F2GAN based few shot image generation for GMAW defects detection using multi-sensor monitoring system

Abstract

Abstract Multi-source information intelligent sensing and online evaluation of the weld are two challenging problems in complex welding scenes. The difficulty is particularly pronounced in different welding scenes, where the multi-source sensing data exists large system deviations and small welding defects sample data are at large. In this paper, we propose modified few shot image generation model Fusing-and-Filling GAN (F2GAN) for welding data augmentation. To verify the efficiency of the modified F2GAN, some experiments were performed with various welding conditions. Through our proposed welding data set augmentation approach, two welding quality classification models combining multiple information are designed. We show the final classification accuracy of normal, burn through, incomplete penetration and welding deviation is 96.60%. Our results are beneficial for welding monitoring and quality evaluation in complex welding scenes.