Machine learning prediction for low-alloy steel strength

Abstract

The experimental measurement of the strength of low-alloy steel is very cumbersome, but it is also essential to knowledge its strength. In this study, two machine learning methods, random forest (RF) and support vector machine (SVM), were used to study the strength of low-alloy steels on the existing data samples of low-alloy steels, so as to make relevant predictions on their strengths and find the most influential factors. Comparing the measured results with the predicted values shows that RF outperform SVM in predicting results. And by calculating the correlation coefficient, the two features that have the greatest influence on the strength are the temperature and the content of V, respectively. This result can be used to optimize the properties of low-alloys.