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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

  • 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) […]

  • 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 […]