Welcome to the Automation and Digital Manufacturing Lab! We are an interdisciplinary research group that conduct research and innovation activities to enhance the automation and intelligence of manufacturing. In order to achieve this mission, we develop and apply methodologies from a wide range of disciplines, e.g., machine learning, statistics, and automatic control. To learn more about our research, please visit the research and publications pages.

Recent News

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    Yuquan has defended his doctoral dissertation successfullyApril 28, 2023

    Yuquan Meng has successfully defended his doctoral dissertation titled “Physics-Informed Machine Learning for Smart Decision-Making in Ultrasonic Metal Welding.” The defense was conducted before an esteemed Examination Committee consisting of Prof. Chenhui Shao, Prof. Placid Ferreira, Prof. Srinivasa Salapaka, and Prof. Pingfeng Wang. In his research, Yuquan Meng has made significant contributions to the field […]

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    Paper on End-to-end Online Quality Prediction for Ultrasonic Metal Welding published in JMPApril 28, 2023

    Our paper titled “End-to-end online quality prediction for ultrasonic metal welding using sensor fusion and deep learning” has been published on Journal of Manufacturing Processes. The paper was co-authored by Yulun Wu, Yuquan Meng and Prof. Chenhui Shao. In industrial-scale production applications of ultrasonic metal welding (UMW), there is a strong need for predicting joint […]

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    Kuan-Chieh will start his internship at Intel this summerApril 26, 2023

    Kuan-Chieh will start an internship position at Intel in summer 2023. He will work with the Logic Technology Development Group to analyze epitaxially grown films and develop an AI algorithm to predict the quality based on process conditions. His Ph.D. research focuses on data efficiency in manufacturing quality monitoring and real-time parameter adjustment for disturbance […]

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    Manan will start his internship at Seagate this SummerApril 26, 2023

    Manan has accepted an internship offer from Seagate Technology for Summer 2023 where he will work as an AI/Machine Learning Intern. He will work in Seagate’s highly dynamic Global Wafer Systems (GWS) Team and collaborate with a global team of Data Scientists and Machine Learning Engineers to shape the future of new data products in […]

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    Paper on Clustered Federated Learning published in IEEE Transactions on Industrial InformaticsMarch 30, 2023

    Our paper titled “A greedy agglomerative framework for clustered federated learning” has been published in the IEEE Transactions on Industrial Informatics. The paper was co-authored by Manan Mehta and Prof. Chenhui Shao. Federated learning (FL) has received widespread attention for supporting the training of deep learning models across multiple IoT devices while preserving data privacy. […]

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    Paper on Federated Learning-based Defect Detection for Additive Manufacturing published in JMSJuly 1, 2022

    Our paper titled “Federated learning-based semantic segmentation for pixel-wise defect detection in additive manufacturing” has been published in the Journal of Manufacturing Systems. The paper was co-authored by Manan Mehta and Prof. Chenhui Shao. Federated learning (FL) is an emerging machine learning (ML) paradigm which allows several participants (manufacturers) to collaboratively train a model while […]