Skip to main content
SpringerLink
Log in

An efficient integrated approach to reduce scraps of industrial manufacturing processes: a case study from gauge measurement tool production firm

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

In the last decades, continuous improvement in industrial manufacturing processes is one of the fundamental principles for remaining in the competitive trade environment. This subject has a particular importance in production process of gauge measurement tools because of their high price. The main objective of this study is to reduce the scraps of gauge measurement tool manufacturing process with implementing of an efficient integrated approach based on six sigma methodology, failure mode and effects analysis (FMEA) technique, design of experiment (DOE) technique, and also simultaneous analysis of several response variables by two different methods. At first, critical to quality (CTQ) factors are determined to specify the quality level of manufacturing process. Then, different causes of poor quality in gauge measurement tool manufacturing process are determined and displayed as a cause and effect diagram. The main causes of poor quality of manufacturing process are determined by using the FMEA technique. After that, adopted for more accurate considering, the DOE technique is employed for analyzing of several input parameters of the manufacturing process and determining of optimal values of them. As a further step, a simultaneous analysis and optimization has been done by two different methods, first, optimization tool of Design-Expert software, and second, the goal programming (GP) from multi-criteria decision making (MCDM) topics. Based on the obtained structure, an optimal set of parameters and levels are determined for the different manufacturing process parameters. The experimental results for a case study illustrate substantial improvements in producing process of gauge measurement tool.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Beauchamp Y, Thomas M, Youssef A, Youssef JM (1996) Investigation of cutting parameter effects on surface roughness in lathe boring operation by use of a full factorial design. Comput Ind Eng 31(3–4):645–651

    Article  Google Scholar 

  2. Box GEP, Hunter JS (1961) The 2k-p fractional factorial designs. Technometrics 3:311–352

    MathSciNet  Google Scholar 

  3. Box GEP, Hunter JS (1961) The 2k-p fractional factorial designs, II. Technometrics 3:449–458

    MathSciNet  Google Scholar 

  4. Buyukozkan G, Berkol C (2011) Designing a sustainable supply chain using an integrated analytic network process and goal programming approach in quality function deployment. Expert Syst Appl 38:13731–13748

    Google Scholar 

  5. Charnes A, Cooper WW (1961) Management models and industrial applications of linear programming. Wiley, New York, p 1961

    MATH  Google Scholar 

  6. Chen MN, Lyu J (2009) A lean six-sigma approach to touch panel quality improvement. Prod Plan Control 20(5):445–454

    Article  Google Scholar 

  7. Chen J, Sun DX, Wu CFJ (1992) A catalogue of two level and three level fractional factorial designs with small runs. IIQP research report, RR-92-09

  8. Coleman DE, Montgomery DC (1993) A systematic approach to planning for a designed industrial experiment. Technometrics 35:1–27

    Article  Google Scholar 

  9. Davidson MJ, Balasubramanian K, Tagore GRN (2008) Surface roughness prediction of flow-formed AA6061 alloy by design of experiments. J Mater Process Technol 202(1–3):41–46

    Article  Google Scholar 

  10. Durmus K (2009) Prediction and control of surface roughness in CNC lathe using artificial neural network. J Mater Process Technol 209(7):3125–3137

    Article  Google Scholar 

  11. Eckes G (2001) Six sigma revolution: how general electric and others turned process into profits. Wiley, New York

    Google Scholar 

  12. Fisher RA (1926) The arrangement of field experiments. J Minist Agric Engl 33:503–513

    Google Scholar 

  13. Fries A, Hunter WG (1980) Minimum aberration 2k-p designs. Technometrics 22:601–608

    MATH  MathSciNet  Google Scholar 

  14. Hsu CW, Hu AH, Wu WC Using FMEA and FAHP to risk evaluation of green components. Proceedings of the 2008 I.E. International Symposium on Electronics and the Environment. ISBN:978-1-4244-2272-2

  15. Hwang YD (2006) The practices of integrating manufacturing executing systems and six sigma methodology. Int J Adv Manuf Technol 30:761–768. doi:10.1007/s00170-005-0090-1

    Article  Google Scholar 

  16. Kalyan Kumar KVBS, Choudhury SK (2008) Investigation of tool wear and cutting force in cryogenic machining using design of experiments. J Mater Process Technol 203(1–3):95–101

    Article  Google Scholar 

  17. Koning HD, Mast JD (2006) A rational reconstruction of six-sigma’s breakthrough cookbook. Int J Qual Reliab Manag 23(7):766–787

    Article  Google Scholar 

  18. Koning HD, Mast JD (2007) The CTQ flowdown as a conceptual model of project objectives. Institute for business and industrial statistics of the University of Amsterdam, (IBIS UVA). www.asq.org

  19. Kwak YH, Anbrai FT (2006) Benefit, obstacles, and future of six sigma approach. Technovation 26:708–715. doi:10.1016/j. technovation.2004.10.003

    Article  Google Scholar 

  20. Li YQ, Cui ZS, Ruan XY, Zhang DJ (2006) CAE-based six sigma robust optimization for deep-drawing sheet metal process. Int J Adv Manuf Technol 30:631–637. doi:10.1007/s00170-005-0121

    Article  Google Scholar 

  21. Li SH, Wu CC, Yen DC, Lee MC (2011) Improving the efficiency of IT help desk service by six sigma management methodology (DMAIC) – a case study of C company. Prod Plan Control 22(7):612–627

    Article  Google Scholar 

  22. Lin TW, O’Leary DE (1993) Goal programming applications in financial management. Adv Math Program Financial Plan 3:211–229

  23. Lo WC, Tsai KM, Hsieh CY (2009) Six sigma approach to improve surface precision of optical lenses in the injection-molding process. Int J Adv Manuf Technol 41:885–896. doi:10.1007/s00170-008-1543-0

    Article  Google Scholar 

  24. Mahesh M, Wong YS, Fuh JYH (2006) A six sigma approach for benchmarking of RP&M process. Int J Adv Manuf Technol 31:374–387. doi:10.1007/s00170-005-0201

    Article  Google Scholar 

  25. Montgomery DC, Runger GC (1993) Gauge capability and designed experiments, part I: basic methods. Qual Eng 6(1):115–135

    Article  Google Scholar 

  26. Mori M, Mizuguchi H, Fujishima M, Ido Y, Mingkai N, Konishi K (2009) Design optimization and development of CNC lathe headstock to minimize thermal deformation. CIRP Ann Manuf Technol 58(1):331–334

    Article  Google Scholar 

  27. Mukerjee R, Wu CFJ (2006) A modern theory of factorial designs. Springer, New York

    MATH  Google Scholar 

  28. Ozcan UG, Toklu B (2009) Multiple-criteria decision-making in two-sided assembly line balancing: a goal programming and a fuzzy goal programming models. Comput Oper Res 36:1955–1965

    Article  Google Scholar 

  29. Palanikumar K, Karunamoorthy L, Karthikeyan R (2006) Assessment of factors influencing surface roughness on the machining of glass fiber-reinforced polymer composites. Mater Des 27(10):862–871

    Article  Google Scholar 

  30. Pearn WL, Wang FK, Chen J (2007) Multivariate capability indices: distributional and inferential properties. J Appl Stat 34(8):941–962

    Article  MathSciNet  Google Scholar 

  31. Puente J, Pino R, Priore P, de la Fuente D (2002) A decision support system for applying failure mode and effects analysis. Int J Qual Reliab Manag 19:137–150

    Article  Google Scholar 

  32. Rhee SJ, Ishii K (2003) Using cost based FMEA to enhance reliability and serviceability. Adv Eng Inform 17:179–188

    Article  Google Scholar 

  33. Shishebori D, Hamadani AZ (2010) Properties of multivariate process capability in presence of gauge measurement errors and dependency measure of process variables. J Manuf Syst 29:10–18

    Article  Google Scholar 

  34. Snedecor GW (1937) Statistical methods. Iowa State College Press, Ames

    MATH  Google Scholar 

  35. Sokovic M, Pavletic D, Fakin S (2005) Application of six sigma methodology for process design. J Mater Process Technol 162–163:777–783. doi:10.1016/j.jmatprotec.2005.02.231

    Article  Google Scholar 

  36. Stamatis DH (1995) Failure mode and effect analysis. ASQ, Quality Press, Milwaukee

    Google Scholar 

  37. Tamiz M, Jones D, Romero C (1998) Goal programming for decision making: an overview of the current state-of-the-art. Eur J Oper Res 111(5):69–81

    Google Scholar 

  38. Thomas M, Beauchamp Y, Youssef AY, Masounave J (1996) Effect of tool vibrations on surface roughness during lathe dry turning process. Comput Ind Eng 31(3–4):637–644

    Article  Google Scholar 

  39. Tong JPC, Tsung F, Yen BPC (2004) A DMAIC approach to printed circuit board quality improvement. Int J Adv Manuf Technol 23:523–531. doi:10.1007/s00170-003-1721

    Article  Google Scholar 

  40. Wikipedia, the free encyclopedia. http://en.wikipedia.org

  41. Yates F (1937) The design and analysis of factorial experiments. Imp Bur Soil Sci Tech Commun 35:1–95

    Google Scholar 

  42. Youssef AY, Beauchamp Y, Thomas M (1994) Comparison of a full factorial experiment to fractional and Taguchi designs in a lathe dry turning operation. Comput Ind Eng 27(1–4):59–62

    Article  Google Scholar 

  43. Załuski D, Gołaszewski J (2006) Efficiency of 35-p fractional factorial designs determined using additional information on the spatial variability of the experimental field. J Agron Crop Sci 192(4):303–309. doi:10.1111/j.1439 037X.2006.00216

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Yazd University, Pajoohesh Square, safaaiyeh, Yazd, Iran, P.C. 89195741

    Davood Shishebori

  2. Department of Industrial Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran, P.C. 1684613114

    M. Javad Akhgari, Rassoul Noorossana & G. Hossein Khaleghi

Authors
  1. Davood Shishebori
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Javad Akhgari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rassoul Noorossana
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. G. Hossein Khaleghi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Davood Shishebori.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shishebori, D., Akhgari, M.J., Noorossana, R. et al. An efficient integrated approach to reduce scraps of industrial manufacturing processes: a case study from gauge measurement tool production firm. Int J Adv Manuf Technol 76, 831–855 (2015). https://doi.org/10.1007/s00170-014-6273-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-014-6273-x

Keywords

Search

Navigation