Optimizing machining parameters of wire-EDM process to cut Al7075/SiCp composites using an integrated statistical approach

doi:10.1007/s40436-016-0148-3

Abstract

Abstract:

Metal matrix composites (MMCs) as advanced materials, while producing the components with high dimensional accuracy and intricate shapes, are more complex and cost effective for machining than conventional alloys. It is due to the presence of discontinuously distributed hard ceramic with the MMCs and involvement of a large number of machining control variables. However, determination of optimal machining conditions helps the process engineer to make the process efficient and effective. In the present investigation a novel hybrid multi-response optimization approach is proposed to derive the economic machining conditions for MMCs. This hybrid approach integrates the concepts of grey relational analysis (GRA), principal component analysis (PCA) and Taguchi method (TM) to derive the optimal machining conditions. The machining experiments are planned to machine Al7075/SiCp MMCs using wire-electrical discharge machining (WEDM) process. SiC particulate size and its weight percentage are explicitly considered here as the process variables along with the WEDM input variables. The derived optimal process responses are confirmed by the experimental validation tests and the results showed satisfactory. The practical possibility of the derived optimal machining conditions is also analyzed and presented using scanning electron microscope examinations. According to the growing industrial need of making high performance, low cost components, this investigation provide a simple and sequential approach to enhance the WEDM performance while machining MMCs.

Key words: Al7075/SiC metal matrix composites (MMCs), Wire-electrical discharge machining (WEDM), Principal component analysis (PCA), Gray relational analysis (GRA), Taguchi method (TM)

Thella Babu Rao. Optimizing machining parameters of wire-EDM process to cut Al7075/SiCp composites using an integrated statistical approach[J]. Advances in Manufacturing, 2016, 4(3): 202-216.

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