| Autor: |
Raman AS; School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, 200 SW Monroe Ave., Corvallis, OR 97331, USA., Haapala KR; School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, 200 SW Monroe Ave., Corvallis, OR 97331, USA., Raoufi K; School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, 200 SW Monroe Ave., Corvallis, OR 97331, USA., Linke BS; Department of Mechanical and Aerospace Engineering, University of California Davis, One Shields Ave., Davis, CA 95616, USA., Bernstein WZ; Systems Integration Division, Engineering Laboratory, National Institute of Standards and Technology, 100 Bureau Dr., Gaithersburg, MD 20899, USA., Morris KC; Systems Integration Division, Engineering Laboratory, National Institute of Standards and Technology, 100 Bureau Dr., Gaithersburg, MD 20899, USA. |
| Abstrakt: |
Over the past century, research has focused on continuously improving the performance of manufacturing processes and systems-often measured in terms of cost, quality, productivity, and material and energy efficiency. With the advent of smart manufacturing technologies-better production equipment, sensing technologies, computational methods, and data analytics applied from the process to enterprise levels-the potential for sustainability performance improvement is tremendous. Sustainable manufacturing seeks the best balance of a variety of performance measures to satisfy and optimize the goals of all stakeholders. Accurate measures of performance are the foundation on which sustainability objectives can be pursued. Historically, operational and information technologies have undergone disparate development, with little convergence across the domains. To focus future research efforts in advanced manufacturing, the authors organized a one-day workshop, sponsored by the U.S. National Science Foundation, at the joint manufacturing research conferences of the American Society of Mechanical Engineers and Society of Manufacturing Engineers. Research needs were identified to help harmonize disparate manufacturing metrics, models, and methods from across conventional manufacturing, nanomanufacturing, and additive/hybrid manufacturing processes and systems. Experts from academia and government labs presented invited lightning talks to discuss their perspectives on current advanced manufacturing research challenges. Workshop participants also provided their perspectives in facilitated brainstorming breakouts and a reflection activity. The aim was to define advanced manufacturing research and educational needs for improving manufacturing process performance through improved sustainability metrics, modeling approaches, and decision support methods. In addition to these workshop outcomes, a review of the recent literature is presented, which identifies research opportunities across several advanced manufacturing domains. Recommendations for future research describe the short-, mid-, and long-term needs of the advanced manufacturing community for enabling smart and sustainable manufacturing. |
