An Online Surface Defects Detection System for AWAM Based on Deep Learning

Defects detecting layer by layer in arc welding based additive manufacturing (AWAM) is a big challenge as it affects the successive layer quality of the products. Most of the work on layer quality defection were focused on 3D profile measurement and X-ray spectroscopy method but it is inefficient, expensive with poor adaptability. In this work, an online intelligent surface defects detection system for AWAM was developed through deep learning algorithm and support vector machine method. To achieve a reliable surface feature of the welding beads, a vision sensor was used to get the image of the shaped surface synchronously. The system was trained offline and online to acquire knowledge of the welding beads which were classified into five patterns as normal, pore, hump, depression and undercut. An defection test result showed 95.29% accuracy. The system was verified to be practical with high accuracy and efficiency for the surface defects.Mechanical Engineerin