Modeling and analysis of WEDM with a new deep neural network

Abstract

In some cases, the models targeted in machining processes are difficult to apply to industrial production. Artificial intelligence techniques applied to industrial production methods in recent times eliminate these problems in machining. The use of deep learning methods in solving these problems has recently increased. In this study, a new deep neural network (DNN) based on artificial neural networks is proposed to model the machining of AISI4140 steel by wire electrical discharge machining (WEDM). The proposed DNN model predicts surface roughness (SR) and wire wear ratio (WWR), which are the most important inferences in cutting with WEDM. Obtaining inferences such as SR and WWR in a real experimental environment means a lot of time and cost. With the proposed model, there is a significant reduction in time and cost factors. The dataset used to train and test the proposed model was obtained from the real experimental environment. For training and testing the model, the data set is divided into two parts: 20% test data and 80% training data. In the experimental tests carried out with the proposed model, error rates such as Mean Absolute Error (MAE), R2, and Mean Squared Error (MSE) accuracy rate between SR and WWR values estimated according to the SR and WWR values obtained from the real environment were examined. MAE, MSE, and R2 for SR reached 0.0495, 0.0005, and 98.48%, respectively, while MAE, MSE, and R2 for WWR reached 0.0156, 0.0036, and 98.39%, respectively. These results of the proposed model provided a lower error rate and higher accuracy than the state-of-the-art models in the literature.