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Ultrasonic machining (USM) process has multiple performance measures, e.g. material removal rate (MRR), tool wear rate (TWR), surface roughness (SR) etc., which are affected by several process parameters. The researchers commonly attempted to optimize USM process with respect to individual responses, separately. In the recent past, several systematic procedures for dealing with the multi-response optimization problems have been proposed in the literature. Although most of these methods use complex mathematics or statistics, there are some simple methods, which can be comprehended and implemented by the engineers to optimize the multiple responses of USM processes. However, the relative optimization performance of these approaches is unknown because the effectiveness of different methods has been demonstrated using different sets of process data. In this paper, the computational requirements for four simple methods are presented, and two sets of past experimental data on USM processes are analysed using these methods. The relative performances of these methods are then compared. The results show that weighted signal-to-noise (WSN) ratio method and utility theory (UT) method usually give better overall optimisation performance for the USM process than the other approaches.

Electrical discharge machining (EDM) is one of the most extensively used non-traditional machining processes having multiple performance characteristics, some of which are usually correlated. So, ideally, use of principal component analysis (PCA)-based approaches that take into account the possible correlations between the responses are suitable for optimization of EDM process. A recently reported study reveals that PCA-based proportion of quality loss reduction (PQLR) method results in the best optimization performance among the four considered PCA-based approaches for EDM process. This paper presents a modified PCA-based utility theory (UT) approach for optimization of correlated responses. The reported experimental data on EDM processes in literature are analyzed using the modified PCA-based UT approach and PCA-based PQLR method. Comparison of the predicted performance measures at the optimal process conditions derived based on these two PCA-based approaches reveal that the modified PCA-based UT approach leads to better overall optimization performance. So, it can be the most promising approach for optimizing EDM processes.

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