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

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.

HAMMER will work to develop a new class of engineers and technicians to enhance the manufacturing talent pipeline, building on the evidence-based success of Fab Labs and Makerspaces to attract students and improve outcomes. 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.