Laser Welding Strength Prediction Using Neural Network Techniques

Laser welding stands out as one of the most precise and efficient manufacturing techniques, with its ability to generate minimal heat‐affected zones and limit material distortion. This study introduces a cutting‐edge neural network–based predictive model designed to estimate tensile strength and welding deformation in laser welding operations. By incorporating three critical input parameters, laser incident angle, laser velocity, and laser power, the model harnesses the power of a neural network to refine process optimization and elevate the quality of welded joints. Among the tested models, the Bayesian regularization (BR) model demonstrated superior accuracy, achieving a remarkably low mean absolute error (MAE) of just 0.0001982. In contrast, the Levenberg–Marquardt (LM) model yielded an MAE of 89.29, while the scaled conjugate gradient (SCG) model recorded an MAE of 41.67. These findings underscore the effectiveness of the BR model in enhancing predictive accuracy for laser welding applications.