This award provides support for the NIST-ASTM-NSF-ASME Workshop on Challenges in Representing Manufacturing Processes for Systemic Sustainability Assessments. The funding partially supports the travel and attendance costs of US scientists, postdoctoral researchers, and graduate students. The workshop is designed to engage the research community in discussions on emerging topics in advanced manufacturing, nanomanufacturing, sustainable manufacturing, and engineering education, all of which are integral to sustainability of advanced manufacturing in America, an enduringly important sector in the nation's economy. An outcome of the workshop is to identify needs for research and education in support of characterizing unit manufacturing processes for sustainability assessment. The research focus is to investigate modeling of manufacturing processes for system level sustainability assessment across manufacturing production scales and methods applied in various fields. The workshop encourages participation of women and under-represented minorities in discussions on advanced manufacturing research and education, also important for the nation's prosperity. It stimulates ideas for future directions in research and career paths. Workshop outcomes will be documented and disseminated as a workshop report and peer-reviewed publications.
This award partially supports the participation of graduate students, postdoctoral fellows, early career faculty, and women and under-represented minorities in a workshop on representations of manufacturing processes. The workshop focuses on research in modeling of unit manufacturing processes for discrete, batch, and continuous methods; and for conventional, nanometer, and additive manufacturing scales. Methodologies are applicable in fields such as mechanical, electrical, chemical, nuclear, biochemical, and biological. Sustainability requires a balance of competing objectives, such as cost, time, and environmental and social considerations. The use of these models for system-level sustainability performance is complicated due to uncertain emergent properties. However, these models are vital to comparing alternative designs of products and production systems. Acquiring and exchanging information on manufacturing processes can lead to consistent process characterization and can help establish a consolidated repository of process models for reuse across advanced manufacturing domains. Improved process modeling and model integration ensures more effective communication of computational analytics and sharing of sustainability performance data. Engaging industrial and academic researchers in discussing their work, sharing best practices, revealing gaps in research and practice is important in developing new research directions.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
