Estimation of cutting forces and tool tip temperature in turning operation with help of artificial neural network

Abstract

Estimation of cutting forces for different metal cutting processes are very important to get the idea about specific cutting power required for the machining and for the design of a machine-fixture-tool system. The main objective of this work is to estimate the machining variables and study the effect of cutting parameters on them. In this work cutting forces as main cutting force, feed force, thrust force and tool tip temperature have been selected as machining variables and their values have been estimated under different combination of cutting parameters such as tool geometry rake angle, entering angle and cutting speed. To study the effect of process parameters initially linear regression equations have been generated and then the effect of process parameters on selected machining variables have been shown with help of 3-D surface plots. For the estimation of machining variables as cutting forces and tool tip temperature, different models have been created with help of an expert system Artificial Neural Network (ANN). In this work different models with combinations of transfer functions, number of neurons, number of epochs have been taken ad best possible model has been found out. Then to train, test the different established Multilayer Feed-Forward Neural Network (MLFFNN) models and for validation the experimental data have been used. The value estimated with help of different ANN models have been collected and find out the coefficient of regression R2, Root Mean Square Error RMSE and Mean absolute percentage error MAPE with respect to actual or experimental values. Based on the higher value of R2, lower value of RMSE and MAPE best model has been selected. With help of selected model machining variables have been estimated and compared with actual values. From this research work it has been pointed out that the model with Bayesian Regularization transfer function, 30 number of neurons, 300 number of epochs performed well with R2 = 0.9996, RMSE = 6.161 and MAPE = 1.67 %. This model provide better performance as compared to other ANN models have been trained with Levenberg-Marquardt and Conjugate Gradient training function.