Abstract
Recently, many manufacturing industries have adopted the methods of “Key Characteristics” (KCs) to identify and analyze product and process critical attributes which need extra control on several levels: product, assembly, sub-assembly, part and process to trace manufacturing variations. Manufacturing variations are those unwanted deviations from nominal values which significantly impact product’s quality, performance and cost. However, those manufacturers face some challenges in the implementation of such procedures. This is due to lack of quantitative models to prioritize those (KCs) and quantify their associated risk of variation. Therefore, there is a need for proactive quantitative mechanisms which incorporate knowledge about the current process capability by communicating Process Capability Data (PCD) during the early design stages to reduce design’s sensitivity to manufacturing variations. The present work builds a systematic interactive environment between the design model and the current process capabilities to analyze PCD which processed and stored in designated databases along with proactive mechanisms to capture the impact of manufacturing variations on performance. It prioritizes and quantifies expected future variations due to possible deviations of the design parameters from their nominal values to assess the related risk of variation. This study comes under the broadest risk management procedure to establish for a novel variation risk management methodology works in an interactive design and manufacturing environment. A case study of a connector beam for an edge card has been carried out successfully to prove the effectiveness of the present methodology in quantifying manufacturing variations and assessing their associated risk of variation.
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Aziz, E., Chassapis, C.: Comparative analysis of tooth-root strength using stress-strength interference (SSI) theory with FEM-based verification. Int. J. Interact. Design Manuf. (IJIDeM) 8(3), 159–170 (2014)
Zhang, Y.M., Liu, Q., Wen, B.: Practical reliability-based design of gear pairs. J. Mech. Mach. Theory 38(12), 1363–1370 (2003)
Clausing, D., Frey, D.: Improving system reliability by failure-mode avoidance including four concept design strategies. Syst. Eng. J. 8(3), 245–261 (2005)
Taguchi, G., Clausing, D.: Robust design, HBR, Harvard Business Review. Retrieved from: https://hbr.org/1990/01/robust-quality
Abraham, B., MacKay, J.: Variation reduction and designed experiments. Int. Stat. Rev. Spec. Issue Stat. Ind. 61(1), 121–129 (1993)
Bailar, B.A.: Quality issues in measurement. Int. Stat. Rev. Revue Int. Stat. 53(2), 123–139 (1985)
Clausing, D.: Reusability in product development. Engineering Design Conference, Uxbridge, England (1998)
Kern, D., Thornton, A.: Structured indexing of process capability data. In: ASME Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Montreal, Canada (2002)
Tata, M., Thornton, A.C.: Process capability database usage in industry: myth vs. reality. In: ASME Design Engineering Technical Conference, Las Vegas, Nevada, USA (1999)
Johansson, P., Chahkunashvili, A., B, S., Bergman, B.: Variation mode and effects analysis: a practical tool for quality improvement, quality and reliability engineering. Int. J. 22(8), 865–876 (2006)
Dantan, J.Y., Hassan, A., Etienne, A., Siadat, A., Martin, P.: Information modeling for variation management during the product and manufacturing process design. Int. J. Interact. Des. Manuf. 2(2), 107–118 (2008)
Thornton, A.C., Donnelly, S., Ertan, B.: More than just robust design: why product development organizations still contend with variation and its impact on quality. Res. Eng. Des. J. 12(3), 127–143 (2000)
Thornton, A.C.: A mathematical framework for the key characteristics process. Res. Eng. Des. J. 11(3), 145–157 (1999)
Delaney, K.D., Phelan, P.: Design improvement using process capability data. J. Mater. Process. Technol. 209(1), 619–624 (2009)
He, X., Oyadiji, S.O.: Application of coefficient of variation in reliability-based mechanical design and manufacture. J. Mater. Process. Technol. 119(1), 374–378 (2001)
Lee, D.J., Thornton, A.C.: The identification and use of key characteristics in the product development process, pp. 1–12. In: Proceedings of the ASME Design Engineering Technical Conference, Irvine, CA (1996)
Shi, X., Chen, J., Yang, H., Peng, Y., Ruan, X.: A novel approach to extract knowledge from simulation results. Int. J. Adv. Manuf. Technol. 20(1), 390–396 (2002)
Anderson, D., Sweeny, D., Williams, T.: Statistics for business and economics, 10th edn. Thomson South-western, Mason (2008). ISBN-10: 0324658370
Braha, D.: Data Mining for Design and Manufacturing. Springer, pp. 544, ISBN: 1-4020-0034-0 (2002). http://necsi.edu/affiliates/braha/PREFACE_DM.pdf
Zhaofeng, H., Yan, J.: Extension of stress and strength theory for conceptual design for reliability. J. Mech. Des. 131(7), 071001 (2009). doi:10.1115/1.3125885
Demoly, F., Monticolo, D., Eynard, B., Rivest, L., Gomes, S.: Multiple viewpoint modelling framework enabling integrated product-process design. Int. J. Interact. Des. Manuf. (IJIDeM) 4(4), 269–280 (2010)
Abraham, B., MacKay, J.: Variation reduction and designed experiments. Int. Stat. Rev. 61(1), 121–129 (1993)
Fosso-Tande, J.: Applications of Taylor series. Retrieved from http://sces.phys.utk.edu/~moreo/mm08/fosso.pdf (2014)
Booker, J.M., Ross, T.J.: An evolution of uncertainty assessment and quantification. Sci. Iran. 18(3), 669–676 (2011)
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Ibrahim, I.H., Chassapis, C. An interactive variation risk management environment to assess the risk of manufacturing variations. Int J Interact Des Manuf 11, 597–608 (2017). https://doi.org/10.1007/s12008-016-0330-7
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DOI: https://doi.org/10.1007/s12008-016-0330-7