Cost engineering for variation management during the product and process development

  • Alain Etienne
  • Shirin Mirdamadi
  • Mehrdad Mohammadi
  • Roozbeh Babaeizadeh Malmiry
  • Jean-François Antoine
  • Ali Siadat
  • Jean-Yves Dantan
  • Reza Tavakkoli
  • Patrick Martin
Original Paper

Abstract

Variation Management during the Product & Process Development can profoundly impact the quality, the cost of the product and the number of scraps in mass production... Designers want tight tolerances to ensure product performance; manufacturers prefer loose tolerances to reduce manufacturing and assembly cost. To analyse compromise solutions, the primary aim is to establish an objective function. This paper presents a model for the key indicators assessment to the relevance of variation management: cost, and investigates which model used in decision analysis is the most appropriate to prioritize and aggregate the predetermined performance measures. The applications of this model are demonstrated through an industrial case study where tolerance allocation, product development, problem is firstly addressed. Once optimized tolerances are attained, inspection planning, process development, problem is approached to ensure the optimized awaited quality level for the least cost.

Keywords

Integrated Product and Process Design Cost Variation management Tolerancing Inspection planning 

Notes

Acknowledgments

The authors would like to acknowledge France embassy in Iran and Campus France for their financial supports.

References

  1. 1.
    Zhang, C., Wang, H.P.: Simultaneous optimization of design and manufacturing—tolerances with process (machine) selection. CIRP Ann. Manuf. Technol. 41(1), 569–572 (1992)CrossRefGoogle Scholar
  2. 2.
    Lööf, J., Hermansson, T., Söderberg, R.: An efficient solution to the discrete least-cost tolerance allocation problem with general loss functions. In: Models for computer aided tolerancing in design and manufacturing, pp. 115–124. Springer, Netherlands (2007)Google Scholar
  3. 3.
    Schmitt, R., Behrens, C.: A statistical method for analyses of cost-and risk optimal tolerance allocations based on assured input data. In: Proceedings 10th CIRP International Seminar on Computer-Aided Tolerancing (2007)Google Scholar
  4. 4.
    Moroni, G., Petrò, S., Tolio, T.: Early cost estimation for tolerance verification. CIRP Ann. Manuf. Technol. 60(1), 195–198 (2011)CrossRefGoogle Scholar
  5. 5.
    Savio, E.: A methodology for the quantification of value-adding by manufacturing metrology. CIRP Ann. Manuf. Technol. 61(1), 503–506 (2012)CrossRefGoogle Scholar
  6. 6.
    Kunzmann, H., Pfeifer, T., Schmitt, R., Schwenke, H., Weckenmann, A.: Productive metrology-adding value to manufacture. CIRP Ann. Manuf. Technol. 54(2), 691–704 (2005)CrossRefGoogle Scholar
  7. 7.
    Mirdamadi, S., Etienne, A., Hassan, A., Dantan, J.Y., Siadat, A.: Cost estimation method for variation management. Procedia CIRP 10, 44–53 (2013)CrossRefGoogle Scholar
  8. 8.
    Ullman, D.G.: The mechanical design process. McGraw-Hill, New York (2010)Google Scholar
  9. 9.
    Beitz, W., Pahl, G., Wallace, K.: Engineering design: a systematic approach. Springer, New York (2007)Google Scholar
  10. 10.
    Tata, M.M., Thornton, A.C.: Process capability database usage in industry: myth vs. reality (1999)Google Scholar
  11. 11.
    Srinivasan, V.: An integrated view of geometrical product specification and verification. In: Geometric Product Specification and Verification: Integration of Functionality, pp. 1–11. Springer, Netherlands (2003)Google Scholar
  12. 12.
    Etienne, A., Dantan, J.Y., Siadat, A., Martin, P.: Cost estimation for tolerance allocation. In: Proc. of the 10th CIRP International Seminar on Computer Aided Tolerancing (2007)Google Scholar
  13. 13.
    Speckhart, F.H.: Calculation of tolerance based on a minimum cost approach. J. Eng. Ind. ASME 94, 447–453 (1972)Google Scholar
  14. 14.
    Sutherland, G.H., Routh, B.: Mechanism design: accounting for manufacturing tolerances and costs in function generating problems. J. Eng. Ind. 97, 283–286 (1975)CrossRefGoogle Scholar
  15. 15.
    Abel-Maleck, L., Asadathorn, N.: An analytical approach to process planning with rework option. Int. J. Prod. Econ. 46–47, 511–520 (1996)CrossRefGoogle Scholar
  16. 16.
    Duverlie, P., Castelain, M.: Cost estimation during design step: parametric method versus case based reasoning. Int. J. Adv. Manuf. Technol. 15, 895–906 (1999)CrossRefGoogle Scholar
  17. 17.
    Kim, G.H., An, S.H., Kang, K.I.: Comparison of construction cost estimating models based on regression analysis, neural networks, and case-based reasoning”. Build. Environ. 39, 1235–1242 (2004)CrossRefGoogle Scholar
  18. 18.
    Feng, C.X., Kusiak, A., Huang, C.C.: Cost evaluation in design with form features. Comput. Aided Design 28(11), 879–885 (1996)CrossRefGoogle Scholar
  19. 19.
    Johnson, H.T., Kaplan, R.S.: Relevance cost: the rise and fall of management accounting. Harvard Business School Press, Boston (1987)Google Scholar
  20. 20.
    Gupta, M., Galloway, K.: Activity based costing and management and its implications on operations management. Technovation 23(3), 131–138 (2003)CrossRefGoogle Scholar
  21. 21.
    Bosch Mauchand, M., Siadat, A., Perry, N., Bernard, A.: VCS: value chains simulator, a tool for value analysis of manufacturing enterprise processes (a value-based decision support tool). J. Intell. Manuf. 23/4, 1389–1402 (2012)CrossRefGoogle Scholar
  22. 22.
    Etienne, A., Dantan, J.Y., Qureshi, J., Siadat, A.: Variation management by functional tolerance allocation and manufacturing process selection. Int. J. Interact. Des. Manuf. 2(4), 207–218 (2008)Google Scholar
  23. 23.
    Pfeifer, T.: Production metrology. Oldenbourg Verlag, München, ISBN 10: 3486258850 (2002)Google Scholar
  24. 24.
    Zhao, F., Xu, X., Xie, S.Q.: Computer-aided inspection planning-the State of the Art. Comput. Ind. 60, 453–466 (2009)CrossRefGoogle Scholar
  25. 25.
    Mohammadi, M., Siadat, A., Dantan, J.Y., Tavakkoli-Moghaddam, R.: Mathematical modelling of a robust inspection process plan: Taguchi and Monte Carlo methods. Int. J. Prod. Res. 53(7) (2014)Google Scholar
  26. 26.
    Shah, L.A., Etienne, A., Siadat, A., Vernadat, F.: Decision-making in the manufacturing environment using a value-risk graph. J. Intell. Manuf. 27(3), 617–631 (2014)CrossRefGoogle Scholar
  27. 27.
    Vernadat, F., Shah, L., Etienne, A., Siadat, A.: VR-PMS: a new approach for performance measurement and management of industrial systems, International Journal of Production Research, pp. 1–19. Taylor & Francis, London (2013)Google Scholar
  28. 28.
    Mousavi, M., Mirdamadi, S., Siadat, A., Dantan, J.-Y., Tavakkoli-Moghaddam, R.: A new intuitionistic fuzzy grey model for selection problems with an application to the inspection planning in manufacturing firms. Eng. Appl. Artif. Intell. 39, 157–167 (2015)CrossRefGoogle Scholar
  29. 29.
    Hassan, A., Siadat, A., Dantan, J.Y., Martin, P.: Conceptual process planning an improvement approach using QFD, FMEA, and ABC methods. Robot Comput. Integr. Manuf. 26(4), 392–401 (2010)CrossRefGoogle Scholar
  30. 30.
    Hassan, A., Siadat, A., Dantan, J.Y., Martin, P.: A quality/cost-based improvement approach for conceptual process planning. Int. J. Manag. Sci. Eng. Manag. 4(3), 188–197 (2009)Google Scholar

Copyright information

© Springer-Verlag France 2016

Authors and Affiliations

  • Alain Etienne
    • 1
  • Shirin Mirdamadi
    • 2
  • Mehrdad Mohammadi
    • 3
  • Roozbeh Babaeizadeh Malmiry
    • 1
  • Jean-François Antoine
    • 1
  • Ali Siadat
    • 1
  • Jean-Yves Dantan
    • 1
  • Reza Tavakkoli
    • 4
  • Patrick Martin
    • 1
  1. 1.LCFC, Arts et Metiers ParisTechMetzFrance
  2. 2.Quartz, Supmeca Paris St OuenSaint-OuenFrance
  3. 3.EMSE-CMP LIMOS, UMR CNRS 6158 Ecole des Mines de Saint-Etienne Campus G. Charpak ProvenceGardanneFrance
  4. 4.School of Industrial Engineering, College of EngineeringUniversity of TehranTehranIran

Personalised recommendations