Application of electrical power measurements for process monitoring in ultrasonic metal welding

• Published in: Welding in the world Vol. 67; no. 2; pp. 395 - 415
• Main Authors: Müller, Florian W., Chen, Chun-Yu, Schiebahn, Alexander, Reisgen, Uwe
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2023; Springer Nature B.V
• Subjects: Chemistry and Materials Science; Cleanliness; Destructive testing; Feature extraction; Hardness; Laser beam welding; Machine learning; Materials Science; Metallic Materials; Monitoring; Position measurement; Power measurement; Research Paper; Solid Mechanics; Theoretical and Applied Mechanics; Triangulation; Wiring harnesses; Gaussian process regression; Quality prediction; Ultrasonic metal welding
• ISSN: 0043-2288; 1878-6669
• DOI: 10.1007/s40194-022-01428-9

For the production of e-mobility components such as cable harnesses, battery cells, power electronics, etc., ultrasonic metal welding is well-established process of choice. These electrical applications require high quality for every single connection; single points of failure and no possibility of repair after installation or commissioning are state of the art. At present, the prevailing binding mechanisms and their sensitivity to the numerous process influencing variables like base material hardness, surface, and cleanliness are still the subject of research. In order to ensure sufficient quality despite the lack of process understanding, random destructive testing is carried out during ongoing production. The welding systems’ internal monitoring methods are currently not sufficient to make a prediction of the joint quality achieved. To determine process phases and extract features regarding the joint formation, the observation of process vibrations at the horn, anvil, and the components using laser-doppler-vibrometry, laser triangulation sensors or other suitable external measurement technology is common. These methods require external accessibility to the measurement position, not given in the industrial production environment. In this study, measurements of the high-frequency power signal of the welding system are conducted, and several machine learning models for quality prediction are set up. To ensure the robustness, several disturbances, e.g., changing material hardness and cleanliness, are taken into account. Thus, it will be evaluated to what extent an industrially suitable quality monitoring can be implemented by means of electrical measuring technology and how much more accurate such an external measuring system is compared to the possibilities already available in the welding system.