Collaborative Research: Environmental Sustainability of Additive Manufacturing Processes: Bridging Geometry and Life Cycle Inventory

点击翻译按钮获取中文摘要

1605472 / 1604825Zhao, Fu / Li, Lin The past decade has seen a rapid proliferation of academic achievements and industrial applications of additive manufacturing (AM) processes. Today the interest in AM continues to grow. Although reducing environmental impact is considered among the main advantages, very limited efforts have been made to rigorously evaluate the environmental performance of AM processes using the well-accepted life cycle assessment (LCA) methodology; also, even less studies are oriented toward process improvement. The overarching goal of this research is to advance understanding on how product design affects energy/material consumption and air emissions of representative AM processes. If successful, this research will bridge geometry and life cycle inventory (LCI), which provides much needed data foundation for LCA of AM processes and furthermore, enables the exploration of opportunities to re-design AM process and equipment for smaller environmental impacts. The research will be undertaken by a team consisting of experts in life cycle assessment, additive manufacturing, manufacturing process modeling, and pollution prevention at the Purdue University and the University of Illinois at Chicago (UIC). Four research tasks will be jointly pursued: (1) Material flow modeling and feedstock chemical composition analysis, (2) Energy consumption modeling, (3) Air emission characterization and quantification, and (4) AM process and equipment redesign for improved environmental performance. The research findings will be verified in both laboratory and industrial settings. The research is an effort to systematically evaluate the environmental impacts of the emerging AM processes using LCA methodology, with particular focus on unit process level LCI. The project will advance understanding of the material/energy flow and air emissions associated with four representative AM processes. The proposed LCI model is capable of predicting inventory flows of environmental significance while requiring only minimal experimental efforts. The research is an effort to bridge geometry and LCI of AM processes, which can guide the design and development of AM equipment and processes for enhanced environmental sustainability. Deeper understanding on energy consumption, feedstock material composition, and occupation hazard risk due to particulate matters and volatile organic compounds can provide guidelines for designers, manufacturers, and government agencies to safeguard workforce and reduce environmental footprints of AM processes. This will in turn accelerate the larger scale technology adoption of AM, and offer a competitive edge regarding sustainability to the U.S. manufacturing sector in the global market. Research results will be broadly disseminated through journal papers, conference presentations, online demonstrations, and industrial collaboration. The educational activities will strengthen the awareness of advanced life cycle analysis on additive manufacturing in engineering and environmental fields. Research results will be incorporated into both graduate and undergraduate courses at the University of Illinois at Chicago and Purdue University to positively impact engineering education.