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 […]
Funding
ADML Receives Funding from AIFS
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 […]
ADML Receives Funding from DOE
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 […]
ADML Receives Funding from National Science Foundation (NSF)
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 […]
ADML Receives Funding from Center for Adaptive, Resilient Cyber-Physical Manufacturing Networks
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. […]
Prof. Shao Won an NSF CAREER Award
Prof. Chenhui Shao has won an NSF Faculty Early Career Development (CAREER) award for his project “Dynamic Process-Attribute-Data-Performance Modeling to Enable Smart Ultrasonic Metal Welding.” The CAREER Program offers NSF’s most prestigious awards in support of early-career faculty who have the potential to serve as academic role models in research and education and to lead advances […]
Kick-off of a new DOE project
The project “A Multi-Scale Computational Platform for Predictive Modeling of Corrosion in Al-steel Joints,” funded by the Vehicle Technologies Office of U.S. Department of Energy (DOE), started recently. We are working in collaboration with researchers from the University of Michigan, the University of Georgia, Pennsylvania State University, General Motors, Optimal Process Technologies, and Livermore Software Technology […]
Project on Quantitative Non-Destructive Evaluation Funded by the REMADE Institute
The project “Quantitative Non-Destructive Evaluation of Fatigue Damage Based on Multi-Sensor Fusion,” funded by the Reducing EMbodied-Energy And Decreasing Emissions (REMADE) Institute, is starting soon. We will work with Professor Katie Matlack from UIUC and Prof. Professor Jingjing Li from Penn State to develop an innovative non-destructive evaluation (NDE) methodology for the quantification and prognostics […]
Project on Waste Heat Recovery Funded by DOE
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 […]
Project on Deep Learning and High-Speed Train Funded by CRRC
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 […]