In body and vehicle construction, a large number of workpieces are connected by means of solder joints. Solder joints are defective if they contain breaks or pores, for example. The suitability of soldered joints can largely be assessed optically. Can images of the soldered joints be assessed sufficiently well and sufficiently quickly using artificial intelligence? It should be noted that defective solder joints are clearly underrepresented in the training images compared to high-quality solder joints.
The images of the solder joints are suitably cut and centered. The imbalanced data set is enriched by means of suitable data augmentation. A neural network is trained on the training data set, which classifies solder joints into different categories based on the images (“good”, “contains breaks”, “contains pores”, “contains chip residues”, …). The artificial intelligence generated in this way is directly integrated into the production process in the form of an embedded system together with a camera solution. The overall system is constructed in such a way that expert feedback regarding classification and categories can be included. In this way, a gradually improving, self-learning system is created.
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