Tri-objective constrained optimization of pulsating DC sourced magnetic abrasive finishing process parameters using artificial neural network and genetic algorithm

Owing to the exceptional mechanical properties of Ti-6Al-4V, it is widely utilized in numerous critical mechanical parts for the uncompromised factor of safety. However, performing machining operations on this alloy in close tolerance is a challenging task. Moreover, establishing a process for its efficient finishing has become the interest of researchers. In this research study, the magnetic abrasive finishing process (MAF) has been studied using the ANN-GA approach, where ANN has been used for modeling of input-output relations, and GA has been used to optimize the MAF process. The experiments were conducted on a pulsating DC sourced MAF set-up, and SiC-based loosely bonded magnetic abrasive media was used for material removal. During experimentation, the current, machining gap, speed of rotation, abrasive composition, and finishing time were taken as input parameters being arranged in an array of L16 orthogonal. In contrast, output parameters were changed in surface roughness, change in the microhardness, and change in the modulus of elastic indentation. ANN-GA approach provides a set of optimal solutions for obtaining suitable output values. Furthermore, loosely bound SiC-based magnetic abrasive media and its composition is found to be a very critical factor for the performance of the finishing quality on Ti-6Al-4 V.