Penetration State Identification from Stereo Image Pair of Weld Pool in GMAW Process by Deep Learning

Abstract

The 3D shape and oscillation of weld pool surface contain abundant significant information related to the welding penetration state, which provides clues to control the welding process. In this paper, a biprism stereo vision system based on a single camera was established to sense the 3D surface of weld pool under different penetration states during the pulsed gas metal arc welding process with a V shape groove. We utilize Pyramid Stereo Matching Network to calculate a high-quality disparity map for weld pool image pair, which fuses features from different levels to improve the disparity estimation accuracy in high light and textureless regions. The disparity map contains two and three dimensions information directly related to the penetration state. Then the disparity maps were input into the Residual Neural Network to extract deeper-level features for training and testing. In our experiments, the welding penetration states were classified as four classes, i.e. partial penetration, full penetration, over penetration and burn through. Testing experiments demonstrated 99.7% as the penetration state identification accuracy.