Articles | Volume 9, issue 1
J. Sens. Sens. Syst., 9, 167–178, 2020
https://doi.org/10.5194/jsss-9-167-2020
J. Sens. Sens. Syst., 9, 167–178, 2020
https://doi.org/10.5194/jsss-9-167-2020
Regular research article
01 Jul 2020
Regular research article | 01 Jul 2020

Compilation of training datasets for use of convolutional neural networks supporting automatic inspection processes in industry 4.0 based electronic manufacturing

Alida Ilse Maria Schwebig and Rainer Tutsch

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Latest update: 28 Sep 2022
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Short summary
This article presents a classification concept based on deep learning as an additional optical test method for real-time visualization and analysis of electrical assemblies in the production environment. For this purpose, a neural convolutional network is used to identify the quality of the solder joint of surface-mounted chip components in the inspection images. The concept can be used to increase the detection performance of the solder joint inspection systems.