Abstract
Pitting corrosion represents a substantial threat to passive metals, potentially leading to stress-corrosion cracking and the abrupt failure of materials. The complex relationship between the internal alloy compositions and external environmental factors complicates the precise prediction of pitting probability. In this study, a hybrid machine learning strategy is developed to predict the probability and boundary of pitting corrosion in stainless steel, considering the collaborative effects of different factor pairs rather than a single factor. The strategy integrates both black-box (adaptive boosting) and white-box (symbolic classification) algorithms, where the former is constructed to determine the pitting probability with an F1 score of 0.85, and the latter is designed to achieve recognition of critical factor pairs. This work aims to offer a systematic diagram and new insights in understanding and optimizing the pitting resistance of stainless steel.
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