Prediction of Slurry Erosion–Corrosion in SLM-Produced Ti-6Al-4V Using ANFIS Modeling: Influence of Impact Angles and Erodent Mass

Abstract

Understanding erosion–corrosion mechanisms in selective laser-melted (SLM) Ti-6Al-4V is essential for optimizing component durability in demanding sectors such as oil and gas, hydropower, and offshore engineering, where slurry-induced degradation prevails. Nevertheless, it is challenging to experimentally evaluate slurry erosion–corrosion over a wide range of SLM processing parameters and various slurry erosion–corrosion operating conditions. The adaptive neuro-fuzzy inference system (ANFIS) offers a robust computational approach for modeling complex systems with independent variables, making it well suited for this investigation. This study aims to assess the efficacy of ANFIS in predicting the mass loss of as-built SLM-processed Ti-6Al-4V under slurry erosion–corrosion conditions, with a focus on the synergistic effects of impact angle and erodent mass in both saline and pure water environments, validated against empirical data. The quantitative analysis reveals that erodent mass is the dominant factor influencing mass loss, followed by impact angles. Notably, the combined effect of erodent mass and impact angles in saline environments (e.g., sea water) exacerbates material loss by approximately 16% compared to pure water, highlighting the critical role of electrochemical corrosion in synergy with mechanical erosion. The results demonstrate that the ANFIS model accurately simulates the degradation behavior of SLM-processed Ti-6Al-4V subjected to water–silica slurry impacts within the experimental parameter space; however, predictive generalization beyond these conditions should be interpreted carefully due to validation constraint.