Use of a structure-borne sound-based in-process sensor system to identify Weld seam irregularities during electron beam welding

In this publication, an in-process quality assurance method for electron beam welding based on a structure-borne sound emission test for the detection of weld irregularities arising in the process is presented. For this purpose, different sheet materials, i.e., AISI 304, AZ31 and AlMg3, were welded in a butt-joint and the resulting process noises were recorded by means of two acoustic emission sensors specifically designed for structure-borne sound. During the welding experiments, typical irregularities, e.g. incidence points, pore lines and cracks, were deliberately induced. Subsequently, the recorded acoustic signals were examined with regard to defect-specific abnormalities. Various methods in the time and frequency domain as well as pre-trained machine learning models were used to analyze the acoustic emission data. The results show that the investigated irregularities can be identified and distinguished from other process emissions, eventually enabling a robust means of identification for weld seam irregularities and, thus, opening pathways towards cost-effective in-process quality control.

Publication Details

Journal
Universitätsbibliothek Kassel
Published
2024-09-27
DOI
https://doi.org/10.17170/kobra-2025020710899
Primary Topic
Welding Techniques and Residual Stresses
Type
article
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article

Use of a structure-borne sound-based in-process sensor system to identify Weld seam irregularities during electron beam welding

Universitätsbibliothek Kassel
Welding Techniques and Residual Stresses
article

Use of a structure-borne sound-based in-process sensor system to identify Weld seam irregularities during electron beam welding

article en

Abstract

In this publication, an in-process quality assurance method for electron beam welding based on a structure-borne sound emission test for the detection of weld irregularities arising in the process is presented. For this purpose, different sheet materials, i.e., AISI 304, AZ31 and AlMg3, were welded in a butt-joint and the resulting process noises were recorded by means of two acoustic emission sensors specifically designed for structure-borne sound. During the welding experiments, typical irregularities, e.g. incidence points, pore lines and cracks, were deliberately induced. Subsequently, the recorded acoustic signals were examined with regard to defect-specific abnormalities. Various methods in the time and frequency domain as well as pre-trained machine learning models were used to analyze the acoustic emission data. The results show that the investigated irregularities can be identified and distinguished from other process emissions, eventually enabling a robust means of identification for weld seam irregularities and, thus, opening pathways towards cost-effective in-process quality control.

Universitätsbibliothek Kassel
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Welding Techniques and Residual Stresses
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