Experimental investigation and optimization of process parameters of hybrid Al/SiC/B4C-MMCs finished by MAFM process using RSM modeling with supervised machine learning algorithm

Magnetic abrasive flow machining (MAFM) has an astonishing capability for improving the surface quality of advanced materials viz. composites, ceramics and hard alloys. The surface quality and finishing have major dependency on various process parameters of the focused surface while finishing through MAFM process. The MAFM process procures several applications in medical fields (Knee joint implant and surgical instruments), automotive, aerospace and tool manufacturing industries. The newness of current study is in the development of an MAFM setup for machining of SiC/B4C hybrid MMCs with aluminium-6063 as a base material and the measurement of parametric effects on the process performance. The efforts made have led towards the modeling of two responses viz. MRR and Delta R-a with response surface methodology. Box-Behnken design approach has been adopted for analyzing six MAFM factors and a total of 54 trials have been conducted for finding their influence on MRR and ?R-a. SEM and EDX have been applied to examine the surface topography. The significance of various process parameters has been analyzed by using ANOVA. The outcomes showed that E-p (extrusion pressure), M (mesh size), N (number of cycles), and M-f (magnetic flux density) are the most essential factors. The optimal solutions have been attained by applying a multi-objective optimization 'desirability' function using statistical and supervised machine learning algorithms which led to the parametric machine learning algorithms reflection for surmising the efficiency of MAFM process. A fine consonance has been obtained among the predicted and actual values. The graphical abstract of the current research work is shown below.