismostof thetimesnotpossible.Takingintoaccountthe wholeproductcosts,includingtheallocated productionandthustheproductionplanningcosts, howevermaychangethispicture.Avoiding unnecessaryworkandincreasingthequalityofthe planning may be able to lessen the overall expenses eveniftheinitialplanningcostsusingthemethods anddigitaltoolsofadigitalfactorymaybehigher thanwhenbeingdonemanually.Thegeneral qualityofplanningistheharmonisationand optimisationoftheprocessofproductionplanning byconsideringe.g.theinformationmanagement, thedataandtheseamlessintregrationofproduct developmentandproductionplanning(VDI4499, 2008).Thesetwopoints,thedifficulttoassess suitablesystemselectionandthesometimes complexcostsituation,inducetheneedfora methodtosupporttheanalysis,selectionand introductionofsuitablemethodsanddigitaltools forthefactoryspecificlyoccuringplanningtasks. Thismethodfortheselectionofthesuitable methods and digital tools and the evaluation of their economicsinadigitalfactorywillconsiderboth, the integration and operation. It needs to be generic inordertobeabletocovertheverydivergent planning phases. To be able to be used for SMEs as wellasbiginternationalfactories,itneedstobe scalable.Inanextparagraph,themostimportant usedtermsinthispaperareclarifiedandconfined. Economic efficiency in the general field of business administration is usually defined as the value of the outputdividedbythevalueoftheinputoras earningsdividedbyexpense(WheandDring, 2010b).Thisdefinitionfocusesonthefinancial aspects.InthecaseofITinvestments,onlytaking the financial aspects into account is most of the time notsufficient,assomeofthebenefitsthattheIT creates are very difficult to quantify and therefore to measure.Thisisespeciallythecase,ifnotonly singulardigitaltoolsareconsidered,butaplethora of tools in the context of a digital factory (Bracht et al, 2011). Therefore, in this paper, we use the more opendefinitionofeconomicsascosts,dividedby benefits (Vajna et al, 2009), as shown in equation-1. benefitcosteconomics = (1) The benefit as denominator implies a quantification ofthebenefits,whichcanbeverydifficult especially when trying to cover complex coherences liketheyexistinthefieldofmethodsanddigital toolsinthecontextofadigitalfactory.Theterm digitaltoolisavaryingone.Thetermislimited here to the methods and tools specific to the factory planning.Adigitalfactoryusuallycoversthe factoryoperationaswell,butinthiscaseitisnot explicitlytakenintoaccount,asthefieldofdigital toolsthereisvastandcannotbeconsideredin sufficientdepth.ThemethodDFapproachedin thispaper,needstobegenericandistherefore applicabletothefactoryoperationaswell,ifthe databasisisgenerated.Digitaltoolsinthis approachwillnotbereflectedonthescaleof specificsoftware(e.g.SiemensNX),butonthe systemscale.Inourapproach,theconsidered systemscaleistheclassification,e.g.CAD,CAQ, CAM. TheFraunhoferInstituteforManufacturing EngineeringandAutomation(IPA)hastheGrid EngineeringforManufacturingLaboratory2.0 (GEMLab2.0)available,whichisthe implementationofthegrid-flow-basedapproachto aholisticandcontinuouslyintegratedfactory engineeringanddesign.Itusesacombinationof commercial and self developed tools, integrated into theGridEngineeringArchitecturethrougha standardisedservice.Foritsvalidation,anexample productwasdesigned,onwhichacontinuous factory planning and optimisation scenario is based. This scenario includes all requiredfactoryplanning steps (Constantinescu et al, 2009). In this paper, this basis is used to imagine an example scenario, where in the existing GEMLab, the digital tool used for the processplanningstepiswishedtobeupdatedor replaced.Thisisusedasahypotheticalcaseto clarifythestepsoftheDFmethodpresentedin chapter four. 3. STATE OF THE ART Thestateoftheartforthisapproachisstructured into three parts. The first part is the confinement of 23 theconsideredmethodsinthisstateoftheart.The secondpartshowsthestateoftheartinevaluation methodsforeconomics.Thethirdpartisdedicated to the method developing systematics and languages needed for the approach DF.Therearetwokeyaspectsthatmaybeseenas basic properties of every evaluation method: Thetimeframe:Itcaneitherbeexanteorex post, depending if the evaluation is done before oraftertheinvestmenthastakenplace(Whe and Dring, 2010a). Thesubject:Theevaluationcanbeeither partial/singularoraportfolio/selectionof investment objects (Adam, 1999). In the case of the method to be approached in this paper,aportfolioconsistingofseveralmethodsis the object of evaluation. Themethodsfortheevaluationofeconomicsin general can be divided into two groups (Schabacker, 2001): Comparativecalculations,whichcompareif actionswithoutinvestments,e.g.changesina production process, are expedient. Investment calculation, which try to evaluate if investments in a certain matter are expedient. In this case, investment calculations are relevant, as the method DF is about investments in methods anddigitaltoolsinthecontextofadigitalfactory. Themethodneedstobespecifiedinrespecttothe evaluatedtimeframeandtheobjectofevaluation. Investmentcalculationcanbedivideditselfinto severaltypesofmethods.Threeapprovedand established types are: Staticinvestmentcalculations:Static investmentcalculationsarebasedonaverage costsandrevenueoveronedefinedperiodof time.Allinputisgatheredforonecalldate. The differing value of money depending on the time scale is therefore not considered. They are themostcommontypeofinvestment calculations,astheeffortforthedata acquisition is usually lower than with the other investmentcalculationtypes(Perridonand Steiner,1995).Typicalstaticmethodsaree.g. thepaybackperiodrule,thecomparativecost methodandtheROIreporting(VDI 2216, 1994). Dynamicinvestmentcalculations:Dynamic investmentcalculationstakeintoaccountthe changingvalueofmoneyovertime.They coverseveralperiodsoftime,consideringthe costsandexpensesforeachandputtingitinto relationtotheaccordingcashvalueandthe generatedrevenue.Therefore,ittakesinto accountthetimingofthecashflowandits differing value over time (Perridon and Steiner, 1995).Theeffortfordynamicinvestment calculationsissignificantlyhigherthanforthe staticinvestmentcalculations,duetothe informationneededforeveryaccounting period.Typicaldynamicmethodsaree.g.the netpresentvaluemethod,theannuitymethod andthemethodofactuarialreturn(VDI 2216, 1994).Cost-benefit-analysis:Thisanalysisisa comparisonofobjectsordifferentalternatives foradecision,basedonpurelyfinancial aspects. It takes into account the comparison of thediscountedinvestmentsinthefuture.The evaluationscalesofthecostsandbenefitsas wellastheextentoftheconsideredfactors cannotbeobjectivelydefined(Venhoffand Grber-Seissinger, 2004; Schabacker, 2001). Thesethreetypesofinvestmentcalculationsare entirelyfocusedonfinancialaspects.Therefore, they are at best only partly able to support decision takingiftherearesubstantialeffectsofthe investmentwhicharenotfinancal.Inthenewer business management approaches, which lead to the developmentofmorespecialisedmethodstaking intoaccountnotonlyfinancialaspectsbuttomix themwithother,butquantifiableaspects.Avery commonly used method here is the value analysis or scoringmodel.Itcreatesacomparisonof alternativeswithquantifiednon-financialfactors.It enablestheusertocomparecomplexalternatives takingintoaccounthispredefinedweightingofthe consideredfactors.Therearedifferentmethodsto approachareplicableweighting,whichcanbe appliedhere(e.g.pairedcomparison).Evenifthe procedurebecomesintricatingwithanincreasing number of factors, it is a very common method. The result,thesumoftheweightedvaluesforevery factor,istheordinalorderofprecedence (Zangemeister,1976a).Thisimplicatesthatthe result is not suitable to be put into relation with e.g. the total costs or the expected proceeds, which often precludesitsuseforaninvestmentdecision. Additionally,itispossibletointegrateastatistical confiden