Earticle

Robust design is an approach to reducing performance variation of quality characteristic values in quality engineering. Product array approach which is used in the Taguchi parameter design has a number of advantages by considering the noise factor. Taguchi has an idea that mean and variation are handled simultaneously to reduce the expected loss in products and processes. Taguchi has used the signal-to-noise ratio (SN) to achieve the appropriate set of operating conditions where variability around target is low in the Taguchi parameter design. Many Statisticians criticize the Taguchi techniques of analysis, particularly those based on the SN. In this paper we propose a substantially simpler optimization procedure for robust design using desirability function without resorting to SN.

Robust parameter design is an off-line production technique for reducing variation and improving the quality of products and processes by using product arrays. However, the use of the product arrays usually requires a large number of runs. To overcome the drawback of the product array, the combined array can be used. Also optimizing multiple responses is increasingly important in industry. Using simultaneous optimization measures, we can deal with the multiple response case. In this paper we compare the simultaneous optimization using the Taguchi's product array with using the combined array. And models possible to set on combined arrays are also investigated and compared with the cases of product arrays.