Implementation and potentials of a machine vision system in a series production using deep learning and low-cost hardware

Würschinger H, Mühlbauer M, Winter M, Engelbrecht M, Hanenkamp N (2020)

Publication Type: Journal article

Publication year: 2020

Journal

Procedia CIRP Curran

Book Volume: 90

Pages Range: 611-616

DOI: 10.1016/j.procir.2020.01.121

Abstract

For manufacturing processes there is a need to ensure an efficient production and to fulfill the increasing quality requirements. To handle these challenges, Machine Vision Systems can be used for process monitoring and quality control. In this paper the implementation and thereby the potentials of such a system in a series production using Transfer Learning with low cost hardware is introduced. The necessary steps, from the hardware implementation, the data acquisition, the preprocessing over the optimization and the application are depicted. Finally we show that the proposed solution can fulfill defined requirements and can compete with a professional Machine Vision System.

Authors with CRIS profile

Hubert Würschinger Lehrstuhl für Ressourcen- und Energieeffiziente Produktionsmaschinen Matthias Mühlbauer Lehrstuhl für Ressourcen- und Energieeffiziente Produktionsmaschinen Nico Hanenkamp Lehrstuhl für Ressourcen- und Energieeffiziente Produktionsmaschinen

Involved external institutions

ZF Friedrichshafen AG DE Germany (DE)

How to cite

APA:

Würschinger, H., Mühlbauer, M., Winter, M., Engelbrecht, M., & Hanenkamp, N. (2020). Implementation and potentials of a machine vision system in a series production using deep learning and low-cost hardware. Procedia CIRP, 90, 611-616. https://dx.doi.org/10.1016/j.procir.2020.01.121

MLA:

Würschinger, Hubert, et al. "Implementation and potentials of a machine vision system in a series production using deep learning and low-cost hardware." Procedia CIRP 90 (2020): 611-616.

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