• Hakimian, Soroosh
• Bouzid, Abdel-Hakim
• Pourrahimi, Shamim

Abstract

<jats:p>Predicting the corrosion behavior of materials in specific environmental conditions is important for establishing a sustainable manufacturing system while reducing the need for time-consuming experimental investigations. Recent studies started to explore the application of supervised Machine Learning (ML) techniques to forecast corrosion behavior in various conditions. However, there is currently a research gap in utilizing classification ML techniques specifically for predicting the corrosion behavior of stainless steel (SS) material in lactic acid-based environments, which are extensively used in the pharmaceutical and food industry. This study presents a ML-based prediction model for corrosion behavior of SSs in different lactic acid environmental conditions, using a database that described the corrosion behavior by qualitative labels. Decision tree (DT), random forest (RF) and support vector machine (SVM) algorithms were applied for classification. Training and testing accuracies of, respectively 97.5% and 92.5% were achieved using the DT classifier. Four SS alloy composition elements (C, Cr, Ni, Mo), acid concentration, and temperature were found sufficient to consider as input data for corrosion prediction. The developed models are reliable for predicting corrosion degradation and, as such, contribute to avoiding failures and catastrophes in industry.</jats:p>

Topics

• impedance spectroscopy
• stainless steel
• corrosion
• random
• machine learning
• alloy composition