Advertisement

A Strategic Methodology for Taguchi Design of Experiments

  • Jiju Antony
  • Mike Kaye

Abstract

Design of experiments and Taguchi methods are well-established methodologies in which only statisticians are formally trained. Design of experiments (DOE) is a powerful approach for investigating the optimal combination of process parameters in respect of quality performance. Taguchi methods (TMs), on the other hand, are used for maximizing product and process robustness by reducing variation due to undesirable external disturbances which cannot be controlled during actual production conditions. Over the past decade, both DOE and TMs have had unprecedented success in showing how statistical methods assist organizations in manufacturing high-quality products at low costs. However, recent research has shown that the application of such statistical methods by the engineering fraternity in manufacturing companies is limited due to lack of skills required in manufacturing and inadequate statistical knowledge for problem solving. In order to bridge this gap, this chapter presents a practical and strategic methodology for Taguchi methods, to tackle and solve manufacturing process quality problems in manufacturing companies. The methodology is easy to understand and readily accessible to the engineering fraternity for solving quality problems in real-life situations. The objective of this step-by-step approach is to assist industrial engineers with limited statistical knowledge to tackle process quality problems using TMs.

Keywords

Quality Characteristic Control Chart Orthogonal Array Taguchi Method Noise Factor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Knowlton, J. and Keppinger R. (1993) “The Experimentation Process”, Quality Progress, February, pp. 43–7.Google Scholar
  2. 2.
    “A Guide to FMEA and FMECA”, BS 5750, Part 5,1991.Google Scholar
  3. 3.
    Dale, B.G. and Cooper C.D. (1992) “Total Quality and Human Resources”, Blackwell Publishers, Oxford.Google Scholar
  4. 4.
    Roy R.K. (1990) “A Primer on the Taguchi Method”, Van Nostrand Reinhold.Google Scholar
  5. 5.
    Barker T.B. (1985) “Quality by Experimental Design”, Marcel Dekker, Inc.Google Scholar
  6. 6.
    Logothetis N. (1992) “Managing for Total Quality—From Deming to Taguchi and SPC”, Prentice Hall.Google Scholar
  7. 7.
    Antony J. et al. (1996) “Sorting out Problems”, Manufacturing Engineer, IEE, October, pp. 221–223.Google Scholar
  8. 8.
    Meisel, R.M. (1991) “A Planning Guide for More Successful Experiments”, ASQC Quality Congress Transactions, pp. 174–179.Google Scholar
  9. 9.
    Lindeke, R.R. and Liou, Y.A. (1989) “Methods for Optimisation in the Manufacturing system—The Taguchi Method”, Journal of Mechanical Working Technology, Vol. 20, pp. 205–218.CrossRefGoogle Scholar
  10. 10.
    Leon, R.V., Shoemaker, A., and Tsui, K.-L. (1993) “Discussion on Planning for a Designed Industrial Experiment”, Technometrics, Vol. 35, No. 1, pp. 21–24.Google Scholar
  11. 11.
    Barrentine L.B. (1991) “Concepts for R & R Studies”, ASQC Quality PressGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2000

Authors and Affiliations

  • Jiju Antony
    • 1
  • Mike Kaye
    • 1
  1. 1.Portsmouth Business SchoolUniversity of PortsmouthPortsmouthUSA

Personalised recommendations