Predictions of Tool Wear in Hard Turning of AISI4140 Steel through Artificial Neural Network, Fuzzy Logic and Regression Models

Over the past few decades machining of hardened components has become a reality by means of hard turning. The cheaper coated carbide tool is seen as a substitute for cubic boron nitride (CBN) inserts in the hard turning; however, the tool flank wear is an unavoidable phenomenon when using coated carbide tools during hard turning. In this investigation, the cutting tool wear estimation in coated carbide tools using regression analysis, fuzzy logic and Artificial Neural Network (A–NN) is proposed. Work piece taken into consideration is AISI4140 steel (47 HRC). Experimentation is based on response surface methodology (RSM) as per design of experiments. The cutting speed (V), feed (f) and depth of cut (d) are taken as the inputs and the tool flank wear is the output. Results reveal that ANN provides better accuracy when compared to regression analysis and Fuzzy logic.