TY - JOUR ID - 73088 TI - Predictions of Tool Wear in Hard Turning of AISI4140 Steel through Artificial Neural Network, Fuzzy Logic and Regression Models JO - International Journal of Engineering JA - IJE LA - en SN - 1025-2495 AU - Rajeev, D AU - Dinakaran, D AU - Kanthavelkumaran, N AU - Austin, N AD - Research Scholar, Mechanical Engineering, Hindustan University, Chennai, India AD - Department of Mechanical Engineering, Hindustan University, Chennai, India AD - Department of Mechanical Engineering, Arunachala College of Engineering for Women, Manavilai, Kanyakumari, Tamilnadu, India AD - Department of Mechanical Engineering, Mar Ephraem College of Engineering and Technology, Elavuvilai, Tamilnadu, India Y1 - 2018 PY - 2018 VL - 31 IS - 1 SP - 32 EP - 37 KW - AISI4140 KW - ANN KW - Hard Turning KW - Regression DO - N2 - The tool wear is an unavoidable phenomenon when using coated carbide tools during hard turning of hardened steels. This   work focuses on the prediction of tool wear using regression analysis and artificial neural network (ANN).The work piece taken into consideration is AISI4140 steel hardened to 47 HRC. The models are developed from the results of experiments, which are carried out based on Design of experiments (Response surface methodology).  The cutting speed, feed and depth of cut are taken as the inputs and the wear is the output. The results reveal that the ANN provides better accuracy when compared to Regression analysis. UR - https://www.ije.ir/article_73088.html L1 - https://www.ije.ir/article_73088_895298fb7dc313c53d1ce64054295bbc.pdf ER -