RII Track-2 FEC: Explainable and Adaptable Artificial Intelligence for Advanced Manufacturing
RII Track-2 FEC: Explainable and Adaptable Artificial Intelligence for Advanced Manufacturing
批准号:
2218063
负责人:
Yifeng Zhu
金额:
$600.0万
依托单位:
依托单位国家:
美国
项目类别:
Cooperative Agreement
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-08-01 至 2026-07-31
中文摘要
先进技术从根本上改变了制造业,对现代经济繁荣至关重要。该项目的目标是利用新兴技术,即人工智能(AI)、3D金属打印和机器人技术,提高新英格兰北部先进制造(AdvMfg)的质量、能力、安全性和可持续性。该项目还将鼓励在工业中采用新技术,以应对该地区面临的制造业挑战。这两个目标将通过创建一个科学和地理上相互关联的团队来实现,即东北综合智能制造实验室(NIIM),由来自缅因州大学、新汉普郡大学、佛蒙特州大学、达特茅斯学院、缅因州南部社区学院和佛蒙特州技术学院社区的成员组成。虽然NIIM的初始资金来自国家科学基金会(NSF)研究基础设施改善Track-2重点EPSCoR协作(RII Track-2 FEC)奖,但NIIM将在未来几年持续影响缅因州(ME)、新罕布夏州(NH)和佛蒙特州(VT)的EPSCoR辖区。NIIM将利用每个州的独特优势和丰富资产,并充分利用现有的州和联邦投资。该项目的研究团队由早期职业教师和高级导师领导,将研究如何将最先进的人工智能技术整合到现代制造流程和系统中。该项目将把STEM经验扩展到本科生和毕业生,特别是那些在STEM领域代表性不足的人。该项目还将为可能成为第一代本科生的低收入高中生创造新的向上跳跃组件,为社区和技术学院的残疾学生和工作者创造新的东北通道。该团队将与这三个州的制造业扩展合作伙伴计划(MEP)、工业咨询委员会、行业合作伙伴和美国经济发展管理局大学经济发展中心密切合作。与这些组织合作将确保这个Track-2项目与州和地区的经济发展优先事项保持密切联系。在这个工业4.0时代,智能工具和技术正在开辟新的维度,以优化制造流程和系统。在该项目中建立的东北集成智能制造实验室(NIIM)旨在创建一个新的、可解释和可适应的人工智能框架,以填补制造业现有和未来的技术空白,如漫长而昂贵的实验和模拟、多台机器之间缺乏协调,以及难以为复杂的制造任务编程机器人。以及(F)了解影响制造企业采用新技术的因素。该项目预期的具体结果将与新英格兰北部的AdvMfg公司直接相关。该项目将通过相关研究、劳动力发展和教育为新英格兰北部的制造业提供服务。
英文摘要
Advanced technologies have radically transformed manufacturing and are essential to modern economic prosperity. The goal of this project is to leverage emerging technologies, i.e., artificial intelligence (AI), 3D metal printing, and robotics, to increase the quality, capability, safety, and sustainability of Advanced Manufacturing (AdvMfg) in northern New England. The project will also encourage the adoption of new technologies in industry to address manufacturing challenges facing the region. These two objectives will be accomplished by creating a scientifically- and geographically-interlinked team, i.e., Northeast Integrated Intelligent Manufacturing Lab (NIIM), consisting of members from the University of Maine, University of New Hampshire, University of Vermont, Dartmouth College, Southern Maine Community College, and Vermont Technical College communities. Although initial funding for NIIM is from a National Science Foundation (NSF) Research Infrastructure Improvement Track-2 Focused EPSCoR Collaboration (RII Track-2 FEC) award, NIIM will sustainably impact the EPSCoR jurisdictions of Maine (ME), New Hampshire (NH), and Vermont (VT) for years to come. NIIM will draw on the unique strengths and rich assets of each state, and fully leverage existing state and federal investments. The project's research team, led by early career faculty and senior mentors, will investigate how to integrate state-of-the-art AI techniques into modern manufacturing processes and systems. A proactive large-scale workforce and economic development assessment will identify the technological needs of firms in the region, which will inform project research and outreach activities, as well as identify skills gaps and opportunities for training and building career pathways in AdvMfg. The project will extend STEM experiences to undergraduates and graduates, especially those underrepresented in STEM fields. This project will also create new components for Upward Bound for low-income high school students, who will potentially be first-generation undergraduate students, and Northeast Passage for disabled students and workers at community and technical colleges. The team will work closely with the manufacturing extension partnership programs (MEPs) in the three states, an industrial advisory board, industry partners, and the US Economic Development Administration University Center for Economic Development. Working with these organizations will ensure that this Track-2 project remains closely tied to state and regional economic development priorities.In this era of Industry 4.0, intelligent tools and techniques are opening new dimensions to optimize manufacturing processes and systems. The Northeast Integrated Intelligent Manufacturing Lab (NIIM), established in this project, aims to create a new, explainable and adaptable AI framework that fills existing and future technology gaps in manufacturing, such as long and expensive experiments and simulations, lack of coordination among multiple machines, and difficulty in programming robots for complicated manufacturing tasks. Our convergent research teams across three EPSCoR jurisdictions (ME, NH and VT) will work closely with industry to create: (a) new AI models with intrinsic interpretability and increased adaptability to support Advanced Manufacturing (AdvMfg); (b) AI-guided design for additive manufacturing of metals that seamlessly connects multi-scale modeling and property predictions without unnecessary trial-and-error; (c) self-aware CNC machines that optimize the coordination and control in subtractive manufacturing; (d) industrial robots that efficiently and safely learn from video demonstrations for cellular manufacturing; (e) an industry-driven, unified hybrid manufacturing framework; and (f) an understanding of the factors that influence the adoption of new technologies by manufacturing businesses. The project anticipates specific outcomes that will be of immediate relevance to AdvMfg companies in Northern New England. For example, it is expected that the project will yield sample-efficient robot learning techniques that will enable factory workers to teach robots new skills through visual demonstrations, allow robots to learn from failure and request relevant demonstrations, and generate risk-bounded safe policies using uncertainty aware learning. This project will serve the northern New England manufacturing sector through relevant research, workforce development, and education. Diversity and inclusion efforts are integrated to remove barriers to STEM education for underrepresented, low income, potential first-generation, and/or disabled individuals.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.
期刊论文(2)
专著(0)
科研奖励(0)
会议论文
DOI:
10.1145/3583131.3590430
发表时间:
2023-07
期刊:
Proceedings of the Genetic and Evolutionary Computation Conference
影响因子:
--
作者:
[Lapo Frati;Neil Traft;Nick Cheney]
通讯作者:
Lapo Frati;Neil Traft;Nick Cheney
DOI:
10.1145/3583133.3590693
发表时间:
2023-07
期刊:
Proceedings of the Companion Conference on Genetic and Evolutionary Computation
影响因子:
--
作者:
[Jackson Dean;Nick Cheney]
通讯作者:
Jackson Dean;Nick Cheney
SHF: SMALL: Collaborative Research: Improving Reliability of In-Memory Storage
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批准号:1618536
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项目类别:Standard Grant
-
资助金额:$21.36万
-
财政年份:2016
-
负责人:Yifeng Zhu
-
依托单位:
CSR: Small: Collaborative Research: SANE: Semantic-Aware Namespace in Exascale File Systems
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批准号:1117032
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项目类别:Standard Grant
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资助金额:$19.09万
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财政年份:2011
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负责人:Yifeng Zhu
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依托单位:
CDI-Type I: GPU-Accelerated Interactive Supercomputing for Climate Studies in the Northern Environment
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批准号:1027809
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项目类别:Standard Grant
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资助金额:$45.46万
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财政年份:2010
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负责人:Yifeng Zhu
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依托单位:
Collaborative Research: HECURA: A New Semantic-Aware Metadata Organization for Improved File-System Performance and Functionality in High-End Computing
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批准号:0937988
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项目类别:Standard Grant
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资助金额:$36.31万
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财政年份:2009
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负责人:Yifeng Zhu
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依托单位:
DC:Small: Energy-aware Coordinated Caching in Cluster-based Storage Systems
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批准号:0916663
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项目类别:Standard Grant
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资助金额:$13.93万
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财政年份:2009
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负责人:Yifeng Zhu
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依托单位:
REU Site: Supercomputing Undergraduate Program in Maine (SuperMe)
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批准号:0754951
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项目类别:Continuing Grant
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资助金额:$30.0万
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财政年份:2008
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负责人:Yifeng Zhu
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依托单位:
HEC: Collaborative Research: SAM^2 Toolkit: Scalable and Adaptive Metadata Management for High-End Computing
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批准号:0621493
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项目类别:Standard Grant
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资助金额:$23.69万
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财政年份:2006
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负责人:Yifeng Zhu
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依托单位:
海外基金