Skip to main content

Advertisement

Log in

An interactive variation risk management environment to assess the risk of manufacturing variations

  • Original Paper
  • Published:
International Journal on Interactive Design and Manufacturing (IJIDeM) Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. 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)

    Article  Google Scholar 

  2. Zhang, Y.M., Liu, Q., Wen, B.: Practical reliability-based design of gear pairs. J. Mech. Mach. Theory 38(12), 1363–1370 (2003)

    Article  MATH  Google Scholar 

  3. Clausing, D., Frey, D.: Improving system reliability by failure-mode avoidance including four concept design strategies. Syst. Eng. J. 8(3), 245–261 (2005)

    Article  Google Scholar 

  4. Taguchi, G., Clausing, D.: Robust design, HBR, Harvard Business Review. Retrieved from: https://hbr.org/1990/01/robust-quality

  5. Abraham, B., MacKay, J.: Variation reduction and designed experiments. Int. Stat. Rev. Spec. Issue Stat. Ind. 61(1), 121–129 (1993)

    Google Scholar 

  6. Bailar, B.A.: Quality issues in measurement. Int. Stat. Rev. Revue Int. Stat. 53(2), 123–139 (1985)

    Article  MathSciNet  Google Scholar 

  7. Clausing, D.: Reusability in product development. Engineering Design Conference, Uxbridge, England (1998)

  8. 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)

  9. 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)

  10. 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)

  11. 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)

    Article  Google Scholar 

  12. 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)

    Article  Google Scholar 

  13. Thornton, A.C.: A mathematical framework for the key characteristics process. Res. Eng. Des. J. 11(3), 145–157 (1999)

    Article  Google Scholar 

  14. Delaney, K.D., Phelan, P.: Design improvement using process capability data. J. Mater. Process. Technol. 209(1), 619–624 (2009)

    Article  Google Scholar 

  15. 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)

    Article  Google Scholar 

  16. 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)

  17. 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)

    Article  Google Scholar 

  18. Anderson, D., Sweeny, D., Williams, T.: Statistics for business and economics, 10th edn. Thomson South-western, Mason (2008). ISBN-10: 0324658370

  19. 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

  20. 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

    Article  Google Scholar 

  21. 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)

    Article  Google Scholar 

  22. Abraham, B., MacKay, J.: Variation reduction and designed experiments. Int. Stat. Rev. 61(1), 121–129 (1993)

    Article  Google Scholar 

  23. Fosso-Tande, J.: Applications of Taylor series. Retrieved from http://sces.phys.utk.edu/~moreo/mm08/fosso.pdf (2014)

  24. Booker, J.M., Ross, T.J.: An evolution of uncertainty assessment and quantification. Sci. Iran. 18(3), 669–676 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ilham H. Ibrahim.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12008-016-0330-7

Keywords

Navigation