Physics-inspired feature engineering and explainable machine learning for tensile strength prediction of CNT–Si₃N₄ reinforced FWTPET joints
Year: 2026
Type: article
Abstract: Introduction: The prediction of the tensile strength of carbon nanotube (CNT)-silicon nitride (Si₃N₄) reinforced friction welding of tube-to-tube plate using an external tool (FWTPET) joints is crucial for improving joint performance. Methods: A physics-inspired machine learning framework was developed using 25 experimentally derived observations to investigate the relationships between welding parameters, engineered process descriptors, and tensile strength. Physically motivated descriptors were constructed to represent thermal input, reinforcement interaction, material flow, and dispersion effects. Hyperparameter optimization was performed for multiple regression models to improve predictive performance. Results: The XGBoost model showed the best performance, with an MAE of 5.70, an RMSE of 7.74, and an R² of 0.9772. SHAP analysis identified CNT content and the dispersion-related descriptor as the most influential predictors. Ablation analysis showed that the combination of the original experimental process variables provided greater predictability than the engineered variables alone or their combination, while the physics-inspired process variables provided further physically interpretable representations of the welding process. Discussion: The results indicate that small-data machine learning, domain-based feature engineering, and explainable analysis are potential techniques for predicting the tensile strength of FWTPET joints.
Institution: Vellore Institute of Technology University
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,353
OpenAlex ID: W7223015419