Next Article in Journal
MLC-LSTM: Exploiting the Spatiotemporal Correlation between Multi-Level Weather Radar Echoes for Echo Sequence Extrapolation
Next Article in Special Issue
Indoor Air-Quality Data-Monitoring System: Long-Term Monitoring Benefits
Previous Article in Journal
Continuous Finger Gesture Recognition Based on Flex Sensors
Previous Article in Special Issue
Accuracy Analysis in Sensor Networks for Asynchronous Positioning Methods
Article

Deep Learning for Industrial Computer Vision Quality Control in the Printing Industry 4.0

1
Hochschule Heilbronn, Fakultät Management und Vertrieb, Campus Schwäbisch Hall, 74523 Schwäbisch Hall, Germany
2
Department of Artificial Intelligence, Escuela Técnica Superior de Ingenieros Informáticos, Universidad Politécnica de Madrid, 28660 Madrid, Spain
3
Matthews International GmbH, Gutenbergstraße 1-3, 48691 Vreden, Germany
4
Departament of Business Intelligence, Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid, 28006 Madrid, Spain
5
InspectOnline, Wiley-VCH Verlag GmbH & Co. KGaA, 69469 Weinheim, Germany
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2019, 19(18), 3987; https://doi.org/10.3390/s19183987
Received: 9 July 2019 / Revised: 9 August 2019 / Accepted: 13 September 2019 / Published: 15 September 2019
Rapid and accurate industrial inspection to ensure the highest quality standards at a competitive price is one of the biggest challenges in the manufacturing industry. This paper shows an application of how a Deep Learning soft sensor application can be combined with a high-resolution optical quality control camera to increase the accuracy and reduce the cost of an industrial visual inspection process in the Printing Industry 4.0. During the process of producing gravure cylinders, mistakes like holes in the printing cylinder are inevitable. In order to improve the defect detection performance and reduce quality inspection costs by process automation, this paper proposes a deep neural network (DNN) soft sensor that compares the scanned surface to the used engraving file and performs an automatic quality control process by learning features through exposure to training data. The DNN sensor developed achieved a fully automated classification accuracy rate of 98.4%. Further research aims to use these results to three ends. Firstly, to predict the amount of errors a cylinder has, to further support the human operation by showing the error probability to the operator, and finally to decide autonomously about product quality without human involvement. View Full-Text
Keywords: soft sensors; industrial optical quality inspection; deep learning; artificial vision soft sensors; industrial optical quality inspection; deep learning; artificial vision
Show Figures

Figure 1

  • Externally hosted supplementary file 1
    Doi: DOI 10.17605/OSF.IO/Z85HX
MDPI and ACS Style

Villalba-Diez, J.; Schmidt, D.; Gevers, R.; Ordieres-Meré, J.; Buchwitz, M.; Wellbrock, W. Deep Learning for Industrial Computer Vision Quality Control in the Printing Industry 4.0. Sensors 2019, 19, 3987. https://doi.org/10.3390/s19183987

AMA Style

Villalba-Diez J, Schmidt D, Gevers R, Ordieres-Meré J, Buchwitz M, Wellbrock W. Deep Learning for Industrial Computer Vision Quality Control in the Printing Industry 4.0. Sensors. 2019; 19(18):3987. https://doi.org/10.3390/s19183987

Chicago/Turabian Style

Villalba-Diez, Javier, Daniel Schmidt, Roman Gevers, Joaquín Ordieres-Meré, Martin Buchwitz, and Wanja Wellbrock. 2019. "Deep Learning for Industrial Computer Vision Quality Control in the Printing Industry 4.0" Sensors 19, no. 18: 3987. https://doi.org/10.3390/s19183987

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop