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AI-Based Quality Control of Wood Surfaces with Autonomous Material Handling
University West, Department of Engineering Science, Division of Subtractive and Additive Manufacturing. (PTW)ORCID iD: 0000-0002-4329-418X
University West, Department of Engineering Science, Division of Production Systems. (PTW)
(PTW)
2021 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 11, no 21, p. 9965-9965Article in journal (Refereed) Published
Abstract [en]

The theory and applications of Smart Factories and Industry 4.0 are increasing the entry into the industry. It is common in industry to start converting exclusive parts, of their production, into this new paradigm rather than converting whole production lines all at once. In Europe and Sweden, recent political decisions are taken to reach the target of greenhouse gas emission reduction. One possible solution is to replace concrete in buildings with Cross Laminated Timber. In the last years, equipment and software that have been custom made for a certain task, are now cheaper and can be adapted to fit more processes than earlier possible. This in combination, with lessons learned from the automotive industry, makes it possible to take the necessary steps and start redesigning and building tomorrows automated and flexible production systems in the wood industry. This paper presents a proof of concept of an automated inspection system, for wood surfaces, where concepts found in Industry 4.0, such as industrial Internet of things (IIoT), smart factory, flexible automation, artificial intelligence (AI), and cyber physical systems, are utilized. The inspection system encompasses, among other things, of the shelf software and hardware, open source software, and standardized, modular, and mobile process modules. The design of the system is conducted with future expansion in mind, where new parts and functions can be added as well as removed.

Place, publisher, year, edition, pages
MDPI, 2021. Vol. 11, no 21, p. 9965-9965
Keywords [en]
Industry 4.0, IIoT, smart factory, flexible automation, AI, AMR, OPCUA, CLT, wood industry, automated inspection
National Category
Production Engineering, Human Work Science and Ergonomics Computer Sciences
Research subject
Production Technology
Identifiers
URN: urn:nbn:se:hv:diva-17941DOI: 10.3390/app11219965ISI: 000718349200001Scopus ID: 2-s2.0-85118270439OAI: oai:DiVA.org:hv-17941DiVA, id: diva2:1621156
Available from: 2021-12-17 Created: 2021-12-17 Last updated: 2022-04-04

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Ericsson, MikaelJohansson, Dahniel

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