Feature Point Identification in Fillet Weld Joints Using an Improved CPDA Method

Abstract

An intelligent, vision-guided welding robot is highly desired in machinery manufacturing, the ship industry, and vehicle engineering. The performance of the system greatly depends on the effective identification of weld seam features and the three-dimensional (3D) reconstruction of the weld seam position in a complex industrial environment. In this paper, a 3D visual sensing system with a structured laser projector and CCD camera is developed to obtain the geometry information of fillet weld seams in robot welding. By accounting for the inclination characteristics of the laser stripe in fillet welding, a Gaussian-weighted PCA-based laser center line extraction method is proposed. Smoother laser centerlines can be obtained at large, inclined angles. Furthermore, an improved chord-to-point distance accumulation (CPDA) method with polygon approximation is proposed to identify the feature corner location in center line images. The proposed method is validated numerically with simulated piece-wise linear laser stripes and experimentally with automated robot welding. By comparing this method with the grayscale gravity method, Hessian-matrix-based method, and conventional CPDA method, the proposed improved CPDA method with PCA center extraction is shown to have high accuracy and robustness in noisy welding environments. The proposed method meets the need for vision-aided automated welding robots by achieving greater than 95% accuracy in corner feature point identification in fillet welding.