Journal of Sensors and Sensor Systems
Journal of Sensors and Sensor Systems
JSSS
Articles | Volume 9, issue 1
Articles | Volume 9, issue 1
https://doi.org/10.5194/jsss-9-167-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/jsss-9-167-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 9, issue 1
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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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.
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