Enhancing Welding Quality Through Predictive Modelling — Insights From Machine Learning Techniques
Year: 2024
Type: article
Abstract: In this work, the application of various machine learning (ML) algorithms for predicting tensile strength based on welding parameters in AA2014-T6 aluminium alloy joints is studied. Six ML models namely linear regression, AdaBoost regression, random forest regression, support vector regression (SVR), multi-layer perceptron regression and gaussian process regression (GPR) are considered. The comprehensive analysis revealed that SVR exhibited superior generalization capabilities on unseen data, achieving an R² of 0.89 and a low RMSE of 15.64. In contrast, GPR, despite its high training accuracy, showed significant overfitting. This work highlights the potential of ML in optimizing welding parameters and highlights the importance of model selection and tuning to prevent overfitting and ensure reliable predictions.
Source: MM Science Journal
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4405003193