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Electrical Discharge Machining of Engineered Al-22%SiC Metal Matrix Composite: Surface Roughness Analysis, Optimization, Economic Analysis, and Sustainability Assessment

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Abstract

This research addresses electrical discharge machining of engineered Al-22%SiC metal matrix composite to analyze the surface roughness of machined part. A series of machining trials are performed under varied process conditions (flushing pressure, gap voltage, pulse-on time, discharge current, pulse-off time) obtained by Box–Behnken design. Additionally, this work addresses on desirability optimization methodology and predictive modeling for minimization of machined surface quality employing response surface methodology. Based on the motivational viewpoint of “Go green-Think green-Act green,” a unique approach has been suggested for economic analysis and sustainability assessment to determine the overall machining cost per part and to justify the usefulness of vegetable oil as dielectric medium in electrical discharge machining. According to statistical analysis, the contribution of spark discharge current was identified as the leading factor in surface quality degradation. The estimated optimal surface roughness of 0.181 µm and the calculated overall machining cost per part of Rs.245.9 were preferred at a pulse-on time of 100 µs, gap voltage of 1 V, pulse-off time of 30 µs, discharge current of 4 A, and flushing pressure of 0.57 kgf/cm2, which indicates techno-economically viable. The vegetable oil considered as dielectric fluid is biodegradable and environmentally safe, thus contributing to sustainable production.

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The authors have made substantial contributions to the conception and design of the study, in the acquisition of the data, and in the analysis and interpretation of the data. The authors participated in drafting the article critically for important intellectual content.

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Correspondence to Sudhansu Ranjan Das.

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Naik, S., Das, S.R., Dhupal, D. et al. Electrical Discharge Machining of Engineered Al-22%SiC Metal Matrix Composite: Surface Roughness Analysis, Optimization, Economic Analysis, and Sustainability Assessment. Process Integr Optim Sustain 6, 223–251 (2022). https://doi.org/10.1007/s41660-021-00207-1

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