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Our group has moved to the University of Michigan! Please visit our new group website for updates.

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

  • Shichen, Sixian, and Alice Launching Their Internships at Meta, Wayfair, and TSMC This SummerJune 17, 2024

    Shichen will begin an internship position as a Software Engineer in Machine Learning at Meta in Summer 2024. She will utilize advanced machine learning methods to conduct feature engineering and improve the content click-through rate for Facebook apps. Sixian will start a summer internship as a Machine Learning Scientist (PhD) at Wayfair in Boston. She […]

  • Manan will start working as a Senior Data Scientist at C3 AIJune 15, 2024

    After graduating with his Ph.D. in Mechanical Engineering (with a graduate minor in Statistics) from our lab, Manan will start his career in industry as a Senior Data Scientist at C3.ai in Redwood City, CA. Founded by veteran Silicon Valley entrepreneur Tom Siebel, C3 AI is a leading provider of Enterprise AI solutions to a […]

  • ADML celebrates Yuquan and Manan’s Ph.D. graduation!June 15, 2024

    Two students from our lab at Illinois – Yuquan Meng and Manan Mehta – graduated with their Ph.D. degrees this May! Both students were hooded by Prof. Shao in the university-wide Ph.D. hooding ceremony held on 10th May 2024 at the State Farm Center at the University of Illinois Urbana-Champaign. Our best wishes to both […]

  • PAPER ON HYBRID PHYSICS-GUIDED DATA-DRIVEN MODELING IN TWO-PHOTON LITHOGRAPHY PUBLISHED IN JOURNAL OF MANUFACTURING PROCESSFebruary 17, 2024

    Our paper titled “Hybrid physics-guided data-driven modeling for generalizable geometric accuracy prediction and improvement in two-photon lithography” has been published on Journal of Manufacturing Processes. The paper was co-authored by Sixian Jia, Jieliyue Sun, Andrew Howes, Prof. Michhelle R. Dawson, Prof. Kimani C. Toussaint Jr. , and Prof. Chenhui Shao. Two-photon lithography (TPL) is an additive […]

  • Paper on Explainable Few-shot Learning for Ultrasonic Metal Welding Published in Journal of Manufacturing ProcessesOctober 30, 2023

    Recently, our paper titled “Explainable few-shot learning for online anomaly detection in ultrasonic metal welding with varying configurations” has been published on Journal of Manufacturing Processes. The paper was co-authored by Yuquan Meng, Kuan-Chieh Lu, Zhiqiao Dong, Shichen Li, and Prof. Chenhui Shao. Modern manufacturing is featured by rapid reconfiguration and agile adaptation that necessitate varying process configurations. In ultrasonic metal welding, a process […]

  • Papers published at MSEC 2023 and NAMRC 51 conferencesOctober 12, 2023

    Our PhD students Kuan-Chieh, Zhiqiao, and Manan published papers and present their research at MSEC 2023 & NAMRC 51 at Rutgers University this summer. Kuan-Chieh’s paper titled “Online Cost-Effective Classification of Mixed Tool and Material Conditions in Ultrasonic Metal Welding: Towards Integrated Monitoring and Control” has been published at MSEC 2023. Ultrasonic metal welding (UMW) […]