Engineering Low-Friction Carbonitrided Steel Surfaces: A Joint RSM–ANN Study of Multi-Pass Scratch Behavior in AISI 4130

This paper concerns the optimization of the tribological properties of carbonitrided steel, a matter of particular significance in the design of highly stressed, hardened mechanical components. The study aims to explore the simultaneous treatment of microhardness, normal load and number of passes as jointly optimized design variables for the multi-pass scratch friction response of carbonitrided AISI 4130 steel. It employs a full-factorial response-surface (RSM) approach, in conjunction with a desirability-function optimization and an artificial neural network (ANN) model, with the objective of both explaining and predicting this response. The microstructural and mechanical properties of carbonitrided AISI 4130 steel were first examined by looking at how the steel wears in tribological applications. The microstructure of the carbonitrided steel was analyzed using optical microscopy and X-ray diffraction (XRD), with varying carbon-potential and tempering parameters. As the tempering temperature increased, the carbonitrided steel demonstrated a decrease in microhardness, consistent with progressive relief of quenching-induced stresses and decomposition of retained austenite. The surface microhardness ranged from 630 HV0.1 (C12, tempered at 550 °C) to 980 HV0.1 (C2), against 270 HV0.1 for untreated steel. To investigate the friction coefficient, a multi-pass scratching approach was employed, utilizing a full-factorial design of experiments (4 × 4 × 4 combinations of hardness, normal load and number of passes). The experimental data were analyzed by response surface methodology (RSM) with a desirability-function approach, and by an artificial neural network (ANN) trained with the standard back-propagation algorithm. The correlation coefficient (R2) of 0.993 demonstrates a strong agreement between the model predictions and the experimental results. The findings establish a validated, transferable quantitative relationship between carbonitriding-induced hardness gradients and adhesive-wear friction behavior, providing a predictive framework that can be extended to other case-hardened low-alloy steels subjected to multi-pass sliding degradation.