Authors
Krishnan Venkatachalam, Senthil Kumaran Selvaraj, R Lokeshkumar, G Sundaramali
Published in
Frontiers in artificial intelligence. Volume 9. Pages 1952539. Epub Oct 20, 2026.
Abstract
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.
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.
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.
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.
PMID:
42978746
Bibliographic data and abstract were imported from PubMed on 04 Nov 2026.
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