Tribological analysis of laser deposited SS316L/Co27Cr6Mo functionally graded materials using adaptive neuro-fuzzy inference system

Image processing, power engineering, robotics, industrial automation etc., have all found successful uses for artificial intelligence (AI) techniques such as neural networks (ANN) and neuro-fuzzy logic (FL). In this study, an adaptive inference system (ANFIS) modelling of machine learning (ML) has been implemented to estimate the tribological properties functionally graded materials (FGM). These FGMs were developed using a direct energy deposition (DED) technique additive manufacturing (AM) from SS316L Co27Cr6Mo alloys. The input data ANFIS is acquired experiments done on FGM samples Pin Disc (PoD) apparatus. main objective work predict parameters by creating data-driven predictive model called ANFIS. From findings, was be efficient method wear rate samples.