Camera-Based In-Process Quality Measurement of Hairpin Welding

The technology of hairpin welding, which is frequently used in the automotive industry, entails high-quality requirements in the welding process. It can be difficult to trace the defect back to the affected weld if a non-functioning stator is detected during the final inspection. Often, a visual ass...

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Autores principales:	Julia Hartung, Andreas Jahn, Oliver Bocksrocker, Michael Heizmann
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	hairpin laser welding semantic segmentation dilated convolution sdu-net spatter detection Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/3a094952ba69477c9f35247c5dc92700
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id	oai:doaj.org-article:3a094952ba69477c9f35247c5dc92700
record_format	dspace
spelling	oai:doaj.org-article:3a094952ba69477c9f35247c5dc927002021-11-11T15:23:52ZCamera-Based In-Process Quality Measurement of Hairpin Welding10.3390/app1121103752076-3417https://doaj.org/article/3a094952ba69477c9f35247c5dc927002021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10375https://doaj.org/toc/2076-3417The technology of hairpin welding, which is frequently used in the automotive industry, entails high-quality requirements in the welding process. It can be difficult to trace the defect back to the affected weld if a non-functioning stator is detected during the final inspection. Often, a visual assessment of a cooled weld seam does not provide any information about its strength. However, based on the behavior during welding, especially about spattering, conclusions can be made about the quality of the weld. In addition, spatter on the component can have serious consequences. In this paper, we present in-process monitoring of laser-based hairpin welding. Using an in-process image analyzed by a neural network, we present a spatter detection method that allows conclusions to be drawn about the quality of the weld. In this way, faults caused by spattering can be detected at an early stage and the affected components sorted out. The implementation is based on a small data set and under consideration of a fast process time on hardware with limited computing power. With a network architecture that uses dilated convolutions, we obtain a large receptive field and can therefore consider feature interrelation in the image. As a result, we obtain a pixel-wise classifier, which allows us to infer the spatter areas directly on the production lines.Julia HartungAndreas JahnOliver BocksrockerMichael HeizmannMDPI AGarticlehairpinlaser weldingsemantic segmentationdilated convolutionsdu-netspatter detectionTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10375, p 10375 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	hairpin laser welding semantic segmentation dilated convolution sdu-net spatter detection Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	hairpin laser welding semantic segmentation dilated convolution sdu-net spatter detection Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Julia Hartung Andreas Jahn Oliver Bocksrocker Michael Heizmann Camera-Based In-Process Quality Measurement of Hairpin Welding
description	The technology of hairpin welding, which is frequently used in the automotive industry, entails high-quality requirements in the welding process. It can be difficult to trace the defect back to the affected weld if a non-functioning stator is detected during the final inspection. Often, a visual assessment of a cooled weld seam does not provide any information about its strength. However, based on the behavior during welding, especially about spattering, conclusions can be made about the quality of the weld. In addition, spatter on the component can have serious consequences. In this paper, we present in-process monitoring of laser-based hairpin welding. Using an in-process image analyzed by a neural network, we present a spatter detection method that allows conclusions to be drawn about the quality of the weld. In this way, faults caused by spattering can be detected at an early stage and the affected components sorted out. The implementation is based on a small data set and under consideration of a fast process time on hardware with limited computing power. With a network architecture that uses dilated convolutions, we obtain a large receptive field and can therefore consider feature interrelation in the image. As a result, we obtain a pixel-wise classifier, which allows us to infer the spatter areas directly on the production lines.
format	article
author	Julia Hartung Andreas Jahn Oliver Bocksrocker Michael Heizmann
author_facet	Julia Hartung Andreas Jahn Oliver Bocksrocker Michael Heizmann
author_sort	Julia Hartung
title	Camera-Based In-Process Quality Measurement of Hairpin Welding
title_short	Camera-Based In-Process Quality Measurement of Hairpin Welding
title_full	Camera-Based In-Process Quality Measurement of Hairpin Welding
title_fullStr	Camera-Based In-Process Quality Measurement of Hairpin Welding
title_full_unstemmed	Camera-Based In-Process Quality Measurement of Hairpin Welding
title_sort	camera-based in-process quality measurement of hairpin welding
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3a094952ba69477c9f35247c5dc92700
work_keys_str_mv	AT juliahartung camerabasedinprocessqualitymeasurementofhairpinwelding AT andreasjahn camerabasedinprocessqualitymeasurementofhairpinwelding AT oliverbocksrocker camerabasedinprocessqualitymeasurementofhairpinwelding AT michaelheizmann camerabasedinprocessqualitymeasurementofhairpinwelding
_version_	1718435360038453248

Camera-Based In-Process Quality Measurement of Hairpin Welding

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