Weld quality characterization by vibration analysis for ultrasonic metal welding processes

•Monitoring of ultrasonic metal welding process using industrial measuring equipment.•Sensor fusion and automated data pre-processing.•Random forest based shear strength prediction.•Robust model suitable for different material parameters. Ultrasonic metal welding is an efficient, solid-state welding...

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Veröffentlicht in:Journal of advanced joining processes Jg. 8; S. 100149
Hauptverfasser: Müller, Florian W., Mirz, Christian, Weil, Sascha, Schiebahn, Alexander, Corves, Burkhard, Reisgen, Uwe
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.11.2023
Elsevier
Schlagworte:
Automated data processing
Machine learning
Process monitoring
Random forest
Ultrasonic metal welding
Vibration analysis
Vibration measurements
Process monitoring
Ultrasonic metal welding
Automated data processing
Machine learning
Random forest
Vibration measurements
Vibration analysis
ISSN:2666-3309, 2666-3309
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Zusammenfassung:•Monitoring of ultrasonic metal welding process using industrial measuring equipment.•Sensor fusion and automated data pre-processing.•Random forest based shear strength prediction.•Robust model suitable for different material parameters. Ultrasonic metal welding is an efficient, solid-state welding process that is well established in the production of electrical equipment. However, isolated and untraceable failures occasionally occur in industrial production processes. According to the state of the art, these failures are due to a multitude of influencing variables, such as material and surface condition of the joining partners, as well as tool and fixture wear. This study presents quality prediction models based on measurements of mechanical vibrations of the welding machine using laser triangulation sensors, sonotrode penetration depth measurements, and machine internal welding power signals. The acquired data are processed and transformed into a random forest model to estimate the shear strength of the welds. The robustness of the prediction model has been successfully validated by welding experiments with significant external disturbances, such as surface roughness, contamination and material hardness. Within the framework of this study, the development of a stable and industrially applicable concept for process monitoring is demonstrated with a regression model that achieves a mean relative estimation error of 4.30% and a R2 value of 0.964. Furthermore, a classification model that determines the external disturbances for the individual welds was successfully validated, achieving a micro F1 value of 94% and a macro F1 value of 95%.
ISSN:2666-3309
2666-3309
DOI:10.1016/j.jajp.2023.100149