Machine Learning-Based Pitting Rate Classification and Prediction for 316L Stainless Steel in NaClO3 and NaCl Environment

The 316L stainless steel is widely utilized as structural material in hydrogen production industry due to its excellent combination of corrosion resistance and mechanical properties. However, it remains susceptible to localized pitting corrosion in chloride-containing high-temperature environments. Especially, the main electrolysis byproduct sodium chlorate (NaClO3) also has complicated effect on pitting corrosion. Therefore, evaluating and predicting the pitting severity grades of 316L steel in NaClO3 and NaCl environment is essential for controlling operation risks. In recent years, machine learning (ML) methods have gained significant attention in the field of corrosion prediction; however, existing research has primarily focused on the regression prediction of continuous parameters, while studies dedicated to the classification and evaluation of pitting severity grades remain relatively limited. Furthermore, experimental datasets are commonly constrained by small sample sizes and imbalanced class distributions, which hinder the performance enhancement of classification models. Based on experimental pitting data of 316L stainless steel, this study employs ADASYN (Adaptive Synthetic Sampling) to mitigate data imbalance and develops a Feedforward Neural Network (FFNN) for pitting rate classification. The proposed model is compared and analyzed against several commonly used machine learning models. Through a comprehensive evaluation of predictive performance, the feasibility of the developed model in pitting severity grading is verified, thereby providing a novel approach for the predictive evaluation of the pitting corrosion of 316L stainless steel.