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10 May 2024 Improving in-line quality management in EV cell manufacturing with ultrasound inspection and machine learning
Ruimin Qiao
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Proceedings Volume PC12952, NDE 4.0, Predictive Maintenance, Communication, and Energy Systems: The Digital Transformation of NDE II; PC1295204 (2024) https://doi.org/10.1117/12.3012617
Event: SPIE Smart Structures + Nondestructive Evaluation, 2024, Long Beach, California, United States
Abstract
In this talk, we highlight the need for advanced inspection methods in EV cell manufacturing at gigafactory scales that deliver comprehensive information for 100% of cells produced to assess the true cell quality distribution and prevent quality escapes. We share how cell and pack manufacturers can achieve and maintain higher quality faster and at lower overall cost by utilizing Liminal’s in-line high-throughput primary inspection to screen 100% of cells produced and high-resolution secondary inspection to diagnose anomalous cells. We discuss the use of ultrasound inspection and physics-assisted machine learning methods to assess cell quality, better than state-of-the-art electrochemical methods.
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Ruimin Qiao "Improving in-line quality management in EV cell manufacturing with ultrasound inspection and machine learning", Proc. SPIE PC12952, NDE 4.0, Predictive Maintenance, Communication, and Energy Systems: The Digital Transformation of NDE II, PC1295204 (10 May 2024); https://doi.org/10.1117/12.3012617
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KEYWORDS
Manufacturing
Inspection
Machine learning
Quality management
Ultrasonography
Laser induced breakdown spectroscopy
Raster graphics
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