Few-shot transfer learning for laser welding prediction

Laser wire filling welding is a key joining technique in the manufacturing of aluminum battery packs for new energy vehicles, yet predictive modeling of melt pool geometry remains limited by scarce experimental data. A two-stage transfer learning framework is developed by integrating multiphysics numerical simulation, data augmentation, and Bayesian neural network (BNN). High-fidelity multiphysics simulations within the experimental process window are generated to expand parameter space coverage and to provide physics-informed data for model pretraining. Limited experimental samples are augmented using a Wasserstein Generative Adversarial Network with gradient penalty applied to laser power and wire feed speed. Gaussian perturbations on travel speed are introduced to represent measurement uncertainties. A shallow BNN is pretrained on simulated samples and fine-tuned on the augmented experimental dataset using physics-consistent regularization and partial layer-freezing strategies. The augmentation strategy is evaluated through leave-one-out cross-validation on eight experimental samples, and generalization is examined using a separate test under previously unobserved travel-speed conditions. After inverse normalization, the framework achieves root mean square errors of 0.027 mm for melt pool depth and 0.025 mm for width, with coefficients of determination of 0.788 and 0.741, respectively. Uncertainty-aware quantitative analysis based on Sobol sensitivity indices and reliability assessment is conducted after model validation to characterize dominant parameter influences and to identify high-confidence process windows under limited data conditions. The proposed framework provides a general simulation-informed and uncertainty-aware learning strategy for manufacturing processes with severely limited experimental data.