Intelligent system for diagnosing the welded joints quality on the basis of the radiographic method

The paper discusses the issue of creating an intelligent diagnostic system for welded joints based on the radiographic method. This will speed up the process of decoding radiographic images and reduce the number of errors associated with human factors, since at this time most of the work on decoding images is done manually. The goal of the work is to develop an intelligent system for finding defects in a welded joint in a radiographic image using neural networks. The obtained results are the algorithm of operation of the intelligent diagnostic system for welded joints based on the radiographic method, a trained neural network for detecting defects of welded joints.This work was supported by the Russian Foundation for Basic Research, research No 17-08-01569

Image segmentation of micro-TIG battery welds

Inspection of cell-to-tab welds in module assembly for battery pack is one the most critical processes in the production of battery packs for transportation electrification. A procedure suitable for the segmentation of weld images on module assembly lines is proposed to separate them into weld and tab regions. The procedure is centred around identifying the edge of the weld and the convex hull region that includes it. The edge is detected with a fuzzy logic rule-based inference system. The procedure is demonstrated with a set of 71 images that are labelled to establish a comparison reference, the so called ground truth. Particle swarm optimisation is used to find values of the parameter procedure that result in a a local minimum of the mean percent error (MPE). An MPE of 4.25 per cent has been obtained