Optimization of correlated multiple responses of ultrasonic machining (USM) process

Ultrasonic machining (USM) process has several important performance measures (responses), some of which are correlated. For example, material removal rate and tool wear rate are highly correlated. Although in the recent past several methods have been proposed in the literature to resolve the multi-response optimization problems, only a few of them take care of the possible correlation between the responses. All these methods primarily make use of principal component analysis (PCA) to consider the possible correlation between the responses. Process engineers may face the difficulty of selecting the appropriate method because the relative optimization performances of these methods are unknown. In this paper, two sets of past experimental data on USM process are analysed using three methods dealing with the multiple correlated responses, and the optimization performances of these three methods are subsequently compared. It is observed that both the weighted principal component (WPC) and PCA-based TOPSIS methods result in a better optimization performance than the PCA-based grey relational analysis method. However, the WPC method is preferable because of its simpler computational procedure.