  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.

Machine Learning