New input factors for machine learning approaches to predict the weld quality of ultrasonically welded thermoplastic composite materials

  • Thermoplastic composites (TCs) enjoy high popularity in the field of engineering. Due to this popularity, there is a growing need to assemble this material with the help of fast and efficient joining processes. One joining process, which has seen increased use, is the process of ultrasonic welding. To make reliable statements about the quality of the joined material, some kind of quality assurance has to be made. In terms of ultrasonic spot welding, there are already some documented approaches for observing or predicting the joining quality, but some of these most promising parameters for quality assurance are difficult to measure in the process of continuous ultrasonic welding. This is why new parameters are investigated for their potential to improve the prediction of ultrasonic-welded TCs’ quality. Thermography and sound emission data have been found to have a correlation with the produced weld quality and are fed into different machine learning algorithms. Despite the relativelyThermoplastic composites (TCs) enjoy high popularity in the field of engineering. Due to this popularity, there is a growing need to assemble this material with the help of fast and efficient joining processes. One joining process, which has seen increased use, is the process of ultrasonic welding. To make reliable statements about the quality of the joined material, some kind of quality assurance has to be made. In terms of ultrasonic spot welding, there are already some documented approaches for observing or predicting the joining quality, but some of these most promising parameters for quality assurance are difficult to measure in the process of continuous ultrasonic welding. This is why new parameters are investigated for their potential to improve the prediction of ultrasonic-welded TCs’ quality. Thermography and sound emission data have been found to have a correlation with the produced weld quality and are fed into different machine learning algorithms. Despite the relatively small dataset, trained algorithms reach binary classification rates of over 90%, indicating that the newly discovered parameters show the potential to improve the quality assurance of ultrasonic-welded TCs in the future. This improvement may enable the establishment of the ultrasonic welding of TCs in manufacturing.show moreshow less

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Metadaten
Author:Dominik Görick, Alfons Schuster, Lars Larsen, Jonas Welsch, Tobias Karrasch, Michael KupkeGND
URN:urn:nbn:de:bvb:384-opus4-1084660
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/108466
ISSN:2504-4494OPAC
Parent Title (English):Journal of Manufacturing and Materials Processing
Publisher:MDPI AG
Type:Article
Language:English
Date of first Publication:2023/08/23
Publishing Institution:Universität Augsburg
Release Date:2023/10/17
Tag:Industrial and Manufacturing Engineering; Mechanical Engineering; Mechanics of Materials
Volume:7
Issue:5
First Page:154
DOI:https://doi.org/10.3390/jmmp7050154
Institutes:Mathematisch-Naturwissenschaftlich-Technische Fakultät
Mathematisch-Naturwissenschaftlich-Technische Fakultät / Institut für Materials Resource Management
Mathematisch-Naturwissenschaftlich-Technische Fakultät / Institut für Materials Resource Management / Lehrstuhl für Faserverbundkunststofftechnologie
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 50 Naturwissenschaften / 500 Naturwissenschaften und Mathematik
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)