Modified whale intelligence algorithm and Combined Compromise Solution (CoCoSo) for machinability evaluation of polymer nanocomposites

Carbon nano-onions (CNOs) are distinct from other carbon nanomaterials due to their distinctive physical and morphological features. Their addition can substantially increase the thermal, mechanical, tribological, and electrical properties. The present work explores the Milling machinability of zero-dimensional CNO reinforced epoxy nanocomposites. The effect of varying parameters, namely, the weight percentage of CNO (wt%), spindle speed (N), feed rate (F), and depth of cut (D), was examined to control the machining performances. Milling tests were conducted using the Taguchi-based L27Orthogonal array (OA), and a nonlinear regression model was used to develop a correlation between machining constraints and responses. A comparatively advanced metaheuristic multi-objective whale optimization algorithm (MOWOA) is used to accomplish an optimal parametric set. This optimization methodology was exploited to achieve non-dominated solutions and established the Pareto front. In the end, combined compromise solution (CoCoSo) was utilized to locate the most relevant result from the Pareto optimum setting. Based on the CoCoSo analysis, the optimal solution was found as CNO ≈ 1.5 wt%, spindle speed ≈ 1500 rpm, feed rate = 50 mm/min, and depth of cut ≈ 2 mm. The findings of the microscopy test support the results of the proposed optimization tool. The suggested MOW optimization procedure could be used in the production sector for monitoring quality and productivity indices. The proposed hybrid module can be forwarded to the industrial sector to optimize the multiple conflicting responses.