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This study focuses on common batteries used in electric vehicles, which are composed of cells grouped into modules and stacked to form a battery pack. Ultrasonic metal welding (UMW) is employed to bond these cells, and ensuring a reliable weld quality by inspection is crucial for maintaining the performance and stability of batteries. Currently, the quality of UMW in battery cells is assessed through sample unit-based T-peel tests and visual inspection, methods that suffer from reduced productivity and increased costs due to their destructive nature. This research introduces an algorithm designed to predict weld quality by analyzing welding process signals of the UMW process, specifically the bonding between 8-&#x03BC;m-thick Cu foil and 0.2-mm-thick nickel-plated copper strip materials used in battery cell manufacturing. To achieve quality prediction, current and voltage signals from the welder, as well as the displacement signal of the welded part are used. A UMW system was constructed, incorporating a current sensor, a voltage sensor, and a linear variable displacement transducer (LVDT) for measuring these signals. The study further derives welding energy from the current and voltage signals, analyzes changes in the behavior of the sonotrode during welding using the LVDT sensor, and employs data analysis to derive feature variables. These variables are then used in a machine learning-based classification model. Ultimately, the study develops and evaluates a support vector machine (SVM)-based algorithm for real-time UMW quality determination. The algorithm achieves a high classification accuracy of 98%, showcasing its effectiveness in predicting the quality of ultrasonic metal welding in the battery manufacturing process.

Investigation on Quality Prediction Algorithm in Ultrasonic Metal Welding for Multilayered Cu for Battery Cells