Focus to generalize (F2G): physics-guided attention for sample efficient and generalizable deep learning defect detection

Despite the promising potential of deep learning (DL) for real-time defect detection in metal additive manufacturing (AM), truly generalizable models remain elusive. While DL excels under controlled laboratory conditions, its performance declines sharply when confronted with printing scenarios that diverge from the training data—a challenge known as low-domain generalization. The few AM studies focusing on the domain generalization problem rely on physics-unguided and pure data-driven approaches that are sample-inefficient. This study introduces “Focus to Generalize” (F2G), a novel DL approach for generalized humping detection in metal AM that bridges the gap between a physics-based understanding of the process and data-driven learning. F2G addresses the challenge of domain generalization through physics-guided attention. It incorporates domain knowledge to direct the model’s focus toward printing-parameters-invariant defect dynamics (i.e., domain-invariant). This enables F2G to learn features that are both class-discriminative and domain-invariant, effectively allowing the DL model to generalize beyond its training printing condition. Qualitatively, F2G’s superiority is evident through enhanced feature representation and attention maps, as demonstrated by Grad-CAM and U-MAP visualizations. Quantitatively, F2G enhances generalization capabilities compared to the physics unguided models, successfully detecting humping in all of testing cases printed with parameters unseen during the model training with test accuracy of 82%. This represents a significant improvement over the physics-unguided model, which achieved 59% accuracy. F2G’s success motivates further research on utilizing physics understanding for improving domain generalization and sample efficiency. These qualities are essential for aligning DL capabilities with the requirements of AM-driven mass customization.