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Investigation of sparking electro discharge machining for fabricating silicon carbide reinforced Al7050 alloy based composite through stir casting

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This research investigates the application of spark electro-discharge machining for fabricating a stir-cast composite reinforcedwith silicon carbide, based on the Al7050 alloy. Employing a response surface methodology with a central composite design,the study explores 20 combinations of control parameters to investigate their collective influence. Specifically, it focuses onunderstanding how three key machining parameters—current, pulse-on time, and pulse-off time—affect material removalrates, electrode wear, and surface roughness. A novel teaching-learning-based optimization strategy, integrating responsesurface methodology with grey relational analysis, is utilized to optimize multiple responses. The optimized parametersderived through response surface methodology are a current of 10 amps, a pulse-on time of 6 µsec, and a pulse-off time of5 µsec, resulting in significant improvements. These optimized settings correspond to material removal rates, electrode wearrates, and surface roughness values of 0.01074 g/min, 0.0040 g/min, and 4.9395 µm, respectively. Additionally, the teachinglearning-based optimization method employs grey relational analysis initially to rank the input factors. With the optimizedprocess variables obtained using GRA-TLBO—8.48 amps for current, 6.22 µsec for pulse-on time, and 3.34 µsec for pulse-offtime—the material removal rate, electrode wear rate, and surface roughness are further enhanced to 0.01159 g/min, 0.00408g/min, and 3.7202 µm, respectively.

Abstract

This research investigates the application of spark electro-discharge machining for fabricating a stir-cast composite reinforcedwith silicon carbide, based on the Al7050 alloy. Employing a response surface methodology with a central composite design,the study explores 20 combinations of control parameters to investigate their collective influence. Specifically, it focuses onunderstanding how three key machining parameters—current, pulse-on time, and pulse-off time—affect material removalrates, electrode wear, and surface roughness. A novel teaching-learning-based optimization strategy, integrating responsesurface methodology with grey relational analysis, is utilized to optimize multiple responses. The optimized parametersderived through response surface methodology are a current of 10 amps, a pulse-on time of 6 µsec, and a pulse-off time of5 µsec, resulting in significant improvements. These optimized settings correspond to material removal rates, electrode wearrates, and surface roughness values of 0.01074 g/min, 0.0040 g/min, and 4.9395 µm, respectively. Additionally, the teachinglearning-based optimization method employs grey relational analysis initially to rank the input factors. With the optimizedprocess variables obtained using GRA-TLBO—8.48 amps for current, 6.22 µsec for pulse-on time, and 3.34 µsec for pulse-offtime—the material removal rate, electrode wear rate, and surface roughness are further enhanced to 0.01159 g/min, 0.00408g/min, and 3.7202 µm, respectively.

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