RII Track-2 FEC: Explainable and Adaptable Artificial Intelligence for Advanced Manufacturing

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。该项目还将鼓励工业采用新技术，以解决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，即东北集成智能制造实验室（NIIM），由来自缅因大学、新罕布什尔大学、佛蒙特大学、达特茅斯学院、南缅因社区学院和佛蒙特技术学院社区的成员组成。尽管NIIM的初始资金来自美国国家科学基金会（NSF）研究基础设施改进轨道2重点EPSCoR合作（RII Track-2 FEC）奖，但NIIM将在未来几年持续影响缅因州（ME）、新罕布什尔州（NH）和佛蒙特州（VT）的EPSCoR管辖区。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。积极主动的大规模劳动力和经济发展评估将确定该区域公司的技术需求，这将为项目研究和推广活动提供信息，并确定技能差距和培训机会，并在AdvMfg建立职业道路。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。该项目还将为低收入高中学生（可能是第一代本科生）创建新的“向上发展”项目，并为残疾学生和社区技术学院的工人创建东北通道。该团队将与三个州的制造业扩展合作伙伴计划（MEPs）、工业顾问委员会、行业合作伙伴以及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一轨道2项目与州和区域经济发展优先事项密切相关。在工业4.0时代，智能工具和技术正在为优化制造流程和系统开辟新的维度。该项目成立的东北集成智能制造实验室（NIIM）旨在创建一个新的、可解释的、适应性强的人工智能框架，以填补制造业现有和未来的技术空白，例如漫长而昂贵的实验和仿真、多台机器之间缺乏协调、机器人编程难以完成复杂的制造任务。我们在三个EPSCoR管辖区（ME， NH和VT）的融合研究团队将与行业密切合作，创建：(a)具有内在可解释性和更高适应性的新AI模型，以支持先进制造（AdvMfg）；(b)人工智能引导的金属增材制造设计，无缝连接多尺度建模和属性预测，无需不必要的反复试验；(c)在减法制造中优化协调和控制的自我感知数控机床；(d)工业机器人，有效和安全地从视频演示中学习细胞制造；(e)行业驱动的统一混合制造框架；(f)了解影响制造企业采用新技术的因素。该项目预计具体的结果将与新英格兰北部的AdvMfg公司直接相关。例如，预计该项目将产生样本高效的机器人学习技术，使工厂工人能够通过视觉演示教机器人新技能，允许机器人从失败中学习并要求相关演示，并使用不确定性感知学习生成风险有限的安全策略。该项目将通过相关研究、劳动力发展和教育服务于新英格兰北部的制造业。将多样性和包容性相结合，为代表性不足、低收入、潜在的第一代和/或残疾人消除STEM教育的障碍。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.