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Vision based beam offset detection in laser stake welding of T-joints using a neural network
University West, Department of Engineering Science, Division of Production Systems. (PTW)ORCID iD: 0000-0002-8018-6145
University West, Department of Engineering Science, Division of Production Systems. (PTW)ORCID iD: 0000-0001-5734-294X
University West, Department of Engineering Science, Division of Production Systems. (PTW)ORCID iD: 0000-0002-8771-7404
University West, Department of Engineering Science, Division of Production Systems. CNR-IFN Institute for Photonics and Nanotechnologies, Physics Department, via Amendola 173, Bari, 70126, Italy. (PTW)ORCID iD: 0000-0002-6247-5429
2019 (English)In: Procedia Manufacturing, E-ISSN 2351-9789, Vol. 36, p. 42-49Article in journal (Refereed) Published
Abstract [en]

This paper presents an experimental study where a vision camera integrates coaxially into a laser beam welding tool to monitor beam deviations (beam offset) in laser stake welding of T-joints. The aim is to obtain an early detection of deviations from the joint centreline in this type of welding where the joint is not visible from the top side. A polynomial surface fitting method is applied to extract features that can describe the behaviour of the melt pool. A nonlinear autoregressive with exogenous inputs neural network model is trained to relate eight image features to the laser beam offset. The performance of the presented model is evaluated offline by different welding samples. The results show that the proposed method can be used to guide post weld inspection and has the potential for on-line adaptive control. © 2019 The Author(s). Published by Elsevier B.V.

Place, publisher, year, edition, pages
2019. Vol. 36, p. 42-49
Keywords [en]
laser beam welding, T-joint, process monitoring, vision camera, neural network
National Category
Manufacturing, Surface and Joining Technology
Research subject
ENGINEERING, Manufacturing and materials engineering; Production Technology
Identifiers
URN: urn:nbn:se:hv:diva-14473DOI: 10.1016/j.promfg.2019.08.007Scopus ID: 2-s2.0-85072518360OAI: oai:DiVA.org:hv-14473DiVA, id: diva2:1357059
Conference
Conference of 17th Nordic Laser Materials Processing Conference, NOLAMP 2019 ; Conference Date: 27 August 2019 Through 29 August 2019
Funder
Knowledge FoundationAvailable from: 2019-10-02 Created: 2019-10-02 Last updated: 2019-11-12Bibliographically approved

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Mi, YongcuiSikström, FredrikNilsen, MorganAncona, Antonio

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