Modeling Yield Strength of Austenitic Stainless Steel Welds Using Multiple Regression Analysis and Machine Learning

Abstract

Designing welding filler metals with low cracking susceptibility and high strength is essential in welding low-temperature base metals, such as austenitic stainless steel, which is widely utilized for various applications. A strength model for weld metals using austenitic stainless steel consumables has not yet been developed. In this study, such a model was successfully developed. Two types of models were developed and analyzed: conventional multiple regression and machine-learning-based models. The input variables for these models were the chemical composition and heat input per unit length. Multiple regression analysis utilized five statistically significant input variables at a significance level of 0.05. Among the prediction models using machine learning, the stepwise linear regression model showed the highest coefficient of determination (R2) value and demonstrated practical advantages despite having a slightly higher mean absolute percentage error (MAPE) than the Gaussian process regression models. The conventional multiple regression model exhibited a higher R2 (0.8642) and lower MAPE (3.75%) than the machine-learning-based predictive models. Consequently, the models developed in this study effectively predicted the variation in the yield strength resulting from dilution during the welding of high-manganese steel with stainless-steel-based welding consumables. Furthermore, these models can be instrumental in developing new welding consumables, thereby ensuring the desired yield strength levels.

Keywords

• multiple regression analysis
• machine learning
• yield strength
• weld metal
• austenitic stainless steel