AI Institute: Planning: AI-Enabled Secure and Responsive Smart Manufacturing

人工智能研究院：规划：人工智能赋能的安全响应式智能制造

• 批准号: 2020246
• 负责人: Vijay Gupta
• 金额: $ 50万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2020246&HistoricalAwards=false
• 关键词: AI; Institute; Planning; Enabled; Secure

Advanced manufacturing is on the cusp of a Fourth Industrial revolution, captured by the term Industry 4.0 or Smart Manufacturing. However, challenges abound in realizing the smart manufacturing paradigm. Rapidly reconfigurable production systems envisaged in smart manufacturing are inherently tied to acquisition of large amounts of continuous streaming data from machines and automated robots on the shop floor, as well as from entities dispersed in the supply-logistics chain, using heterogeneous sensors. The data has to be transmitted over wireless channels with tight latency and security guarantees, and coupled with real-time analysis to allow precise trouble-shooting, coordination, and monitoring. The inescapable conclusion is that to enable smart manufacturing, a tight integration – and, in fact, cross-layer optimization – of manufacturing technologies, robotics and control, networking protocols such as 5G, and machine learning paradigms such as inference on the edge is needed. Achieving this vision is the goal of the institute envisaged in this planning grant. Realizing an effective institute will require experts in advanced manufacturing, AI, sensing, computation, and sensing, as well as collaboration with industry and federal research labs. The major activities in this planning grant aim at developing such a team through a range of activities. The intellectual bulwark of the institute is the enabling of smart manufacturing through a tight integration and cross-layer optimization of manufacturing technologies, networking protocols such as 5G, robotics and control, and machine learning paradigms such as inference on the edge. This integration will also generate and revitalize new research areas that encompass fundamental materials science to large-scale supply networking and cybersecurity to AI-enabled collaborative robotics and human-robot interaction and beyond. The activities also include new curriculum development both at the participating universities and community colleges, as well as plans for outreach through local governments and high schools, for developing the workforce for such a paradigm and broadening participation into AI-related technology among society.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.

先进制造正处于第四次工业革命的风口浪尖，即工业 4.0 或智能制造。然而，在实现智能制造范式方面存在大量挑战。智能制造中设想的快速可重构生产系统本质上与大量采购相关。来自车间机器和自动化机器人以及分散在供应物流链中的实体的连续流数据，使用异构传感器。数据必须通过具有严格延迟和安全保证的无线通道进行传输，并结合。实时分析以实现精确的故障排除、协调和监控不可避免的结论是，要实现智能制造，就需要制造技术、机器人和控制、网络协议的紧密集成（实际上是跨层优化）。实现这一愿景是该研究所在本次拨款规划中设想的目标，需要先进制造、人工智能、传感、计算和人工智能领域的专家。传感、以及与工业界和联邦研究实验室的合作。该计划的主要活动旨在通过一系列活动来发展这样一个团队，该研究所的智力堡垒是通过紧密集成和跨层来实现智能制造。制造技术、5G、机器人和控制等网络协议以及边缘推理等机器学习范式的优化，还将产生和振兴新的研究领域，涵盖基础材料科学、大规模供应网络和网络安全。人工智能支持的协作这些活动还包括参与大学和社区学院的新课程开发，以及通过地方政府和高中进行推广的计划，以培养此类范例的劳动力并扩大参与范围。社会中与人工智能相关的技术。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Effect of contaminations on the acoustic emissions during wire and arc additive manufacturing of 316L stainless steel

• DOI: 10.1016/j.addma.2021.102585
• 发表时间: 2021-12
• 期刊: Additive Manufacturing
• 影响因子: 11
• 作者: A. Ramalho;T. Santos;Ben Bevans;Z. Smoqi;Prahalada K. Rao;J. P. Oliveira

Closed-loop Control of Meltpool Temperature in Directed Energy Deposition

• DOI: 10.1016/j.matdes.2022.110508
• 发表时间: 2022-03
• 期刊: Materials & Design
• 作者: Z. Smoqi;Ben Bevans;A. Gaikwad;J. Craig;Alan Abul-Haj;B. Roeder;B. Macy;J. Shield;Prahalada K. Rao

Adversarial attacks in consensus-based multi-agent reinforcement learning

• DOI: 10.23919/acc50511.2021.9483080
• 发表时间: 2021-03
• 期刊: 2021 American Control Conference (ACC)
• 作者: Martin Figura;K. Kosaraju;V. Gupta

In-process monitoring and prediction of droplet quality in droplet-on-demand liquid metal jetting additive manufacturing using machine learning

• DOI: 10.1007/s10845-022-01977-2
• 发表时间: 2022-06
• 期刊: Journal of Intelligent Manufacturing
• 影响因子: 8.3
• 作者: A. Gaikwad;Tammy Chang;B. Giera;N. Watkins;S. Mukherjee;A. Pascall;D. Stobbe;Prahalada K. Rao

Prediction of recoater crash in laser powder bed fusion additive manufacturing using graph theory thermomechanical modeling

• DOI: 10.1007/s40964-022-00331-5
• 发表时间: 2022-08-05
• 期刊: PROGRESS IN ADDITIVE MANUFACTURING
• 作者: Kobir, Md Humaun;Yavari, Reza;Rao, Prahalada
• 通讯作者: Rao, Prahalada