4.6 Article

Selection of optimal process parameters in WEDM while machining Al7075/SiCp metal matrix composites

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SPRINGER LONDON LTD
DOI: 10.1007/s00170-014-5780-0

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Al7075/SiCp; Stir casting; WEDM; Non-dominated Sorting Genetic Algorithm-II

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Aluminium metal matrix composites (MMCs) reinforced with silicon carbide particulate (SiCp) find several applications due to their improved mechanical properties over the conventional metals for a wide variety of aerospace and automotive applications. However, the presence of discontinuously distributed hard ceramic in the MMCs made them as difficult-to-cut materials for conventional machining methods. The wire electrical discharge machining (WEDM), as a widely adopted non-traditional machining method for difficult-to-cut precision components, found an appropriate metal removal process for MMCs to enhance quality of cut within the stipulated cost. While machining the advanced materials like MMCs, a clear understanding into the machining performance of the process for its control variables could make the process uncomplicated and economical. In light of the growing industrial need of making high performance-low cost components, the investigation aimed to explore the machining performance characteristics of SiCp reinforced Al7075 matrix composites (Al7075/SiCp) during WEDM. While conducting the machining experiments, surface roughness, metal removal rate, and wire wear ratio are considered the responses to evaluate the WEDM performance. Response surface methodology is used to develop the empirical models for these WEDM responses. SiC particulate size and volume percentages are considered the process variables along with pulse-on time, pulse-off time, and wire tension. Analysis of variance (ANOVA) is used to check the adequacy of the developed models. Since the machining responses are conflicting in nature, the problem is formulated as a multi-objective optimization problem and is solved using the Non-dominated Sorting Genetic Algorithm-II to obtain the set of Pareto-optimal solutions. The derived optimal process responses are confirmed by the experimental validation tests, and the results are analyzed by SEM.

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