Abstract
Ultrasonic metal welding, an emerging solid state bonding method, has drawn extensive attention and been applied in various manufacturing scenarios in recent years. This paper reviews the quality monitoring system of ultrasonic metal welding from in-situ testing (online monitoring), non-in-situ testing and prediction of ultrasonic weld quality through machine learning methods. In-situ testing focuses on the acquisition of different process parameters and their relationship to weld quality, while non-in-situ testing focus on the monitoring indicators of the weld quality and the testing means. The exploration of machine learning methods concentrates on the influence of model selection and parameter input on prediction accuracy. Based on the analysis, the future development trend of ultrasonic weld quality monitoring is provided.
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