1512217 (Sutherland). The overarching objective of this research is to generate knowledge needed to establish a new framework for the design and operation of digital manufacturing equipment for increased resource/energy efficiency and enhanced environmental sustainability. Research will be conducted in the following four thrusts: 1) Machine-level energy flow characterization, analysis, and optimization; 2) Energy conscious production planning and control on a digitalized shop floor; 3). Retrofitting and remanufacturing of Computer Numerical Controlled (CNC) machines; and 4) Sustainable design of CNC machines. The research will be jointly pursued by a highly qualified team consisting of researchers from both China and the United States. Professor Sutherland from Purdue University, a pioneer in the area of sustainable manufacturing, will lead the U.S. part of the team. Professor Shao from Huazhong University of Science and Technology, a renowned expert in digital manufacturing, will lead the Chinese part of the team in partnership with Harbin Institute of Technology and Hefei University of Technology. The team members expertise complements perfectly with each other to pursue seamless integration of sustainable manufacturing and digital manufacturing. Working together it is expected that the knowledge and technologies generated in this project will be disseminated to industry in a very timely manner. The data/information enabled sustainable manufacturing approach to be developed will enhance productivity, efficiency, and sustainability, a key for both the China and U.S. machine tool industries and their respective manufacturing sectors.
This research represents one of the first efforts to take advantage of the emerging digital manufacturing paradigm with the goal of advancing environmental sustainability. The project will enhance understanding of resource and energy flows over the entire life cycle of digital manufacturing equipment, from design, manufacture, operation, maintenance, to remanufacturing, which in turn offers insight into the design and operation of this equipment for improved environmental performance. In particular, characterizing and modeling energy flows at both the machine and shop floor levels make it possible to develop dynamic scheduling procedures for better energy and power efficiency when facing uncertainties and within smart grid scenarios. Data mining-enabled automated LCA could lead to a completely new and highly efficient approach for conducting LCA on complicated products. This project will significantly improve the resource and energy efficiency of digital manufacturing equipment over its entire life cycle. This, in turn, will lead to significant cost saving and carbon emission reduction. In addition, the research findings will be infused into multiple courses within the industrial, mechanical, and environmental and ecological engineering curricula at Purdue, as well as corresponding schools/departments in partnering Chinese universities. These activities will increase the amount of sustainability-related educational materials in these curricula. During this project, the PIs will continue their efforts to recruit students from under-represented groups into engineering. Students working on this project will be encouraged to share their experiences via social media to generate interests in conducting research among their peers. By communicating with their counterparts in the other country, students are also expected to gain exposure to a different culture. To reach an even broader audience, all the findings, case studies, and teaching materials will be made available on manufacturingHUB.org.
This grant is co-funded by the Global Venture Fund (GVF) of NSF's International Science and Engineering section (ISE), the CMMI Manufacturing Machines and Equipment program, and the CBET/ENG Environmental Sustainability program.
