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Beam Offset Detection in Laser Stake Welding of Tee Joints Using Machine Learning and Spectrometer Measurements
University West, Department of Engineering Science, Division of Production Systems. (PTW)
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
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2022 (English)In: Sensors, E-ISSN 1424-8220, Vol. 22, no 10Article in journal (Refereed) Published
Abstract [en]

Laser beam welding offers high productivity and relatively low heat input and is one key enabler for efficient manufacturing of sandwich constructions. However, the process is sensitive to how the laser beam is positioned with regards to the joint, and even a small deviation of the laser beam from the correct joint position (beam offset) can cause severe defects in the produced part. With tee joints, the joint is not visible from top side, therefore traditional seam tracking methods are not applicable since they rely on visual information of the joint. Hence, there is a need for a monitoring system that can give early detection of beam offsets and stop the process to avoid defects and reduce scrap. In this paper, a monitoring system using a spectrometer is suggested and the aim is to find correlations between the spectral emissions from the process and beam offsets. The spectrometer produces high dimensional data and it is not obvious how this is related to the beam offsets. A machine learning approach is therefore suggested to find these correlations. A multi-layer perceptron neural network (MLPNN), support vector machine (SVM), learning vector quantization (LVQ), logistic regression (LR), decision tree (DT) and random forest (RF) were evaluated as classifiers. Feature selection by using random forest and non-dominated sorting genetic algorithm II (NSGAII) was applied before feeding the data to the classifiers and the obtained results of the classifiers are compared subsequently. After testing different offsets, an accuracy of 94% was achieved for real-time detection of the laser beam deviations greater than 0.9 mm from the joint center-line.

Place, publisher, year, edition, pages
MDPI, 2022. Vol. 22, no 10
Keywords [en]
laser beam offset; feature selection; laser beam welding; machine learning; spectrometer; tee joint
National Category
Bioinformatics and Systems Biology
Research subject
Production Technology
Identifiers
URN: urn:nbn:se:hv:diva-18671DOI: 10.3390/s22103881ISI: 000803647200001PubMedID: 35632290Scopus ID: 2-s2.0-85130378549OAI: oai:DiVA.org:hv-18671DiVA, id: diva2:1678023
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Knowledge Foundation, 20170315Available from: 2022-06-28 Created: 2022-06-28 Last updated: 2024-04-12

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Jadidi, AydinMi, YongcuiSikström, FredrikNilsen, MorganAncona, Antonio

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