Application of Machine Learning Algorithms to Classify and Predict Corrosion Behavior of Stainless Steels in Lactic Acid

Abstract

Corrosion of metals is a critical issue, which causes a considerable amount of loss in industries. There are numerous factors that can influence corrosion, including chemical composition and multiple environmental conditions. Hence, it is difficult to determine the relation between individual environmental factors and corrosion processes based on physics-based corrosion laws or to predict the corrosion life of materials. Predicting corrosion behavior of materials in any type of environment is important since testing materials in each different environment is time-consuming and expensive. Hence, analyzing corrosion data and, subsequently, predicting corrosion behavior needs advanced data mining methods. Recently, machine learning (ML) methods have been extensively used in materials research thanks to their powerful data mining capabilities. By learning from sample data and experience, it automates the searching for knowledge without reliance on predetermined equations. In corrosion research, machine learning algorithms such as random forest, support vector regression, and artificial neural networks have been used to study the corrosion behavior.Stainless steels are widely used in corrosive environments. This research aims to develop classification methods for predicting stainless steel corrosion behavior in different concentrations of lactic acid and different temperatures. The Handbook of corrosion data was used to gather data on stainless steel corrosion in lactic acid. Outlier, repeated, and missing data were treated during a pre-processing step (Figure 1). Based on the ML results, we can properly predict the corrosion behavior of various grades of stainless steels, in different lactic acid concentration and test temperatures. The best training and testing accuracies are 98.73% and 90.00%, respectively, which are obtained by fitting the decision tree classifier. It is also concluded that the percentage of four essential elements (C, Cr, Ni, Mo) in stainless steel alloys, alongside acid concentration and temperature, can be used as the input data to predict the corrosion behavior. Receiver operating characteristic (ROC) curves indicate that stainless steels with poor corrosion behavior are correctly classified by support vector machine (SVM) multiclassification modeling. Therefore, the SVM algorithm is reliable for detecting stainless steels with poor corrosion behavior, which are highly risky for critical applications if chosen incorrectly. Figure 1