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Quality Improvement via Optimization of Tolerance Intervals During the Design Stage

  • Sevgui Hadjihassan
  • Eric Walter
  • Luc Pronzato
Part of the Applied Optimization book series (APOP, volume 3)

Abstract

A deterministic model-based approach to quality improvement is proposed, along Taguchi’s ideas for off-line quality control. This new approach takes into account fluctuations in the factors; these fluctuations are characterized in terms of tolerance intervals.

The case of an a priori known model (i.e., known dependency of the performance characteristics on the factors) is considered first. The optimal factor design is chosen by worst-case optimization.

If the model’s parameters are unknown, a bounded-error approach is used to characterize their uncertainty. A min-max optimization is then performed, taking into account the fluctuations of the quality of the product components as well as the uncertainty on the model parameters.

Keywords

Quality Improvement Noise Factor Interval Computation Tolerance Interval Estimation Stage 
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.

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Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Sevgui Hadjihassan
    • 1
  • Eric Walter
    • 1
  • Luc Pronzato
    • 2
  1. 1.Laboratoire des Signaux et SystèmesCNRS/ESEGif sur YvetteFrance
  2. 2.Laboratoire I3SCNRS URA-1376ValbonneFrance

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