The project is titled, “Collaborative Research: A Digital Manufacturing Platform to Democratize Biological Tissue Access Using Smart Two-Photon Polymerization.” The funding comes from the NSF’s Advanced Manufacturing (AM) program within the Division of Civil, Mechanical and Manufacturing Innovation (CMMI). Working with our collaborators at Brown University, we will create a digital manufacturing platform for cloud-based […]
The Artificial Intelligence Institute for Next Generation Food Systems, or AIFS, was launched October 1, 2020 to solve the world’s biggest challenges to crop and food production facing our planet. For more information about AIFS, please see this news article and the AIFS website. In the first year of the Institute, we will work on […]
The group receives funding from the Advanced Manufacturing Office of the U.S. Department of Energy (DOE). The project, entitled “Novel Energy-Efficient Drying Technologies for Food, Pulp and Paper, and other Energy Intensive Manufacturing Industries,” aims to develop innovative dryer platforms for the food and forest products industry to reduce energy consumption and to improve product […]
A paper entitled “multi-task learning for data-efficient spatiotemporal modeling of tool surface progression in ultrasonic welding” is recently published in the Journal of Manufacturing Systems. The paper was co-authored by Haotian Chen, Yuhang Yang, and Chenhui Shao. This paper developed a multi-task learning method to enable data-efficient spatiotemporal modeling. The method was evaluated using tool […]
A new project entitled “Smart Drying Enabled by Multi-Source Data Fusion and Machine Learning” is funded by NSF through The Center for Advanced Research in Drying (CARD). CARD is an NSF-Sponsored Industry/University Cooperative Research Center (I/UCRC) devoted to research in drying of moist, porous materials such as food and other agricultural products, forestry products, chemical […]
The Center on Adaptive, Resilient Cyber-Physical Manufacturing Networks (AR-CyMaN) is recently established by the University of Illinois at Urbana-Champaign’s Grainger College of Engineering and Zhejiang University. AR-CyMaN aims to define the science and technology for creating smart and highly flexible manufacturing networks. We will develop machine learning solutions that can enhance the flexibility of manufacturing. […]
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 […]
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, […]
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.
UPDATE: The positions have been filled. Thank you for your attention. Our group currently has multiple openings for Ph.D. students. Full scholarships (RA or TA) will be provided. Students with a solid background in the areas (some but not necessarily all) of (1) manufacturing (or industrial engineering), (2) control, (3) statistics, and (4) machine learning are […]