TY  - EJOU
AU  - Wang, Ziyang 
AU  - Duan, Lian 
AU  - Kuang, Lei 
AU  - Zhou, Haibo 
AU  - Duan, Ji’an 

TI  - Physics-Informed Gaussian Process Regression with Bayesian Optimization for Laser Welding Quality Control in Coaxial Laser Diodes
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 84
IS  - 2
SN  - 1546-2226

AB  - The packaging quality of coaxial laser diodes (CLDs) plays a pivotal role in determining their optical performance and long-term reliability. As the core packaging process, high-precision laser welding requires precise control of process parameters to suppress optical power loss. However, the complex nonlinear relationship between welding parameters and optical power loss renders traditional trial-and-error methods inefficient and imprecise. To address this challenge, a physics-informed (PI) and data-driven collaboration approach for welding parameter optimization is proposed. First, thermal-fluid-solid coupling finite element method (FEM) was employed to quantify the sensitivity of welding parameters to physical characteristics, including residual stress. This analysis facilitated the identification of critical factors contributing to optical power loss. Subsequently, a Gaussian process regression (GPR) model incorporating finite element simulation prior knowledge was constructed based on the selected features. By introducing physics-informed kernel (PIK) functions, stress distribution patterns were embedded into the prediction model, achieving high-precision optical power loss prediction. Finally, a Bayesian optimization (BO) algorithm with an adaptive sampling strategy was implemented for efficient parameter space exploration. Experimental results demonstrate that the proposed method effectively establishes explicit physical correlations between welding parameters and optical power loss. The optimized welding parameters reduced optical power loss by 34.1%, providing theoretical guidance and technical support for reliable CLD packaging.
KW  - Coaxial laser diodes; laser welding; physics-informed; Gaussian process regression; Bayesian optimization

DO  - 10.32604/cmc.2025.065648