Prediction of surface roughness in milling with a ball end tool using an artificial neural network

Introduction. Milling stainless steel with a ball-end tool is a complex technological process that requires precise control of processing parameters to ensure high surface quality. In this regard, it is an urgent task to develop methods for predicting roughness parameters, such as Rz. The aim of this work is to develop a predictive neural network model that can estimate surface roughness when milling stainless steel using a ball-end tool. Method and methodology. The main focus is on error backpropagation and gradient descent methods, as well as hyperparameter tuning, which are necessary to prevent overfitting and underfitting of the model. Experimental studies include the analysis of both controlled variables, such as feed per tooth, angle of inclination and diameter of the tool, and uncontrolled, including coolant supply and tool wear. Results and discussions. The use of coolant for milling austenitic steel has reduced the roughness parameters Rz by an average of 14%. A strong negative correlation has been established between the dimensional wear of the tool and the parameter Rz (−0.95). At the same time, wear in the range of 2…4 μm affects an increase in the Rz parameter by 21% compared to the minimum values. The data obtained were used to train eight configurations of artificial neural networks, which were used to predict roughness using the Rz parameter. The results show that the 3-16-16-1 network configuration showed the lowest MSE (0.0313), followed by 3-20-14-1 (0.0470) and 3-64-64-1 (0.0481), respectively. In addition, these configurations also demonstrated the lowest average absolute error values, which demonstrate the average of the absolute differences between predicted and observed values (0.1014; 0.1251 and 0.1155, respectively), and the coefficient of determination, which is a statistical measure indicating the proportion of data variability explained by the model (0.9944; 0.9916; 0.9904). A comparison of the experimental data with the predictions of various models allowed us to determine the average value of the absolute differences for the models according to the parameter Ra ≈ 0.074. The study suggests approaches to training neural network models for accurate prediction of roughness parameters, which makes a significant contribution to the methods of modeling machining processes.