A Novel Approach to the Holistic 3D Characterization of Weld Seams—Paving the Way for Deep Learning-Based Process Monitoring

In an industrial environment, the quality assurance of weld seams requires extensive efforts. The most commonly used methods for that are expensive and time-consuming destructive tests, since quality assurance procedures are difficult to integrate into production processes. Beyond that, available te...

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Autores principales:	Maximilian Schmoeller, Christian Stadter, Michael Karl Kick, Christian Geiger, Michael Friedrich Zaeh
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	non-destructive testing weld seam contour microfocus computed tomography laser beam welding Deep Learning Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
Acceso en línea:	https://doaj.org/article/26ec7cc25d454188a952830c0e6577b0
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id	oai:doaj.org-article:26ec7cc25d454188a952830c0e6577b0
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spelling	oai:doaj.org-article:26ec7cc25d454188a952830c0e6577b02021-11-25T18:14:48ZA Novel Approach to the Holistic 3D Characterization of Weld Seams—Paving the Way for Deep Learning-Based Process Monitoring10.3390/ma142269281996-1944https://doaj.org/article/26ec7cc25d454188a952830c0e6577b02021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1944/14/22/6928https://doaj.org/toc/1996-1944In an industrial environment, the quality assurance of weld seams requires extensive efforts. The most commonly used methods for that are expensive and time-consuming destructive tests, since quality assurance procedures are difficult to integrate into production processes. Beyond that, available test methods allow only the assessment of a very limited set of characteristics. They are either suitable for determining selected geometric features or for locating and evaluating internal seam defects. The presented work describes an evaluation methodology based on microfocus X-ray computed tomography scans (µCT scans) which enable the 3D characterization of weld seams, including internal defects such as cracks and pores. A 3D representation of the weld contour, i.e., the complete geometry of the joint area in the component with all quality-relevant geometric criteria, is an unprecedented novelty. Both the dimensions of the weld seam and internal defects can be revealed, quantified with a resolution down to a few micrometers and precisely assigned to the welded component. On the basis of the methodology developed within the framework of this study, the results of the scans performed on the alloy AA 2219 can be transferred to other aluminum alloys. In this way, the data evaluation framework can be used to obtain extensive reference data for the calibration and validation of inline process monitoring systems employing Deep Learning-based data processing in the scope of subsequent work.Maximilian SchmoellerChristian StadterMichael Karl KickChristian GeigerMichael Friedrich ZaehMDPI AGarticlenon-destructive testingweld seam contourmicrofocus computed tomographylaser beam weldingDeep LearningTechnologyTElectrical engineering. Electronics. Nuclear engineeringTK1-9971Engineering (General). Civil engineering (General)TA1-2040MicroscopyQH201-278.5Descriptive and experimental mechanicsQC120-168.85ENMaterials, Vol 14, Iss 6928, p 6928 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	non-destructive testing weld seam contour microfocus computed tomography laser beam welding Deep Learning Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85
spellingShingle	non-destructive testing weld seam contour microfocus computed tomography laser beam welding Deep Learning Technology T Electrical engineering. Electronics. Nuclear engineering TK1-9971 Engineering (General). Civil engineering (General) TA1-2040 Microscopy QH201-278.5 Descriptive and experimental mechanics QC120-168.85 Maximilian Schmoeller Christian Stadter Michael Karl Kick Christian Geiger Michael Friedrich Zaeh A Novel Approach to the Holistic 3D Characterization of Weld Seams—Paving the Way for Deep Learning-Based Process Monitoring
description	In an industrial environment, the quality assurance of weld seams requires extensive efforts. The most commonly used methods for that are expensive and time-consuming destructive tests, since quality assurance procedures are difficult to integrate into production processes. Beyond that, available test methods allow only the assessment of a very limited set of characteristics. They are either suitable for determining selected geometric features or for locating and evaluating internal seam defects. The presented work describes an evaluation methodology based on microfocus X-ray computed tomography scans (µCT scans) which enable the 3D characterization of weld seams, including internal defects such as cracks and pores. A 3D representation of the weld contour, i.e., the complete geometry of the joint area in the component with all quality-relevant geometric criteria, is an unprecedented novelty. Both the dimensions of the weld seam and internal defects can be revealed, quantified with a resolution down to a few micrometers and precisely assigned to the welded component. On the basis of the methodology developed within the framework of this study, the results of the scans performed on the alloy AA 2219 can be transferred to other aluminum alloys. In this way, the data evaluation framework can be used to obtain extensive reference data for the calibration and validation of inline process monitoring systems employing Deep Learning-based data processing in the scope of subsequent work.
format	article
author	Maximilian Schmoeller Christian Stadter Michael Karl Kick Christian Geiger Michael Friedrich Zaeh
author_facet	Maximilian Schmoeller Christian Stadter Michael Karl Kick Christian Geiger Michael Friedrich Zaeh
author_sort	Maximilian Schmoeller
title	A Novel Approach to the Holistic 3D Characterization of Weld Seams—Paving the Way for Deep Learning-Based Process Monitoring
title_short	A Novel Approach to the Holistic 3D Characterization of Weld Seams—Paving the Way for Deep Learning-Based Process Monitoring
title_full	A Novel Approach to the Holistic 3D Characterization of Weld Seams—Paving the Way for Deep Learning-Based Process Monitoring
title_fullStr	A Novel Approach to the Holistic 3D Characterization of Weld Seams—Paving the Way for Deep Learning-Based Process Monitoring
title_full_unstemmed	A Novel Approach to the Holistic 3D Characterization of Weld Seams—Paving the Way for Deep Learning-Based Process Monitoring
title_sort	novel approach to the holistic 3d characterization of weld seams—paving the way for deep learning-based process monitoring
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/26ec7cc25d454188a952830c0e6577b0
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A Novel Approach to the Holistic 3D Characterization of Weld Seams—Paving the Way for Deep Learning-Based Process Monitoring

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