Abstract A workshop on Challenges in Representing Manufacturing Processes for Systematic Sustainability Assessments, jointly sponsored by the U.S. National Science Foundation, the U.S. National Institute of Standards and Technology, ASTM International, the American Society of Mechanical Engineers, and the Society of Manufacturing Engineers, was held in College Station, Texas on June 21, 2018. The goals of the workshop were to identify research needs supporting manufacturing process characterization, define limitations in associated education practices, and emphasize on challenges to be pursued by the advanced manufacturing research community. An important aspect surrounded the introduction and development of reusable abstractions of manufacturing processes (RAMP), which are standard representations of unit manufacturing processes to support the development of metrics, methods, and tools for the analysis of manufacturing processes and systems. This paper reports on the workshop activities and findings, which span the improvement of engineering education, the understanding of process physics and the influence of novel materials and manufacturing processes on energy and environmental impacts, and approaches for optimization and decision-making in the design of manufacturing systems. A nominal group technique was used to identify metrics, methods, and tools critical to advanced manufacturing industry as well as highlight the associated research challenges and barriers. Workshop outcomes provide a number of research directions that can be pursued to address the identified challenges and barriers. 
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                            Advanced Manufacturing: Navigating the Path Forward
                        
                    
    
            Abstract Highlights from a member survey and panel discussion outlining the challenges and benefits of advanced manufacturing for the materials community. The article covers research being conducted in academia and industry to develop and integrate advanced manufacturing techniques; methods for in-process monitoring and machine learning; and challenges in adopting new digital manufacturing technologies. 
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                            - Award ID(s):
- 2133630
- PAR ID:
- 10578466
- Publisher / Repository:
- Advanced Materials and Processes
- Date Published:
- Journal Name:
- AM&P Technical Articles
- Volume:
- 181
- Issue:
- 1
- ISSN:
- 0882-7958
- Page Range / eLocation ID:
- 23 to 31
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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