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

Related authors

Intelligent fault detection of electrical assemblies using hierarchical convolutional networks for supporting automatic optical inspection systems
Alida Ilse Maria Schwebig and Rainer Tutsch
J. Sens. Sens. Syst., 9, 363–374, https://doi.org/10.5194/jsss-9-363-2020,https://doi.org/10.5194/jsss-9-363-2020, 2020
Short summary

Related subject area

Measurement systems: Sensor signal processing and electronics
Smart in-cylinder pressure sensor for closed-loop combustion control
Dennis Vollberg, Peter Gibson, Günter Schultes, Hans-Werner Groh, and Thomas Heinze
J. Sens. Sens. Syst., 11, 1–13, https://doi.org/10.5194/jsss-11-1-2022,https://doi.org/10.5194/jsss-11-1-2022, 2022
Short summary
Efficient transient testing procedure using a novel experience replay particle swarm optimizer for THD-based robust design and optimization of self-X sensory electronics in industry 4.0
Qummar Zaman, Senan Alraho, and Andreas König
J. Sens. Sens. Syst., 10, 193–206, https://doi.org/10.5194/jsss-10-193-2021,https://doi.org/10.5194/jsss-10-193-2021, 2021
Short summary
Intelligent fault detection of electrical assemblies using hierarchical convolutional networks for supporting automatic optical inspection systems
Alida Ilse Maria Schwebig and Rainer Tutsch
J. Sens. Sens. Syst., 9, 363–374, https://doi.org/10.5194/jsss-9-363-2020,https://doi.org/10.5194/jsss-9-363-2020, 2020
Short summary
Measurement uncertainty analysis of field-programmable gate-array-based, real-time signal processing for ultrasound flow imaging
Richard Nauber, Lars Büttner, and Jürgen Czarske
J. Sens. Sens. Syst., 9, 227–238, https://doi.org/10.5194/jsss-9-227-2020,https://doi.org/10.5194/jsss-9-227-2020, 2020
Short summary
Employing correlation for wireless components and device characterization in reverberation chambers
Christoph Cammin, Dmytro Krush, Ralf Heynicke, and Gerd Scholl
J. Sens. Sens. Syst., 8, 185–194, https://doi.org/10.5194/jsss-8-185-2019,https://doi.org/10.5194/jsss-8-185-2019, 2019
Short summary

Cited articles

Berger, M.: Test- und Prüfverfahren in der Elektronikfertigung: Vom Arbeitsprinzip bis Design-for-Test-Regeln, VDE-Verlag, Berlin, 250 pp., 2012. 
Frid-Adar, M., Diamant, I., Klang, E., Amitai, M., Goldberger, J., and Greenspan, H.: GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification, Neurocomputing, 321, 321–331, https://doi.org/10.1016/j.neucom.2018.09.013, 2018. 
He, K., Zhang, X., Ren, S., and Sun, J.: Deep Residual Learning for Image Recognition, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, https://doi.org/10.1109/CVPR.2016.90, 2015. 
Hu, J., Shen, L., Albanie, S., Sun, G., and Wu, E.: Squeeze-and-Excitation Networks, available at: http://arxiv.org/pdf/1709.01507v4, last access: 5 September 2017. 
Huang, G., Liu, S., van der Maaten, L., and Weinberger, K. Q.: CondenseNet: An Efficient DenseNet using Learned Group Convolutions, available at: http://arxiv.org/pdf/1711.09224v2 (last access: 7 June 2018), 2018a. 
Download
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.