| Summary: | The present research used the stir-casting method to develop an Al-based composite. The developed composite exhibited challenges while being processed on conventional machining. Thus, a non-traditional machining process was opted to process the composite. The machining variables selected for the current research were the pulse off time (Toff), pulse on time (Ton), servo voltage (SV), current (I), and tool electrode. Three tool electrodes (SS-304, copper, and brass) were used to process the developed composite (Al/SiC/Gr). The experimental plan was designed using response surface methodology (RSM). The output responses recorded for the analysis were the material removal rate (MRR) and tool wear rate (TWR). The obtained data was optimized using complex proportional assessment (COPRAS) and machine learning methods. The optimized settings predicted by the RSM–COPRAS method were Ton: 60 µs; Toff: 60 µs; SV: 7 V; I: 12 A; and tool: brass. The maximum MRR and TWR at the suggested settings were 1.11 g/s and 0.0114 g/s, respectively. A morphological investigation of the machined surface and tool surface was conducted with scanning electron microscopy. The morphological examination of the surface (machined) presented the presence of cracks, lumps, etc.
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