Abstract

Shape memory alloys (SMAs) are an excellent material for producing components for a wide range of industrial applications, such as orthopedic implacers, micro-equipment, actuators, fittings, and screening components, as well as military equipment, aerospace components, bio-medical equipment, and fabrication requirements. Despite its remarkable qualities, the production of SMAs is a problem for investigators all over the globe. The purpose of this research is to evaluate the effects of altering the [Formula: see text], [Formula: see text], [Formula: see text], and GV while processing copper-based SMA in an electrical discharge machining process on the material removal rate (MRR) and surface roughness (SR). The major runs were designed using a central composite design. SEM was also utilized to examine the micro-structure of EDM-processed electrode tools and work samples. SEM scans indicated the presence of debris, micro-cracks, craters, and a newly formed recast layer on the electrode tool and workpiece surface. High [Formula: see text] and prolonged [Formula: see text] provide huge spark energy simply at the work sample-tool contact, resulting in debris production. The experimental results reveal that the least and highest MRR values are 10.333 and 185.067[Formula: see text]mm3/min, respectively, while the minimum and maximum SR values are 3.07 and 7.15[Formula: see text][Formula: see text]m. The desirability technique, teacher learning based optimization (TLBO), and the Jaya algorithm were also utilized to optimize the studied solutions (i.e. MRR and SR) on a single and multi-objective basis. The best MRR and SR were determined using the desirability approach, the Jaya Algorithm, and the TLBO to be 152.788[Formula: see text]mm3/min and 4.764[Formula: see text][Formula: see text]m; 240.0256[Formula: see text]mm3/min and 1.637[Formula: see text][Formula: see text]m; and 240.0257[Formula: see text]mm3/min and 1.6367[Formula: see text][Formula: see text]m.

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