Prediction of weld quality in laser welding of hardmetal and steel using high-speed imaging and machine learning methods

Abstract

Laser welding of steel and hardmetal presents significant challenges due to their differing material properties. Improper laser welding parameters can result in unstable joints, ultimately leading to reduced mechanical strength of the weld. Therefore, defining an optimal process window is critical to ensuring weld quality. In addition, a continuous process monitoring method like High-Speed Imaging (HSI) is essential in real industrial applications to maintain stability and detect potential defects. Understanding plume dynamics helps identify the most important features of weld quality, but it also provides deeper insight into operational parameters that discriminate different weld types. Analysis of individual image plume frames from HSI reveals distinct statistical features that are identified as unique to each welding condition. Performing systematic feature selection using plume morphology, spatter generation and weld quality, we achieved>95 % leveraging Machine Learning (ML) classifiers. Particularly, Gradient Boosting Classifier (GBC), Linear Discriminant Analysis (LDA), Multinomial Logistic Regression (MNL-LR), Support Vector Machine (SVM), and Random Forest (RF), where the RF obtained >99 % classification accuracy of weld quality. The RF was then used in performing Recursive Feature Elimination (RFE), and with the robustness analysis, we managed to reduce the number of features from forty-nine to nine features while maintaining satisfactory performance (Accuracy = 0.981, F1-score = 0.961, AUROC = 0.997). The position of the weld plume, plume eccentricity and plume width are the most essential features that lead to the improvement of node purity and classification accuracy.