WE Colloquium: Weihong Guo, Sensor Fusion and Process Monitoring for Ultrasonic Welding of Lithium-ion Batteries: an enabler for in situ NDE

Abstract

The wide applications of automatic sensing devices and computer systems have resulted in a temporally and spatially dense data-rich environment, which provides unprecedented opportunities for quality improvement in advanced manufacturing. The increasing complexity of data structures raises significant research challenges on data analytics. New methodologies for effective data fusion and information integration to support decision-making are in demand.

Ultrasonic metal welding is used for joining lithium-ion batteries of electric vehicles. The quality of the joints is essential to the performance of the entire battery pack. Hence, the ultrasonic welding process that creates the joints must be equipped with online sensing and real-time process monitoring systems. This would help ensure the process to be operated under the normal condition and quickly address quality-related issues. This talk focuses on the methods in process monitoring and fault diagnosis using online sensing signals for ultrasonic metal welding.

The first part of this talk will present a wavelet-based profile monitoring method that is capable of making decisions within a welding cycle and guiding real-time process adjustments. The second part of the talk will focus on sensor fusion and fault diagnosis using online sensor signals. The proposed method extracts features from the multi-sensor heterogeneous profile data; the features are then fed into classifiers to detect faulty operations and identify fault types. Lastly, this talk will discuss the future of data fusion for welding and other advanced manufacturing processes.

Bio

Weihong (Grace) Guo is an Assistant Professor in the Department of Industrial and Systems Engineering. She earned her B.S. degree in Industrial Engineering from Tsinghua University, China, in 2010 and her Ph.D. in Industrial & Operations Engineering from the University of Michigan, Ann Arbor, in 2015. Dr. Guo’s research interests are in the areas of statistical quality control and process monitoring, data mining for advanced manufacturing, and quality-oriented design and modeling of complex manufacturing systems. Her current research focuses on data fusion methods in the interface between applied statistics and system control/optimization. Dr. Guo received the Best Paper Award Second Place for paper titled “Profile Monitoring and Fault Diagnosis via Sensor Fusion for Ultrasonic Welding” at the ASME 2016 Manufacturing Science and Engineering Conference. She also received the Best Paper Award at the 2014 International Conference on Frontiers of Design and Manufacturing Sciences, the Best Student Paper Award Finalist at the 2014 ISERC Quality Control & Reliability Engineering, the Rackham Predoctoral Fellowship from the University of Michigan, and the Wilson Prize for the Best Student Paper in Manufacturing. She is a member of ASME, IISE, INFORMS, and Tau Beta Pi.