Machine learning models for determination of weldbead shape parameters for gas metal arc welded T-joints -- A comparative study
This work addresses weld quality assessment for numerical analysis in welding engineering, but it is incremental as it compares existing methods on new data.
The study tackled predicting weld bead shape parameters for gas metal arc welded T-joints using multiple linear regression (MLR) and artificial neural networks (ANN), finding that MLR-based models performed better in predictability and error assessment.
The shape of a weld bead is critical in assessing the quality of the welded joint. In particular, this has a major impact in the accuracy of the results obtained from a numerical analysis. This study focuses on the statistical design techniques and the artificial neural networks, to predict the weld bead shape parameters of shielded Gas Metal Arc Welded (GMAW) fillet joints. Extensive testing was carried out on low carbon mild steel plates of thicknesses ranging from 3mm to 10mm. Welding voltage, welding current, and moving heat source speed were considered as the welding parameters. Three types of multiple linear regression models (MLR) were created to establish an empirical equation for defining GMAW bead shape parameters considering interactive and higher order terms. Additionally, artificial neural network (ANN) models were created based on similar scheme, and the relevance of specific features was investigated using SHapley Additive exPlanations (SHAP). The results reveal that MLR-based approach performs better than the ANN based models in terms of predictability and error assessment. This study shows the usefulness of the predictive tools to aid numerical analysis of welding.