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CP-Ti G2 has become the preferred biocompatible material for various devices mainly used in orthopedic and dental implants and it is also used in aviation and aircraft. While CP-Ti G2 deals with good ductility, higher stiffness, and fatigue resistance. The novelty of present research work was attentive to the effect of WEDM factors on MRR. After machining, surface topography was examined through SEM. MRR was modeled through ANOVA to analyze the adequacy. It was observed that POT, POFT, PC, and SGV most significant factors. The WEDM factors have also been significantly deteriorating the morphology of machined samples in the form of craters, debris, and micro cracks. A multi-objective optimization ‘desirability’ function hybrid with a supervised machine learning algorithm was applied to obtain the optimal solutions. The results show a good agreement between actual and predicted values.

Experimental investigation of WEDM process through integrated desirability and machine learning technique on implant material