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Taguchi's robust parameter design to analyse ordered categorical data using inverse omega transformation

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  • M. Shilpa
  • P. Parthiban

Abstract

The manufacturing industry is striving hard to gain an edge over its competitors as far as the product quality is concerned. The product may have both variable and attribute quality characteristics. The variable cases are addressed using Shewhart's variable control charts, process capability analysis etc. Most of the attribute cases are addressed using 'fraction defective'; this serves as a quality measure. When such attributes of the product are categorised according to their severity, then the same case may be dealt with as 'ordered categorical data'. This research work presents the use of Taguchi's parameter design to improve the product quality during spline hob operation of a shaft. The visual defects that occur during this operation are treated as ordered categorical data and the analysis of this data is carried out using inverse omega transformation. The paper has resulted in determining the optimum settings for the process parameters in the spline hob operation.

Suggested Citation

  • M. Shilpa & P. Parthiban, 2022. "Taguchi's robust parameter design to analyse ordered categorical data using inverse omega transformation," International Journal of Enterprise Network Management, Inderscience Enterprises Ltd, vol. 13(2), pages 127-139.
    • Handle: RePEc:ids:ijenma:v:13:y:2022:i:2:p:127-139
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