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Improved Automatic Quality Inspections through the Integration of State-of-the-Art Machine Vision and Collaborative Robots
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-3973-3394
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0002-3705-5553
2019 (English)In: Advances in Manufacturing Technology XXXIII: Proceedings of the 17th International Conference on Manufacturing Research, incorporating the 34th National Conference on Manufacturing Research, September 10–12, 2019, Queen’s University Belfast, UK / [ed] Yan Jin; Mark Price, Amsterdam: IOS Press, 2019, Vol. 9, p. 107-112Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we discuss the concepts of a flexible and high-performing solution for automatic quality control that integrates state-of-the-art machine learning algorithms with collaborative robots. The overall aim of the paper is to take the first steps towards improved automatic quality inspections in the manufacturing industry, leading to reduced quality defects and reduced costs in the manufacturing process. For developing and evaluating a first version of a solution that integrates state-of-the-art machine vision and collaborative robots we use a real-world case study focusing on improved quality inspection. Results from the case study shows that it is possible to realize automatic quality inspections through the use of a collaborative robot as intended, but also that there are some challenges that need to be further addressed in order to achieve a top-performing system.

Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2019. Vol. 9, p. 107-112
Series
Advances in Transdisciplinary Engineering, ISSN 2352-751X, E-ISSN 2352-7528 ; 9
Keywords [en]
Industrial Quality Control, Machine Vision, Collaborative Robot
National Category
Production Engineering, Human Work Science and Ergonomics
Research subject
Production and Automation Engineering; VF-KDO
Identifiers
URN: urn:nbn:se:his:diva-18222DOI: 10.3233/ATDE190020ISBN: 978-1-64368-008-8 (print)ISBN: 978-1-64368-009-5 (electronic)OAI: oai:DiVA.org:his-18222DiVA, id: diva2:1396148
Conference
17th International Conference on Manufacturing Research, incorporating the 34th National Conference on Manufacturing Research, 10–12 September 2019, Queen’s University, Belfast, UK
Part of project
Virtual factories with knowledge-driven optimization (VF-KDO), Knowledge FoundationAvailable from: 2020-02-25 Created: 2020-02-25 Last updated: 2024-06-19Bibliographically approved

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Syberfeldt, AnnaEkblom, Tom

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