  To advance quality assurance in the welding process, this study presents a
robust deep learning model that enables the prediction of two critical welds
Key Performance Characteristics (KPCs): welding depth and average pore volume.
In the proposed approach, a comprehensive range of laser welding Key Input
Characteristics (KICs) is utilized, including welding beam geometries, welding
feed rates, path repetitions for weld beam geometries, and bright light weld
ratios for all paths, all of which were obtained from hairpin welding
experiments. Two deep learning networks are employed with multiple hidden dense
layers and linear activation functions to showcase the capabilities of deep
neural networks in capturing the intricate nonlinear connections inherent
within welding KPCs and KICs. Applying deep learning networks to the small
numerical experimental hairpin welding dataset has shown promising results,
achieving Mean Absolute Error (MAE) values as low as 0.1079 for predicting
welding depth and 0.0641 for average pore volume. Additionally, the validity
verification demonstrates the reliability of the proposed method. This, in
turn, promises significant advantages in controlling welding outcomes, moving
beyond the current trend of relying merely on monitoring for defect
classification.
