Journal Article

Supervised Machine Learning Models for Predicting SS304H Welding Properties Using TIG, Autogenous TIG, and A-TIG

Haldar, Barun, Mondal, Subrata, Tadepalli, Srinivas, Joardar, Hillol, Saha, Subhodwip, Das, Santanu

2025

Overview

This investigation explores the application of supervised machine learning regression approaches to predict various responses, including penetration, bead width, bead height, hardness, ultimate tensile strength, and percentage elongation in autogenous TIG-, A-TIG-, and TIG-welded joints of SS304H, which is considered as an advanced high-temperature resistant material. The machine learning (ML) models were constructed based on the data gathered from 50 experimental runs, considering eight key input variables: gas flow rate, torch angle, filler material, welding pass, flux application, root gap, arc gap, and heat input. A total of 80% of the collected dataset was used for training the models, while the remaining 20% was reserved for testing their performance. Six ML algorithms—Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting Regression (GBR), and Extreme Gradient Boosting (XGBoost)—were implemented to assess their predictive accuracy. Among these, the XGBoost model has demonstrated the highest predictive capability, achieving R2 scores of 0.886 for penetration, 0.926 for width, 0.915 for weld bead height, 0.868 for hardness, 0.906 for ultimate tensile strength, and 0.926 for percentage elongation, along with the lowest values of RMSE, MAE, and MSE across all responses. The outcomes establish that machine learning models, particularly XGBoost, can accurately predict welding characteristics, marking a significant advancement in the optimization of TIG welding parameters. Consequently, integrating such predictive models can substantially enhance the precision, reliability, and overall efficiency of welding processes.

• Publisher: MDPI AG
• Subject: A-TIG / Algorithms / Artificial neural networks / autogenous TIG / Computational linguistics / Efficiency / Elongation / Equipment and supplies / Gas flow / Gas tungsten arc welding / Geometry / Hardness / Height / High temperature / Information management / Language processing / Machine learning / Mechanical properties / Metals / Natural language interfaces / Neural networks / Prediction models / process parameter optimization / Regression / Stainless steel / Supervised learning / supervised machine learning / Support vector machines / Teaching / Tensile strength / Ultimate tensile strength / Wear resistance / Welded joints / Welding / Welding fluxes / Welding parameters