Planning Grant: Engineering Research Center for Resilient AI Network (RAIN) for next-generation manufacturing
Planning Grant: Engineering Research Center for Resilient AI Network (RAIN) for next-generation manufacturing
批准号:
2124295
负责人:
Ali Shakouri
金额:
$10.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-08-01 至 2022-07-31
中文摘要
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英文摘要
There is an increasing digital divide making it difficult for small and medium manufacturers to be globally competitive. Although they are the lifeblood of well-paying jobs in rural communities, many small and medium manufacturers throughout the U.S. do not have access to the large datasets needed to train classical artificial intelligence / machine learning (AI/ML) algorithms. Progress in the U.S. is currently hampered by this digital-divide as well as a fragmented ecosystem that obviates rapid scale-up. Unfortunately, the rigidity of this system provides little safety net, as shown during the COVID-19 pandemic and the massive electricity blackout in Texas. This planning grant will help develop a nationwide engineering center that is trusted and empowered by industry partners of all sizes. The vision of the proposed Resilient AI Network (RAIN) for next-generation manufacturing ERC is to empower U.S. manufacturers to increase resiliency and international competitiveness by successfully adopting privacy-preserving, scalable, certify-as-you-build AI/ML practices to erase the digital-divide and accrue the benefit of economy-of-scale without centralization. This new manufacturing framework/paradigm based on the fundamental reimagination of AI/ML techniques will empower U.S. manufacturers of all sizes to lead among global competitors. The planning grant will help foster the partnership with community leaders, K-12 and colleges to develop a plan to attract a new diverse engineering workforce gaining insights from AI and data analytic tools and equipped to bring new products to the market.RAIN will enable distributed edge-AI tools integrating inline imaging, sound, vibration, and multi-sensor analytics. This can significantly improve manufacturing quality control, and machine-up time in many industries. AI-commons optimizing privacy-utility tradeoffs for industry cooperatives will be studied. Human-centered design is essential for resilient and agile manufacturing as new products or changes in supply-chain introduce new conditions requiring human input. The RAIN team brings together experts from AI and computer systems, human-centered design, policy, privacy, and engineering education as well as those with extensive experience in manufacturing scale up, testbeds, and life-cycle environmental and economic analysis. A key focus of this planning grant will be bolstering RAIN’s partnerships, resources, and support among our broad range of stakeholders. A central goal will be enhancing RAIN’s industry partnerships, which require a trusting relationship since our vision is high risk but with a high reward. With this planning grant, RAIN will expand our number of trusted industry partnerships through three key methods: (1) sharing capacity-building and value creation responsibilities among all our university partners by expanding existing industry-university relationships through networking that brings together stakeholders; (2) cultivating new relationships with small and medium manufacturers; and (3) hosting workshops with appropriate stakeholders to gain buy-in from manufacturers to issues of intellectual properties (IP), secure a common platform, and promote workforce development opportunities. RAIN will broaden its activities by developing plans for recruiting and accessing an educated, diverse workforce, as well as promoting training opportunities for current employees within our manufacturing demonstration facilities. RAIN will be implementing a comprehensive plan for promoting a diverse and inclusive team.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.
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