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A Fuzzy Multiple Regression Approach for Optimizing Multiple Responses in the Taguchi Method

A Fuzzy Multiple Regression Approach for Optimizing Multiple Responses in the Taguchi Method

Abbas Al-Refaie, Ibrahim Rawabdeh, Reema Abu-alhaj, Issam S. Jalham
Copyright: © 2012 |Volume: 2 |Issue: 3 |Pages: 22
ISSN: 2156-177X|EISSN: 2156-1761|EISBN13: 9781466612174|DOI: 10.4018/ijfsa.2012070102
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MLA

Al-Refaie, Abbas, et al. "A Fuzzy Multiple Regression Approach for Optimizing Multiple Responses in the Taguchi Method." IJFSA vol.2, no.3 2012: pp.13-34. http://doi.org/10.4018/ijfsa.2012070102

APA

Al-Refaie, A., Rawabdeh, I., Abu-alhaj, R., & Jalham, I. S. (2012). A Fuzzy Multiple Regression Approach for Optimizing Multiple Responses in the Taguchi Method. International Journal of Fuzzy System Applications (IJFSA), 2(3), 13-34. http://doi.org/10.4018/ijfsa.2012070102

Chicago

Al-Refaie, Abbas, et al. "A Fuzzy Multiple Regression Approach for Optimizing Multiple Responses in the Taguchi Method," International Journal of Fuzzy System Applications (IJFSA) 2, no.3: 13-34. http://doi.org/10.4018/ijfsa.2012070102

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Abstract

The fuzzy regression has been found effective in modeling the relationship between the dependent variable and independent variables when a high degree of fuzziness is involved and only a few data sets are available for model building. This research, therefore, proposes an approach for optimizing multiple responses in the Taguchi method using fuzzy regression and desirability function. The statistical regression is formulated for the signal to noise (S/N) ratios of each response replicate. Then, the optimal factor levels for each replicate are utilized in building fuzzy regression model. The desirability function, pay-off matrix, and the deviation function are finally used for formulating the optimization models for the lower, mean, and upper limits. Two case studies investigated in previous literature are employed for illustration; where in both case studies the proposed approach efficiently optimized processes performance.

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