A physics-informed optimization framework for mitigating porosity defects in laser beam welding

Laser beam welding (LBW) in keyhole mode enables high-productivity joining for modern manufacturing processes, yet its industrial deployment is hindered by porosity defects that degrade weld quality and process reliability. This work presents a physics-informed optimization framework designed to systematically mitigate porosity in aluminum LBW by integrating multi-physics modeling, experimental data, and machine-learning-based predictive analytics. The framework couples a series of predictive physics-informed machine learning models (penetration predictor, porosity predictor, and physics estimator) with evolutionary and Bayesian optimization strategies to identify optimal process parameters across a wide operating space of laser power, welding speed, beam diameter, and focal position. High-fidelity thermal–fluid simulations and comprehensive experiments were used to train and validate the predictive models. The framework consistently converged toward parameter sets that achieve target penetration depths while suppressing porosity, revealing the inherent trade-off between weld penetration and defect formation. Beyond accurate prediction and optimization, the approach provides clear interpretability by quantifying key physical factors, such as keyhole stability, weld pool morphology, and local solidification rates, that govern porosity formation. The results demonstrate the potential of physics-informed machine learning as a scalable tool for quality-driven process control and intelligent optimization in advanced manufacturing processes.