Abstract
In Chapt. 4, it is claimed that perhaps the majority of quality problems are caused by variation in quality characteristics. The evidence is that typically only a small fraction of units fail to conform to specifications. If characteristic values were consistent, then either 100% of units would conform or 0%. Robust design methods seek to reduce the effects of input variation on a system’s outputs to improve quality.
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References
Allen TT, Ittiwattana W, Richardson RW, Maul G (2001) A method for robust process design based on direct minimization of expected loss applied to arc welding. J Manufact Syst 20:329–348
Allen TT, Richardson RW, Tagliabue D, Maul G (2002) Statistical process design for robotic GMA welding of sheet metal. Welding J 81(5):69s–77s
Chen LH, Chen YH (1995) A computer-simulation-oriented design procedure for a robust and feasible job shop manufacturing system. Journal of Manufacturing Systems 14:1–10
Devor R, Chang T, et al (1992) Statistical quality design and control. Macmillan, New York, pp 47–57
Johnson NL, Kotz S et al (1995) Continuous univariate distributions. John Wiley, New York
Lucas JM (1994) How to achieve a robust process using response surface methodology. J Qual Technol 26:248–260
Myers R, Montgomery D (2001) Response surface methodology, 5th edn. Wiley, Hoboken, NJ
Nair VN, Pregibon D (1986) A data analysis strategy for quality engineering experiments. AT&T Technical J 74–84
Song AA, Mathur A et al (1995) Design of process parameters using robust design techniques and multiple criteria optimization. IEEE Trans Syst Man Cybernet 24:1437–1446
Taguchi G (1987) A system for experimental design. UNIPUB, Detroit
Taguchi G (1993) Taguchi methods: research and development. In: Konishi S (ed), Quality engineering series, vol 1. The American Supplier Institute, Livonia, MI
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Allen, T.T. (2019). DOE: Robust Design. In: Introduction to Engineering Statistics and Lean Six Sigma. Springer, London. https://doi.org/10.1007/978-1-4471-7420-2_14
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DOI: https://doi.org/10.1007/978-1-4471-7420-2_14
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