Three Dimensional Surface Reconstruction Method for the Welding Pool Using the Fuzzy Neural Network

Abstract

In order to automatically control the quality of the welding process, this paper concentrates on the problem of three dimensional surface reconstructions for the welding pool. As the relationships between size and shape of the welding pool are very complex and nonlinear, we utilize the fuzzy neural network to solve the proposed problem. In the fuzzy neural network, the input vector with 48 dimensions is made up of three parts: 1) welding parameters, 2) welding pool size parameters, and 3) shape parameters. In particular, the size of the welding pool negative side is regarded as the output. In the experiment, the welding process is implemented using direct current electrode negative GTAW, and then we suppose that the weld pool rotates when torch orientation, imaging plane, laser projector, and camera are fixed. Experimental results demonstrate that 1) the speed of the fuzzy neural network training process is fairly quick, and 2) the proposed three dimensional surface reconstruction method is robust under current disturbances.