Modeling and Optimization of Surface Quality Characteristics in Electrochemical Surface Grinding of Metal Matrix Composite

Study of complex machining behavior of metal matrix composites has always been a matter of investigation. The different mechanical, physical and thermal properties of matrix and reinforcement materials pose difficulties in machining by conventional or advanced processes. Recent research works show that although electrochemical machining (ECM) process can be used to machine MMCs, it leads to inferior surface quality and formation of oxide layer on the machined surface which restricts the application of ECM. This work investigates the machining performance of Al-SiC-Gr MMC using ECSG which is a hybrid variant of ECM. Artificial intelligence-based hybrid approach of ANN and GA has been applied to predict and optimize the surface roughness (SR), oxide layer thickness (OLT) and micro-crack width (MCW). The results show an overall improvement of 29% in multiple quality characteristics SR, OLT and MCW. The SEM images of machined surface also reveal the improvement in surface quality.