Optimizing the Milling Operation on 3D-printed Carbon Fiber-reinforced ABS Composite using PROMETHEE: A Comparative Analysis

This paper optimizes the milling operation on 3D-printed carbon fiber-reinforced acrylonitrile butadiene styrene (ABS+15% CF) composite using preference ranking organization method for enrichment of evaluations (PROMETHEE) in a multi-criteria decision making paradigm. Spindle speed (SS), feed rate (FR) and depth of cut (DOC) are treated as the milling parameters, while delamination factor, material removal rate, tool wear rate and surface roughness are the responses. Through main effects plots and analysis of variance results, influences of all the milling parameters on the responses are investigated. During optimization of the said milling process using PROMETHEE, effects of the six preference functions, i.e. usual, U-shape, V-shape, level, linear and Gaussian on the rankings of the conducted experiments are examined. It is revealed that V-shape, level, linear and Gaussian functions pick out trial number 25 (SS = 1000 rpm, FR = 600 mm/min and DOC = 0.2 mm) as the ideal choice, while, a parametric mix of SS = 1000 rpm, FR = 400 mm/min and DOC = 0.2 mm (experiment number 22) appears as the best trial for usual and U-shape functions. Considering the highest CRITIC weight allotted to MRR, experiment number 25 provides the optimal parametric combination for the considered milling operation on ABS+15% CF composite. Confirmation experiment at the optimal setting reproduces the observed responses and when evaluated by PROMETHEE against the other experiments, it retains the same rank as the initial trial for all six preference functions. The rankings of the alternative experiments are almost similar across the six preference functions of PROMETHEE. Thus, this data-driven approach can be effectively employed for parametric optimization of any of the manufacturing processes through simple calculation steps.