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- Title
Modeling Yield Strength of Austenitic Stainless Steel Welds Using Multiple Regression Analysis and Machine Learning.
- Authors
Park, Sukil; Choi, Myeonghwan; Kim, Dongyoon; Kim, Cheolhee; Kang, Namhyun
- 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.
- Subjects
STAINLESS steel welding; AUSTENITIC stainless steel; MULTIPLE regression analysis; STEEL welding; MACHINE learning; STAINLESS steel
- Publication
Metals (2075-4701), 2023, Vol 13, Issue 9, p1625
- ISSN
2075-4701
- Publication type
Article
- DOI
10.3390/met13091625