A novel intelligent tool wear monitoring system in ball end milling of Ti6Al4V alloy using artificial neural network

Abstract The geometry and sharpness of the cutting tool have a substantial impact on the final product’s quality. The geometry of cutting edges is altered throughout the machining process, and wear causes the cutting edge to become dull. This causes increased surface roughness, dimensional inaccuracy, cutting forces, chatter, and vibration. The present research focuses on tool wear (Vb) under dry machining conditions during ball end milling of Ti6Al4V alloy. The experiments are conducted using the full factorial design of experiments with three parameters, viz. feed ( f ), depth of cut ( A t ), and rotational speed ( S ) at three levels. A total of 27 experiments are conducted with one replicate. Artificial neural network (ANN) with 3-18-2-1 architecture is used for the study of the tool wear monitoring (TWM) system. Results revealed that the TWM model is highly adequate, with R 2 = 99.89% and R 2 adj = 99.65%. The percentage contribution of A t is the highest, amounting to 80.6%, followed by feed of 12.46%. The rotational speed has the least contribution to tool wear, amounting to 1.5%. From ANN modeling, R 2 value testing is found to be 0.9974, which is close to unity and reveals that the trained model excellently fitted the testing data. The model accuracy is also found to be 96.46%.