Development of Tensile Shear Strength Prediction Model for GMAW Welds Using Laser Vision Sensor and Gaussian Process Regression

This study developed a model to predict the tensile shear strength of Gas Metal Arc Welding (GMAW) welds using statistical analysis of laser vision sensor profiles and Gaussian Process Regression (GPR). Despite GMAW's widespread use, welding defects due to disturbances like thermal deformation can compromise mechanical properties. To address this, weld bead external profile data, acquired via a laser vision sensor, and process variables were utilized for model development. A cumulative sum (CUSUM)-based change point detection algorithm extracted top and bottom plate boundary points rapidly and accurately from weld bead profiles. These points were transformed to minimize measurement condition influence. Subsequently, a GPR model was constructed, employing these transformed feature points and process variables as inputs, to predict tensile shear strength. The model demonstrated excellent predictive performance with an R2 of 0.9587, RMSE of 13.9447 MPa, and MAPE of 8.6958 %. Analysis revealed voltage setting was the most influential variable in predicting tensile shear strength. Transformed feature point coordinates, representing the distance from the bottom plate boundary to the weld reinforcement feature point, also showed significant influence. This study confirmed that the tensile shear strength of GMAW weldments can be predicted accurately with limited input data and fast processing time.