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Intelligent fault detection of electrical assemblies using hierarchical convolutional networks for supporting automatic optical inspection systems

ORCID
https://orcid.org/0000-0002-8538-3789
Affiliation
Institut für Produktionsmesstechnik, Technische Universität Braunschweig
Schwebig, Alida Ilse Maria;
GND
172488176
Affiliation
Institut für Produktionsmesstechnik, Technische Universität Braunschweig
Tutsch, Rainer

Electrical assemblies are the core of many electronic devices and therefore represent a crucial part of the overall product, which must be carefully checked before integration into its functional environment. For this reason, automatic optical inspection systems are required in electronic manufacturing to detect visible errors in products at an early stage. In particular, the automotive electronics production area is one of the sectors in which quality assurance has uppermost priority, as undetected defects can pose a danger to life. However, most optical inspection processes still have error slippage rates, which are responsible for delivering faulty electrical assemblies to customers. Therefore, this article shows how an application strategy of deep learning, based on neural networks, can be combined with an automatic optical inspection system to further increase the recognition accuracy of the process.

The additional use of artificial intelligence supported classification systems provides a way to find out the exact details about the manufacturing-related errors of electrical assemblies. However, due to the high number of different error categories, a single classification algorithm is usually not sufficient to provide reliable visual inspection results with high robustness against error slip. For this reason, a hierarchical model with multiple classifiers is proposed in this article. The principle is based on the hierarchical description of the quality status and fault types using several combined neural networks. In this context, the original classification task is distributed over different subnetworks. These subnetworks, which interact as an overall model, only verify certain error and quality features of the electrical assemblies, which means that higher recognition accuracy and robustness can be achieved compared to a single network.

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