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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: 2021-08-17Bibliographically approved
In thesis
1. Novel beam shaping and computer vision methods for laser beam welding
Open this publication in new window or tab >>Novel beam shaping and computer vision methods for laser beam welding
2021 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Laser beam welding has been widely applied in different industrial sectors due to its unique advantages. However, there are still challenges, such as beam positioning in T-joint welding, and gap bridging in butt joint welding,especially in the case of varying gap width along a joint. It is expected that enabling more advanced control to a welding system, and obtaining more in-depth process knowledge could help to solve these issues. The aim of this work is to address such welding issues by a laser beam shaping technology using a novel deformable mirror together with computer vision methods and also to increase knowledge about the benefits and limitations with this approach.

Beam shaping in this work was realized by a novel deformable mirror system integrated into an industrial processing optics. Together with a wave front sensor, a controlled adaptive beam shaping system was formed with a response time of 10 ms. The processes were monitored by a coaxial camera with selected filters and passive or active illumination. Conduction mode autogenous bead-on-plate welding and butt joint welding experiments have been used to understand the effect of beam shaping on the melt pool geometry. Circular Gaussian, and elliptical Gaussian shapes elongated transverse to and along the welding direction were studied. In-process melt pool images and cross section micrographs of the weld seams/beads were analyzed. The results showed that the melt pool geometry can be significantly modified by beam shaping using the deformable mirror. T-joint welding with different beam offset deviations relative to the center of the joint line was conducted to study the potential of using machine learning to track the process state. The results showed that machine learning can reach sufficient detection and estimation performance, which could also be used for on-line control. In addition, in-process and multidimensional data were accurately acquired using computer vision methods. These data reveal weaknesses of current thermo-fluid simulation model, which in turn can help to better understand and control laser beam welding. The obtained results in this work shows a huge potential in using the proposed methods to solve relevant challenges in laser beam welding.

Abstract [sv]

Lasersvetsning används i stor utsträckning i olika industrisektorer på grund av dess unika fördelar. Det finns emellertid fortfarande utmaningar, såsom rätt positionering av laserstrålen vid genomträngningssvetsning av T-fogar och hantering av varierande spaltbredd längs fogen vid svetsning av stumfogar. Sådana problem förväntas kunna lösas med avancerade metoder för automatisering, metoder som också förväntas ge fördjupade kunskaper om processen. Syftet med detta arbete är att ta itu med dessa problem med hjälp av en teknik för lasereffektens fördelning på arbetsstycket, s.k. beam shaping. Det sker med hjälp av en ny typ av i realtid deformerbar spegel tillsammans med bildbehandling av kamerabilder från processen. För- och nackdelar med detta tillvägagångssätt undersöks.Beam shaping åstadkoms med hjälp av ny typ av deformerbart spegelsystem som integreras i en industriell processoptik. Tillsammans med en vågfrontsensor bildas ett adaptivt system för beam shaping med en svarstid på 10 ms. Processen övervakas av en kamera linjerad koaxialt med laserstrålen. För att kunna ta bilder av svetspunkten belyses den med ljus av lämplig våglängd, och kameran är försedd med ett motsvarande optiskt filter. Försök har utförts med svetsning utan tillsatsmaterial, direkt på plåtar, svetsning utan s.k. nyckelhål, för att förstå effekten av beam shaping på svetssmältans geometri. Gauss fördelade cirkulära och elliptiska former, långsträckta både tvärs och längs svetsriktningen har studerats. Bilder från svetssmältan har analyserats och även mikrostrukturen i tvärsnitt från de svetsade plåtarna. Resultaten visar att svetssmältans geometri kan modifieras signifikant genom beam shaping med hjälp av det deformerbara spegelsystemet. Genomträngningssvetsning av T-fogar med avvikelser relativt foglinjens centrum genomfördes för att studera potentialen i att använda maskininlärning för att fånga processens tillstånd. Resultaten visade att maskininlärning kan nå tillräcklig prestanda för detektering och skattning av denna avvikelse. Något som också kan användas för återkopplad styrning. Flerdimensionell processdata har samlats i realtid och analyserats med hjälp av bildbehandlingsmetoder.  Dessa data avslöjar brister i nuvarande simuleringsmodeller,vilket i sin tur hjälper till med att bättre förstå och styra lasersvetsning.Resultaten från detta arbete uppvisar en god potential i att använda de föreslagna metoderna för att lösa relevanta utmaningar inom lasersvetsning.

Place, publisher, year, edition, pages
Trollhättan: University West, 2021. p. 96
Series
Licentiate Thesis: University West ; 33
Keywords
Laser beam welding; Beam shaping, Deformable mirror, Process monitoring, Process control, Model validation, Computer vision, Machine learning, Butt joint, T-joint., Lasersvetsning, Beam shaping, Deformerbar spegel, Processövervakning, Processkontroll, Modellvalidering, Datorsyn, Maskininlärning, Butt joint, T-fogar
National Category
Manufacturing, Surface and Joining Technology
Identifiers
urn:nbn:se:hv:diva-16970 (URN)978-91-88847-96-6 (ISBN)978-91-88847-95-9 (ISBN)
Presentation
2021-08-18, Zoom, University West, Trollhättan, 10:00 (English)
Supervisors
Note

Till licentiatuppsats hör 2 inskickade artiklar, som visas inte nu.

Available from: 2021-08-18 Created: 2021-08-17 Last updated: 2021-11-29

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Mi, YongcuiSikström, FredrikNilsen, MorganAncona, Antonio

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