Optimization and Prediction of Mass Loss During Adhesive Wear of Nitrided AISI 4140 Steel Parts

Adhesive wear has been identified as a significant cause of material loss, representing a substantial challenge across diverse industrial sectors. In order to address this issue, it is imperative to conduct studies with the aim of mitigating this degradation. The present study focuses on achieving a high-quality product with minimal mass loss during adhesive wear by utilizing gas nitriding treatment to optimize the wear parameters of AISI 4140 steel. The present study employed the Taguchi methodology and response surface methodology (RSM) in order to design the experiments. A comprehensive investigation was conducted into the key wear parameters, encompassing sliding speed (V), normal load (FN), and the microhardness of nitrided parts (HV). Furthermore, an artificial neural network (ANN) prediction model was developed to forecast the wear performance of 4140 Steel. The ANN model demonstrated an accuracy of approximately 99% when compared to the experimental data. In order to enhance the precision of wear estimation, prediction optimization was conducted using Bayesian and genetic algorithms. The findings demonstrated that the predicted R2 values exhibited a reasonable alignment with the adjusted R2 values, with a discrepancy of less than 0.2. The analysis demonstrated that the normal load is the most significant factor influencing wear, followed by hardness. In contrast, sliding speed was found to have the least significant impact.