Investigating the Effect of Machining Parameters on Surface Roughness and Machining Force using Artificial Neural Network

When working with hardened materials, it's important to control and optimize the surface roughness and machining force. To achieve this, we can use intelligent methods that are based on prediction and optimization models. In this study, an artificial neural network was used to evaluate the surface roughness and machining force of hardened steel 4140 by analyzing cutting speed, feed rate, and machining time. A full factorial method was used to carry out 27 experiments, and an uncoated cemented carbide tool TCMW 16T304 H13A was used to measure surface roughness and machining force during turning. An artificial neural network model with two hidden layers was selected as the optimal architecture for separately predicting surface roughness and machining force. The predicted values were then compared with the experimental results, and the average error percentage for validation data was calculated as 4.25% for surface roughness and 5.11% for machining force. Finally, the optimal cutting parameters were selected to minimize surface roughness and machining force.