Home

Welcome to the Automation and Digital Manufacturing (ADM) Lab! The ADM Lab is directed by Dr. Chenhui Shao. To learn more about our research, please visit the research and publications pages.

Recent News

  • Project on Waste Heat Recovery Funded by DOEAugust 21, 2018

    Our project “Roll-to-Roll Manufactured Hybrid Metal-Polymer Heat Exchangers with Anti-Fouling and Self-Monitoring for Waste Heat Recovery”  is funded by the U.S. Department of Energy (DOE).  We will collaborate with Prof. Sinha, Prof. Miljkovic, Prof. Ferreira and Prof. Salapaka to develop an innovative waste heat recovery method that aims to resolving the long-standing challenges of low-grade heat recovery through four instinctive […]

  • Prof. Shao Named Among SME 30 Under 30July 30, 2018

    Prof. Chenhui Shao was named to SME’s 2018 class of 30 Under 30 honorees, a program that recognizes individuals who exemplify extraordinary promise in manufacturing and the STEM skills that support the discipline. The program is designed to recognize and encourage young people who can make a difference in manufacturing, whether on the shop floor, in an engineering or R&D […]

  • Yuhang’s Work on Spatial Modeling Published in AMSE JMSEJuly 30, 2018

    Read the paper here.

  • Project on Deep Learning and High-Speed Train Funded by CRRCOctober 13, 2017

    Our project “Research on Key Technology of Rail Transit-Based Wireless Sensor Intelligence Data” is funded by China Railway Rolling Stock Corporation (CRRC). We will work with Professor Niao He‘s lab to develop deep learning and sensor fusion methodologies that will enhance the intelligence and automation of rolling stock, especially high-speed trains. The project (budget $300k) is […]

  • Project on Big Data in Manufacturing Funded by National Center for Supercomputing Applications (NCSA)October 12, 2017

    The funded project “Big Data Enabled Multi-Level Decision-Making for Smart Manufacturing” aims to establish theoretical foundations and a new paradigm for multi-level decision-making by leveraging big data and high-performance computing (HPC). We will design big data based solutions for manufacturers, including NCSA’s industrial partners, and equip them with toolkits for smart sensing, monitoring, control, diagnosis, prognostics, […]

  • Project in Wheel-Track Wear Supported by CRRCFebruary 1, 2017

    China Railway Rolling Stock Corporation (CRRC) has funded for our project “Wheel-Track Wear Indicators, Calculation and Testing Methods for Heavy-Haul Freight Cars.” This one-year project ($132k) will tackle fundamental and technical challenges in evaluating and predicting wheel-track wear for heavy-haul freight cars.

Archives