In this study, an approach based on artificial neural network (ANN) was proposed to predict the experimental cutting temperatures generated in orthogonal turning of AISI 316L stainless steel. Experimental and numerical analyses of the cutting forces were carried out to numerically obtain the cutting temperature. For this purpose, cutting tests were conducted using coated (TiCN+Al2O3 +TiN and Al2O3) and uncoated cemented carbide inserts. The Deform-2D programme was used for numerical modelling and the Johnson–Cook (J–C) material model was used. The numerical cutting forces for the coated and uncoated tools were compared with the experimental results. On the other hand, the cutting temperature value for each cutting tool was numerically obtained. The artificial neural network model was used to predict numerical cutting temperatures by means of the numerical cutting forces. The best results in predicting the cutting temperature were obtained using the network architecture with a hidden layer which has seven neurons and LM learning algorithm. Finally, the experimental cutting temperatures were predicted by entering the experimental cutting forces into a formula obtained from the artificial neural networks. Statistical results (R 2, RMSE, MEP) were quite satisfactory. This demonstrates that the established ANN model is a powerful one for predicting the experimental cutting temperatures. yield stress (MPa) output value aluminum oxide number of pattern artificial neural network absolute fraction of variance hardening modulus (MPa) root mean square error back propagation scaled conjugate gradient learning algorithm strain rate sensitivity parameter temperature of work material (°C) cutting tools room temperature (°C) main cutting force (N) melting temperature of work material (°C) feed force (N) cutting temperature (°C) feed rate (mm/rev) titanium carbo-nitride transfer function titanium nitride heat transfer coefficient of work material (kW/m2 °C) target value processing elements cutting speed (m/min) shear flow stress of chip (MPa) the weights of the connections between ith and jth processing elements Levenberg–Marquardt learning algorithm the weights of the biases between layers shear friction factor the output of the jth processing element thermal softening coefficient yield stress (MPa) absolute mean error percentage (%) strain rate (sn−1) the weighted sum of the input to the ith processing element reference strain rate (sn−1) strain hardening index equivalent plastic strain rate (sn−1) number of processing elements in the previous layer shear stress at the tool–chip interface (MPa) 
Prediction of cutting temperature in orthogonal machining of AISI 316L using artificial neural network