Orthogonal Cutting Investigations on Ti64 Alloy Machining by Using Artificial Neural Networks

Titanium and its alloys are attractive materials due to their unique high strength to weight ratio and their exceptional corrosion resistance. This paper combines the predictive machining approach with neural network modeling of cutting parameters. Experimental work has been performed in orthogonal cutting of Ti-6Al-4V using Plain Carbide tool. At the selected cutting conditions the forces have been measured. The experimental data were utilized to train the developed simulation environment based on back propagation neural network modeling. The cutting speed, feed, depth of cut have been considered as the input parameters and cutting force, feed force as output parameters to develop the model. The trained neural network was used in predicting the cutting parameters. Predictive ANN models were found to be capable of better predictions of forces about 93 to 97% accuracy within the range that they had been trained.