Award Abstract # 2133630
NSF Engineering Research Center for Hybrid Autonomous Manufacturing Moving from Evolution to Revolution (ERC-HAMMER)

NSF Org: EEC
Division of Engineering Education and Centers
Recipient: OHIO STATE UNIVERSITY, THE
Initial Amendment Date: August 9, 2022
Latest Amendment Date: August 16, 2024
Award Number: 2133630
Award Instrument: Cooperative Agreement
Program Manager: Sumanta Acharya
sacharya@nsf.gov
 (703)292-4509
EEC
 Division of Engineering Education and Centers
ENG
 Directorate for Engineering
Start Date: September 1, 2022
End Date: August 31, 2027 (Estimated)
Total Intended Award Amount: $25,938,414.00
Total Awarded Amount to Date: $13,728,166.00
Funds Obligated to Date: FY 2022 = $3,498,592.00
FY 2023 = $4,479,599.00

FY 2024 = $5,749,975.00
History of Investigator:
  • Glenn Daehn (Principal Investigator)
    daehn.1@osu.edu
  • Jian Cao (Co-Principal Investigator)
  • Jagannathan Sankar (Co-Principal Investigator)
  • Tony Schmitz (Co-Principal Investigator)
  • John Lewandowski (Co-Principal Investigator)
Recipient Sponsored Research Office: Ohio State University
1960 KENNY RD
COLUMBUS
OH  US  43210-1016
(614)688-8735
Sponsor Congressional District: 03
Primary Place of Performance: The Ohio State Universiy
1960 Kenny Road
Columbus
OH  US  43210-1016
Primary Place of Performance
Congressional District:
03
Unique Entity Identifier (UEI): DLWBSLWAJWR1
Parent UEI: MN4MDDMN8529
NSF Program(s): ERC-Eng Research Centers,
GOALI-Grnt Opp Acad Lia wIndus
Primary Program Source: 01002223DB NSF RESEARCH & RELATED ACTIVIT
01002324DB NSF RESEARCH & RELATED ACTIVIT

01002425DB NSF RESEARCH & RELATED ACTIVIT

01002526DB NSF RESEARCH & RELATED ACTIVIT

01002627DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 019Z, 123E, 129E, 1480, 1504, 7680
Program Element Code(s): 148000, 150400
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041

ABSTRACT

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 60)
Abdelmaola, Mohammed and Thurston, Brian and Panton, Boyd and Vivek, Anupam and Daehn, Glenn "The Effects of Target Thicknesses and Backing Materials on a Ti-Cu Collision Weld Interface Using Laser Impact Welding" Metals , v.14 , 2024 https://doi.org/10.3390/met14030342 Citation Details
Acquaah, Yaa T. and Gokaraju, Balakrishna and Tesioro, Raymond C. and Monty, Gregory and Roy, Kaushik "Occupancy and Thermal Preference-Based HVAC Control Strategy Using Multisensor Network" IEEE Sensors Journal , v.23 , 2023 https://doi.org/10.1109/JSEN.2023.3264474 Citation Details
Alberts, Matthew and St_John, Sam and Odie, Simon and Khojandi, Anahita and Jared, Bradley and Schmitz, Tony and Karandikar, Jaydeep and Coble, Jamie B "Transitioning from Simulation to Reality: Applying Chatter Detection Models to Real-World Machining Data" Machines , v.12 , 2024 https://doi.org/10.3390/machines12120923 Citation Details
Benton_Jr, Kevin and Dewberry, Nicholas and Jaiswal, Chandra and Chowdhury, Shuva and AlHmoud, Issa and Suarez, Derick and Ehmann, Kornel and Cao, Jian and Gokaraju, Balakrishna "Initial framework design of a digital twin mixed-reality-application on human-robot bi-directional collaboration for forming double curvature plate" Manufacturing Letters , v.41 , 2024 https://doi.org/10.1016/j.mfglet.2024.09.174 Citation Details
Cao, Jian and Bambach, Markus and Merklein, Marion and Mozaffar, Mojtaba and Xue, Tianju "Artificial intelligence in metal forming" CIRP Annals , v.73 , 2024 https://doi.org/10.1016/j.cirp.2024.04.102 Citation Details
Casukhela, Rohan and Vijayan, Sriram and Jinschek, Joerg R. and Niezgoda, Stephen R. "A Framework for the Optimal Selection of High-Throughput Data Collection Workflows by Autonomous Experimentation Systems" Integrating Materials and Manufacturing Innovation , v.11 , 2022 https://doi.org/10.1007/s40192-022-00280-5 Citation Details
Chen, Yi-Ping and Wang, Liwei and Comlek, Yigitcan and Chen, Wei "A Latent Variable Approach for Non-Hierarchical Multi-Fidelity Adaptive Sampling" Computer Methods in Applied Mechanics and Engineering , v.421 , 2024 https://doi.org/10.1016/j.cma.2024.116773 Citation Details
Chen, Yixue and Zhang, Jianjing and Babinec, Tyler and Thurston, Brian and Daehn, Glenn and Dean, David and Loparo, Kenneth and Hoelzle, David and Gao, Robert X "Machine Learning-Enhanced Model Predictive Control for Incremental Bending of Skeletal Fixation Plates" , 2024 https://doi.org/10.1115/ISFA2024-140948 Citation Details
Chmielewska, Agnieszka and Dean, David "The role of stiffness-matching in avoiding stress shielding-induced bone loss and stress concentration-induced skeletal reconstruction device failure" Acta Biomaterialia , v.173 , 2024 https://doi.org/10.1016/j.actbio.2023.11.011 Citation Details
Cooper, Clayton and Zhang, Jianjing and Gao, Robert X. "Error homogenization in physics-informed neural networks for modeling in manufacturing" Journal of Manufacturing Systems , v.71 , 2023 https://doi.org/10.1016/j.jmsy.2023.09.013 Citation Details
Cooper, Clayton and Zhang, Jianjing and Huang, Joshua and Bennett, Jennifer and Cao, Jian and Gao, Robert X. "Tensile strength prediction in directed energy deposition through physics-informed machine learning and Shapley additive explanations" Journal of Materials Processing Technology , v.315 , 2023 https://doi.org/10.1016/j.jmatprotec.2023.117908 Citation Details
(Showing: 1 - 10 of 60)

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