{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T21:40:15Z","timestamp":1776289215937,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2019,9,15]],"date-time":"2019-09-15T00:00:00Z","timestamp":1568505600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"EU RFCS program","award":["793505"],"award-info":[{"award-number":["793505"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s19183987","type":"journal-article","created":{"date-parts":[[2019,9,16]],"date-time":"2019-09-16T03:17:57Z","timestamp":1568603877000},"page":"3987","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":211,"title":["Deep Learning for Industrial Computer Vision Quality Control in the Printing Industry 4.0"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2423-1474","authenticated-orcid":false,"given":"Javier","family":"Villalba-Diez","sequence":"first","affiliation":[{"name":"Hochschule Heilbronn, Fakult\u00e4t Management und Vertrieb, Campus Schw\u00e4bisch Hall, 74523 Schw\u00e4bisch Hall, Germany"},{"name":"Department of Artificial Intelligence, Escuela T\u00e9cnica Superior de Ingenieros Inform\u00e1ticos, Universidad Polit\u00e9cnica de Madrid, 28660 Madrid, Spain"}]},{"given":"Daniel","family":"Schmidt","sequence":"additional","affiliation":[{"name":"Matthews International GmbH, Gutenbergstra\u00dfe 1-3, 48691 Vreden, Germany"},{"name":"Departament of Business Intelligence, Escuela T\u00e9cnica Superior de Ingenieros Industriales, Universidad Polit\u00e9cnica de Madrid, 28006 Madrid, Spain"}]},{"given":"Roman","family":"Gevers","sequence":"additional","affiliation":[{"name":"Matthews International GmbH, Gutenbergstra\u00dfe 1-3, 48691 Vreden, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9677-6764","authenticated-orcid":false,"given":"Joaqu\u00edn","family":"Ordieres-Mer\u00e9","sequence":"additional","affiliation":[{"name":"Departament of Business Intelligence, Escuela T\u00e9cnica Superior de Ingenieros Industriales, Universidad Polit\u00e9cnica de Madrid, 28006 Madrid, Spain"}]},{"given":"Martin","family":"Buchwitz","sequence":"additional","affiliation":[{"name":"InspectOnline, Wiley-VCH Verlag GmbH &amp; Co. KGaA, 69469 Weinheim, Germany"}]},{"given":"Wanja","family":"Wellbrock","sequence":"additional","affiliation":[{"name":"Hochschule Heilbronn, Fakult\u00e4t Management und Vertrieb, Campus Schw\u00e4bisch Hall, 74523 Schw\u00e4bisch Hall, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ustundag, A., and Cevikcan, E. (2018). Industry 4.0: Managing The Digital Transformation, Springer.","DOI":"10.1007\/978-3-319-57870-5"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.compchemeng.2012.06.037","article-title":"Smart manufacturing, manufacturing intelligence and demand-dynamic performance","volume":"47","author":"Davis","year":"2012","journal-title":"Comput. Chem. 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