DefinePK

Abstract

We present an artificial neural network (ANN)-based predictive model for the estimation of flank and nose wear of uncoated carbide inserts during orthogonal machining of NST (Nigerian steel) 37.2. Turning experiments were conducted at different cutting conditions on a M300 Harrison lathe using Sandvic Coromant uncoated carbide inserts with ISO designations SNMA 120406 using full factorial design. Cutting speed (v), feed rate (f), depth of cut (d), spindle power (W), and length of cut (l) are the input parameters to both the machining experiments as well as ANN prediction model while the flank wear (VB) and nose wear (NC) were used as the output variables. Nine different structures of multi-layer perceptron neural networks with feed-forward and back-propagation learning algorithms were designed based on the concept presented by Chen et al. (2007) using the MATLAB Neural Network Toolbox. The optimal ANN architecture of 5-12-4-2 with the Levenberg-Marquardt training algorithm and a learning rate of 0.1 was obtained using Taguchi method of experimental design. The results of ANN prediction show that the model generalized well with root mean square errors (RMSE) of 3.55 % and 4.67 % for flank wear and nose wear respectively. With the optimized ANN architecture, parametric study was conducted to show the effects of the cutting parameters on the tool wear. The ANN predictive model captures the dynamic behaviour of the tool wear and can be effectively deployed for online monitoring process.