A DEA-based multi-response fusion model in the context of Taguchi method

Abstract

Abstract Taguchi methods have been widely used to optimize machining parameters of a manufacturing process so that the product performances or responses are close to the desired targets and not sensitive to external noises. For single-response problems, the optimal machining parameters can be obtained based on Taguchi signal-to-noise ratio or response surface models. For multi-response problems, a multi-objective optimization approach or a fusion model is needed before obtaining the optimal parameters; and the latter is preferred due to its simplicity. This paper proposes a fusion model to combine multiple responses into a composite response variable. The proposed model first transforms the response data obtained from Taguchi experiments into the data with smaller-the-better or larger-the-better quality characteristics, and then combines the transformed data into the composite response variable in the way similar to Data Envelopment Analysis (DEA). Once the fusion model is built, the problem reduces into a single-response problem, which can solved using existing approaches. The proposed model can be easily implemented using a spreadsheet program; and one real-world example is included to illustrate its appropriateness.