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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

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    Paper on ML-Enabled Geometric Compliance Improvement for Two-Photon Lithography Published in JMPMarch 9, 2022

    Our paper titled “Machine-learning-enabled geometric compliance improvement in two-photon lithography without hardware modifications” has been published on Journal of Manufacturing Processes. The paper was co-authored by Yuhang Yang and Prof. Chenhui Shao. In recent years, two-photon lithography (TPL) has emerged as a practical and promising micro- and nano-fabrication technique for a wide range of applications. […]

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    Paper on Hierarchical Data Models for Additive Manufacturing Published in AMFebruary 24, 2022

    Our paper titled “Hierarchical data models improve the accuracy of feature level predictions for additively manufactured parts” has been published on Additive Manufacturing. The paper was co-authored by Yuhang Yang and Prof. Chenhui Shao. Industrial-scale production applications of additive manufacturing (AM) are growing rapidly, and scalable AM production requires quality systems that monitor and control […]

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    Shichen will start her internship at Meta this summerNovember 30, 2021

    Shichen has accepted an internship offer from Meta in 2022 Summer. She will work as a Machine learning engineer intern. She will conduct engineering projects and cutting-edge researches in the fields of Machine Learning (ML), Natural Language Processing (NLP). Her PhD research focusing on machine learning in manufacturing processes and her recent involvement in a […]

  • Image of the main quad with students sitting in grass
    Siyuan Will Start his Internship at 3MNovember 12, 2021

    Siyuan has recently accepted an internship position at 3M as a Data Science intern this summer. He will work with the AI & ML lab at 3M’s Corporate Research Systems Laboratory to design new material structure and geometry using Reinforcement Learning (RL) and Generative Adversarial Networks (GANs). His PhD research focusing on machine learning in […]

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    Paper on Multi-Object Tracking in Videos published on IEEE AccessNovember 11, 2021

    Our paper titled “Efficient Online Tracking-by-Detection With Kalman Filter” has been published on IEEE Access. The paper was co-authored by Siyuan Chen and Prof. Chenhui Shao. Visual Multi-Object Tracking (MOT) has a promisingly broad application in manufacturing, construction, traffic, logistics, etc., especially in large-scale applications where it is not feasible to attach markers to many […]

  • Paper on adaptive sampling for multi-task Gaussian processes published in JMSSeptember 27, 2021

    Our paper titled “Adaptive sampling design for multi-task learning of Gaussian processes in manufacturing” has been published in the Journal of Manufacturing Systems. The paper was co-authored by Manan Mehta and Prof. Chenhui Shao. Multi-task learning (MTL) is a machine learning technique used to enhance learning performance in similar-but-not-identical tasks. However, the accuracy of MTL […]