Articles | Volume 9, issue 2
https://doi.org/10.5194/jsss-9-363-2020
https://doi.org/10.5194/jsss-9-363-2020
Regular research article
 | 
02 Nov 2020
Regular research article |  | 02 Nov 2020

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

Alida Ilse Maria Schwebig and Rainer Tutsch

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Cited articles

Berger, M.: Test- und Prüfverfahren in der Elektronikfertigung: Vom Arbeitsprinzip bis Design-for-Test-Regeln, VDE-Verlag, Berlin, 250 pp., 2012. 
Combet, C. and Chang, M.-M.: 01005 Assembly, the AOI route to optimizing yield, Vi TECHNOLOGY, available at: https://smtnet.com/library/files/upload/01005Assembly.pdf (last access: May 2020), 2009. 
Goodfellow, I., Bengio, Y., and Courville, A.: Deep learning, MIT Press, Cambridge, Massachusetts, London, UK, 1785 pp., 2016. 
Hope, T., Resheff, Y. S., and Lieder, I.: Einführung in TensorFlow: Deep-Learning-Systeme programmieren, trainieren, skalieren und deployen, Safari Tech Books Online, O'Reilly, Heidelberg, 224 pp., 2018. 
Mao, X., Hijazi, S., Casas, R., Kaul, P., Kumar, R., and Rowen, C.: Hierarchical CNN for traffic sign recognition, in: IEEE Intelligent Vehicles Symposium (IV), Gothenburg, 130–135, https://doi.org/10.1109/IVS.2016.7535376, 2016. 
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Short summary
In order to further increase the performance of neural networks in the field of optical quality assurance of soldered joints, a hierarchical classifier can be used instead of a single network. The global expansion of the classifier enables the inspection task to be distributed over several subnetworks, which results in higher accuracy. Since the individual sub-models only concentrate on the identification of certain characteristics, categorical problems can be solved more effectively.