The International Journal of Flexible Manufacturing Systems, 16, 11–44, 2004 c 2004 Kluwer Academic Publishers. Manufactured in The Netherlands.
Time-Based Competition in Multistage Manufacturing: Stream-of-Variation Analysis (SOVA) Methodology—Review D. CEGLAREK firstname.lastname@example.org W. HUANG email@example.com S. ZHOU firstname.lastname@example.org Department of Industrial Engineering, University of Wisconsin-Madison, Madison, WI 53706-1572, USA Y. DING YuDing@iemail.tamu.edu Department of Industrial Engineering, Texas A&M University, College Station, TX 77843, USA R. KUMAR Y. ZHOU Dimensional Control Systems, Inc., Troy, MI 48084, USA email@example.com firstname.lastname@example.org
Abstract. Frequency of model change and the vast amounts of time and cost required to make a changeover, also called time-based competition, has become a characteristic feature of modern manufacturing and new product development in automotive, aerospace, and other industries. This paper discusses the concept of time-based competition in manufacturing and design based on a review of on-going research related to stream-of-variation (SOVA or SoV) methodology. The SOVA methodology focuses on the development of modeling, analysis, and control of dimensional variation in complex multistage assembly processes (MAP) such as the automotive, aerospace, appliance, and electronics industries. The presented methodology can help in eliminating costly trial-and-error ﬁne-tuning of new-product assembly processes attributable to unforeseen dimensional errors throughout the assembly process from design through ramp-up and production. Implemented during the product design phase, the method will produce math-based predictions of potential downstream assembly problems, based on evaluations of the design and a large array of process variables. By integrating product and process design in a pre-production simulation, SOVA can head off individual assembly errors that contribute to an accumulating set of dimensional variations, which ultimately result in out-of-tolerance parts and products. Once in the ramp-up stage of production, SOVA will be able to compare predicted misalignments with actual measurements to determine the degree of mismatch in the assemblies, diagnose the root causes of errors, isolate the sources from other assembly steps, and then, on the basis of the SOVA model and product measurements, recommend solutions. Key Words: variation reduction, quality, root cause identiﬁcation, manufacturing systems
Introduction: Time-based-competition—New paradigm and challenges
The US automotive industry has dominated world auto markets for years. The mass production paradigm, initiated by Henry Ford and Frederick W. Taylor, has been the most powerful tool for the United States in global markets for almost half of the last century. However, the landscape has shifted dramatically from the old world of mass production, which was characterized by few standardized products, homogeneous markets, and long product life
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cycle and development lead time. A new standard is gradually emerging in which increased customization, product proliferation, heterogeneous markets, shorter product life cycle and development time, responsiveness, and related factors are increasingly becoming key features (Bollinger, 1998; Gervin and Barrowman, 2002). For example, in Japan, Toyota was reportedly offering customers ﬁve-day delivery from the time the customer designed a customized car on a CAD system (from modular options) to the actual product delivery. One of the characteristic features of the automotive industry is the frequency of model change and vast amounts of time and cost that are required to make a changeover. This trend has continuously gone up in the last two decades. Since 1980s, US car market’s total demand has essentially remained stable, but the number of nameplates has increased by 35% from 139 to 183, respectively. This increase continued during the 1990s...
References: Apley, D. W. and Shi, J., “Diagnosis of Multiple Fixture Faults in Panel Assembly,” ASME Journal of Manufacturing Science and Engineering, Vol. 120, pp. 793–801 (1998). Baron, J., “Dimensional Analysis and Process Control of Body-In-White Processes,” Ph. D. Dissertation, University of Michigan, Ann Arbor, MI (1992). Bollinger, J. E. (Ed.), Visionary Manufacturing Challenges for 2020, Committee on Visionary Manufacturing Challenges, National Research Council, National Academy Press, Washington, DC (1998). Cai, W., Hu, S. J., and Yuan J. X., “Deformable Sheet Metal Fixturing: Principles, Algorithms, and Simulations,” Transactions of ASME, Journal of Manufacturing Science and Engineering, Vol. 118, No. 3, pp. 318–324 (1996). Camelio, J., Hu, S. J., and Ceglarek, D., “Modeling Variation Propagation of Multi-Station Assembly Systems with Compliant Parts,” Transactions of ASME, Journal of Mechanical Design, Vol. 125, No. 4, pp. 673–681 (2003). Ceglarek, D., “Knowledge-Based Diagnosis for Automotive Body Assembly: Methodology and Implementation,” Ph.D. Dissertation, University of Michigan, Ann Arbor, MI (1994). Ceglarek, D. and Shi, J., “Dimensional Variation Reduction for Automotive Body Assembly Manufacturing,” Manufacturing Review, Vol. 8, No. 2, pp. 139–154 (1995). Ceglarek, D. and Shi, J., “Fixture Failure Diagnosis for Auto Body Assembly Using Patter Recognition,” ASME Transactions, Journal of Engineering for Industry, Vol. 118, No. 1, pp. 55–65 (1996). Ceglarek, D. and Shi, J., “Design Evaluation of Sheet Metal Joints for Dimensional Integrity,” Transactions of ASME, Journal of Manufacturing Science and Engineering, Vol. 120, No. 2, pp. 452–460 (1998).
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Ceglarek, D., Shi, J., and Wu, S. M., “A Knowledge-Based Diagnosis Approach for the Launch of the Auto-Body Assembly Process,” Transactions of ASME, Journal of Engineering for Industry, Vol. 116, No. 4, pp. 491–499 (1994). Chase, K. W. and Parkinson, A. R., “A Survey of Research in the Application of Tolerance Analysis to the Design of Mechanical Assemblies,” Res. Eng. Design, Vol. 3, pp. 23–37 (1991). CIRP, “Flexible Automation—Assessment and Future,” CIRP Scientiﬁc Technical Committee Survey in USA, Europe and Japan (January 27, 2000). DCS, 3D Variation Simulation, Dimensional Control Systems, Inc. (2000). Ding, Y., Ceglarek, D., and Shi, J., “Modeling and Diagnosis of Multistage Manufacturing Process: Part I State Space Model,” Proceedings of 2000 Japan–USA Symposium on Flexible Automation, July 23–26, Ann Arbor, MI, 2000JUSFA-13146 (2000). Ding, Y., Ceglarek, D., and Shi, J., “Design Evaluation of Multi-station Assembly Processes by Using State Space Approach,” ASME Transactions, Journal of Mechanical Design, Vol. 124, No. 3, pp. 408–418 (2002a). Ding, Y., Ceglarek, D., and Shi, J., “Fault Diagnosis of Multistage Manufacturing Processes by Using State Space Approach,” ASME Transactions, Journal of Manufacturing Science and Engineering, Vol. 124, No. 2, pp. 313–322 (2002b). Ding, Y., Shi, J., and Ceglarek, D., “Diagnosability Analysis of Multistage Manufacturing Processes,” ASME Transactions, Journal of Dynamic Systems, Measurement, and Control, Vol. 124, No. 1, pp. 1–13 (2002c). Ding, Y., Kim, P., Ceglarek, D., and Jin, J., “Optimal Sensor Distribution for Variation Diagnosis in Multi-Station Manufacturing Processes,” IEEE Transactions on Robotics and Automation, Vol. 19, No. 4, pp. 543–556 (2003). El-Gizawy, A. S., Hwang, J.-Y., and Brewer, D. H., “A Strategy for Integrating Product and Process Design of Aerospace Components,” Manufacturing Review, Vol. 3, No. 3, pp. 178–186 (1990). ERC-RMS, National Science Foundation–Engineering Research Center for Reconﬁgurable Manufacturing Systems (ERC-RMS), Technical Report, University of Michigan (1999). Gadh, R., “A Hybrid Approach to Intelligent Geometric Design Using Features-Based Design and Feature Recognition,” Proceedings of the 19th Annual ASME Design Automation Conference, Vol. DE-65, No. 2, pp. 273–283 (1993). Gerwin, D. and Barrowman, N. J., “An Evaluation of Research on Integrated Product Development,” Management Science, Vol. 48, No. 7, pp. 938–953 (2002). Greer, D., “On-line Machine Vision Sensor Measurements in a Coordinate System,” SME Paper #IQ88–289 (1988). Hu, S. J., “Impact of 100% Measurement Data on Statistical Process Control (SPC) in Automobile Body Assembly,” Ph.D. Dissertation, University of Michigan, Ann Arbor, MI (1990). Hu, S. J., “Stream-of-Variation Theory for Automotive Body Assemblies,” Annals of CIRP, Vol. 46, No. 1, pp. 1–6 (1997). Hu, S. and Wu, S. M., “Identifying Root Causes of Variation in Automobile Body Assembly Using Principal Component Analysis,” Transactions of NAMRI, Vol. 20, pp. 311–316 (1992). Huang, W., Kong, Z., Ceglarek, D., and Brahmst, E., “The Analysis of Feature-Based Measurement Error in Coordinate Metrology,” IIE Transactions on Design and Manufacturing, Vol. 36, No. 3, pp. 237–251 (2004). Jin, J. and Shi, J., “State Space Modeling of Sheet Metal Assembly for Dimensional Control,” ASME Transactions, Journal of Manufacturing Science & Engineering, Vol. 121, pp. 756–762 (1999). Khan, A., Ceglarek, D., and Ni, J., “Sensor Location Optimization for Fault Diagnosis in Multi-Fixture Assembly Systems,” Transactions of ASME, Journal of Manufacturing Science and Engineering, Vol. 120, No. 4, pp. 781– 792 (1998). Khan, A., Ceglarek, D., Shi, J., Ni, J., and Woo, T. C., “Sensor Optimization for Fault Diagnosis in Single Fixture Systems: A Methodology,” ASME Transaction, Journal of Manufacturing Science and Engineering, Vol. 121, pp. 109–117 (1999). Koren, Y., Heisel, U., Jovane, F., Moriwaki, T., Pritschow, G., Ulsoy, G. A., and Brussel, H., “Reconﬁgurable Manufacturing Systems,” Annals of CIRP, Vol. 50, No. 2 (1999). Lawless, J. F., Mackay, R. J., and Robinson, J. A., “Analysis of Variation Transmission in Manufacturing Processes—Part I,” Jouranal of Quality Technology, Vol. 31, pp. 131–142 (1999). Mantripragada, R. and Whitney, D. E., “Modeling and Controlling Variation Propagation in Mechanical Assemblies Using State Transition Models,” IEEE Transactions on Robotics and Automation, Vol. 15, pp. 124–140 (1999).
CEGLAREK ET AL.
Mehrabi, M. G., Ulsoy, A. G., and Koren, Y., “Reconﬁgurable Manufacturing Systems: Key to Future Manufacturing,” Journal of Intelligent Manufacturing, Vol. 11, No. 4, pp. 403–419 (2000). Menassa, R. J. and DeVries, W. R., “Locating Point Synthesis in Fixture Design,” Annals of CIRP, Vol. 38, No. 1, pp. 165–169 (1989). Parkinson, A., Sorensen, C., and Pourhassan, N., “A General Approach for Robust Optimal Design,” Transactions of ASME, Journal of Mechanical Design, Vol. 115, No. 1, pp. 74–80 (1993). Perceptron, 1000, “Measurement System Manual,” Perceptron Inc. (1991). Sekine, Y., Koyama, S., and Imazu, H., “Nissan’s New Production System: Intelligent Body Assembly System,” SAE Technical Paper Series, 910816, pp. 1–12 (1991). Shalon, D., Gossard, D., Ulrich, K., and Fitzpatrick, D., “Representing Geometric Variations in Complex Structural Assemblies on CAD Systems,” Proceedings of the 19th Annual ASME Advances in Design Automation Conference, Vol. DE-44, No. 2, pp. 121–132 (1992). Suri, R., Quick Response Manufacturing: A Company wide Approach to Reducing Lead Times, Productivity Press (1998). Takezawa, N., “An Improved Method for Establishing the Process-Wise Quality Standard” Rep. Stat. Appl. Res., JUSE, Vol. 27, No. 3, pp. 63–75 (1980). Thomke, S. and Fujimoto, T., “The Effect of “Front-Loading” Problem-Solving on Product Development Performance,” Journal of Production Innovation Management, Vol. 17, No. 2, pp. 128–142 (2000). Ulrich, K., Sartorius, D., Pearson, S., and Jakiela, M., “Including the Value of Time in Design-for-Manufacturing Decision-Making,” Management Science, Vol. 39, No. 4, pp. 429–447 (1993).
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