NSF Engineering Research Center for Hybrid Autonomous Manufacturing Moving from Evolution to Revolution (ERC-HAMMER)

NSF 混合自主制造工程研究中心从进化到革命 (ERC-HAMMER)

• 批准号: 2133630
• 负责人: Glenn Daehn
• 金额: $ 2593.84万
• 依托单位: Ohio State University
• 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
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2133630&HistoricalAwards=false
• 关键词: NSF; Engineering; Research; Center; Hybrid

The Engineering Research Center, Hybrid Autonomous Manufacturing, Moving from Evolution to Revolution (HAMMER), will advance national goals to assert American leadership in advanced manufacturing by developing and transitioning new manufacturing technologies to industry use. Simultaneously, the Center will drive new technical education and provide credentials that will prepare, upskill, or reskill the relevant workforce, and expand capabilities across the manufacturing supply chain to meet national needs. Core partners of the Center include The Ohio State University, Northwestern University, North Carolina Agricultural and Technical State University, Case Western Reserve University, and the University of Tennessee. They will work with collaborators from more than 70 industries, educational, and technical organizations to develop and implement new manufacturing technologies for agile, high-performance and quality-assured components. Through basic, applied, and translational research, HAMMER will accelerate the development and deployment of intelligent autonomous manufacturing systems that will use multiple processes to control material properties and component dimensions to allow rapid customization and high assured performance. These systems will learn from each operation, improving themselves over time. Importantly, as HAMMER works to develop a new class of engineers and technicians, it will also actively work to enhance diversity in the manufacturing talent pipeline, building on the evidence-based success of Fab Labs and Makerspaces to attract students and improve outcomes. Special emphasis will be focused on including urban, military, and Appalachian communities in educational pipeline programs. Ultimately, HAMMER will ensure this country’s competitive advantage, rebuild the U.S. industrial base, create new high-skilled, highly paid jobs, and unleash American ingenuity by providing cost-effective, local, customized production. HAMMER’s primary goal is to enable the concurrent design of products with novel manufacturing processes using hybrid (or multi-tool) manufacturing systems and pathways. This approach will automate and greatly extend the flexibility and ingenuity of practicing human artisans. The HAMMER framework will use designs that will enable leveraging recent developments in robotics and sensors, leading to novel convergent processes. New control, autonomy, and intelligence approaches will guide, and learn from prior manufacturing processes. Quality will be assured through understanding and predicting the local structure and properties of the material being processed within quantified uncertainty limits. Ultimately, HAMMER will advance the current state of technology to unite design, tools, artificial intelligence and computational materials engineering into a single framework, enabling the agile production of components. These components will possess locally optimized materials chemistry, microstructure, and properties in ways that are not attainable currently. The relevant systems are expected to improve in efficiency and performance with experience. Specific use cases to be considered include: 1) numerically controlled deformation sequences and equipment to create complex components that may be currently produced as closed die forgings, but with reduced lead-time and improved performance, 2) employing numerically-controlled deformation to locally optimize properties in additively manufactured components, 3) expanding capabilities for point-of-care manufacturing wherein automated operations including deformation are used to rapidly tailor medical devices to the patient anatomy, and 4) developing low-cost, desktop training systems that provide students hands-on learning in programming, operating, and maintaining new manufacturing systems, as well as experiences creating new physical products using incremental deformation and hybrid processes. Strong partnerships with industry, educational and technical organizations will enable HAMMER to train personnel at many levels from pre-college to practicing engineers. HAMMER will lead next-generation certification standards to facilitate widespread adoption of these technologies by the associated workforce.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.

混合自主制造工程研究中心，从进化到革命（HAMMER），将通过开发新的制造技术并将其转化为工业应用来推进国家目标，以维护美国在先进制造领域的领导地位。该中心的核心合作伙伴包括俄亥俄州立大学、西北大学、北卡罗来纳州立农业技术大学、凯斯大学。西部保留地他们将与来自 70 多个行业、教育和技术组织的合作者合作，通过基础、应用和转化来开发和实施敏捷、高性能和质量有保证的组件的新制造技术。通过研究，HAMMER 将加速智能自主制造系统的开发和部署，该系统将使用多个流程来控制材料属性和组件尺寸，以实现快速定制和高保证的性能，这些系统将从每次操作中学习，并随着时间的推移不断改进。 HAMMER 致力于开发新的它还将积极致力于增强制造人才渠道的多样性，以 Fab Labs 和 Makerspaces 的循证成功为基础，以吸引学生并改善成果，特别注重包括城市、军事、最终，HAMMER 将确保这个国家的竞争优势，重建美国的工业基础，创造新的高技能、高薪工作，并通过提供具有成本效益的本地定制生产来释放美国人的创造力。 HAMMER 的主要目标是使用混合（或多工具）制造系统和路径实现产品与新颖制造流程的并行设计，这种方法将实现自动化并极大地扩展人类工匠的灵活性和独创性。这将能够利用机器人和传感器的最新发展，从而产生新的融合流程，并通过了解和预测现有的制造流程来指导和学习质量。正在加工的材料最终，HAMMER 将推进当前的技术水平，将设计、工具、人工智能和计算材料工程整合到一个框架中，从而实现组件的敏捷生产。相关系统预计将随着经验的积累而提高效率和性能，具体用例包括：1）数控变形序列和设备，以创建当前可能生产的复杂部件。闭式模锻件，但是缩短交货时间并提高性能，2) 采用数控变形来局部优化增材制造组件的性能，3) 扩大现场护理制造能力，使用包括变形在内的自动化操作来快速定制医疗设备，以适应医疗设备的需求。患者解剖学；4) 开发低成本桌面培训系统，为学生提供编程、操作和维护新制造系统的实践学习，以及使用增量变形和混合流程创建新物理产品的经验。工业、教育和技术组织将使 HAMMER 能够培训从大学预科生到执业工程师的多个级别的人员。HAMMER 将引领下一代认证标准，以促进相关劳动力广泛采用这些技术。该奖项反映了 NSF 的法定使命，并被认为值得支持。通过使用基金会的智力优点和更广泛的影响审查标准进行评估。

项目成果

Robot forming: Automated English wheel as an avenue for flexibility and repeatability

机器人成型：自动化英式轮作为灵活性和可重复性的途径

• DOI: 10.1016/j.mfglet.2023.08.104
• 发表时间: 2023
• 期刊: Manufacturing Letters
• 影响因子: 3.9
• 作者: Huang, Dean;Suarez, Derick;Kang, Putong;Ehmann, Kornel;Cao, Jian
• 通讯作者: Cao, Jian

A Framework for the Optimal Selection of High-Throughput Data Collection Workflows by Autonomous Experimentation Systems

• DOI: 10.1007/s40192-022-00280-5
• 发表时间: 2022-10-31
• 期刊: INTEGRATING MATERIALS AND MANUFACTURING INNOVATION
• 影响因子: 3.3
• 作者: Casukhela, Rohan;Vijayan, Sriram;Niezgoda, Stephen R.
• 通讯作者: Niezgoda, Stephen R.

Hybrid manufacturing by additive friction stir deposition, metrology, CNC machining, and microstructure analysis

通过增材搅拌摩擦沉积、计量、数控加工和微观结构分析进行混合制造

• DOI: 10.1016/j.mfglet.2023.08.021
• 发表时间: 2023
• 期刊: Manufacturing Letters
• 影响因子: 3.9
• 作者: Kincaid, Joshua;Zameroski, Ross;Charles, Elijah;No, Timothy;Bohling, John;Compton, Brett;Schmitz, Tony
• 通讯作者: Schmitz, Tony

Error homogenization in physics-informed neural networks for modeling in manufacturing

• DOI: 10.1016/j.jmsy.2023.09.013
• 发表时间: 2023-12
• 期刊: Journal of Manufacturing Systems
• 影响因子: 12.1
• 作者: Clayton Cooper;Jianjing Zhang;R. X. Gao

Blockchain-Empowered Distributed Additive Manufacturing-as-a-Service: An Architectural Perspective

区块链赋能的分布式增材制造即服务：架构视角

• DOI: 10.1109/mnet.129.2200459
• 发表时间: 2023
• 期刊: IEEE Network
• 影响因子: 9.3
• 作者: Monroy, Sergio A.;Li, Pan;Fang, Yuguang;Loparo, Kenneth A.
• 通讯作者: Loparo, Kenneth A.