Wolf Christian, Sommer Niklas, Böhm Stefan
Department for Cutting and Joining Manufacturing Processes - Institute for Production Technologies and Logistics, University of Kassel, Kurt-Wolters-Straße 3, 34125, Kassel, Germany.
Sci Rep. 2024 Sep 27;14(1):22120. doi: 10.1038/s41598-024-73797-8.
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.
在本出版物中，提出了一种基于结构声发射测试的电子束焊接过程质量保证方法，用于检测焊接过程中出现的焊缝不规则情况。为此，将不同的板材，即AISI 304、AZ31和AlMg3，进行对接焊接，并通过两个专门设计用于结构声的声发射传感器记录产生的过程噪声。在焊接实验过程中，故意引入典型的不规则情况，如入射点、气孔线和裂纹。随后，针对特定缺陷的异常情况对记录的声信号进行检查。使用了时域和频域中的各种方法以及预训练的机器学习模型来分析声发射数据。结果表明，可以识别所研究的不规则情况并将其与其他过程排放区分开来，最终实现一种可靠的焊缝不规则情况识别方法，从而为具有成本效益的过程质量控制开辟道路。
