Award Abstract # 2119654
RII Track 2 FEC: Enabling Factory to Factory (F2F) Networking for Future Manufacturing

NSF Org: OIA
OIA-Office of Integrative Activities
Recipient: UNIVERSITY OF SOUTH CAROLINA
Initial Amendment Date: September 16, 2021
Latest Amendment Date: September 5, 2024
Award Number: 2119654
Award Instrument: Cooperative Agreement
Program Manager: Pinhas Ben-Tzvi
OIA
 OIA-Office of Integrative Activities
O/D
 Office Of The Director
Start Date: October 1, 2021
End Date: March 31, 2025 (Estimated)
Total Intended Award Amount: $3,832,326.00
Total Awarded Amount to Date: $3,832,326.00
Funds Obligated to Date: FY 2021 = $931,932.00
FY 2022 = $953,257.00

FY 2024 = $1,947,137.00
History of Investigator:
  • Ramy Harik (Principal Investigator)
    harik@clemson.edu
  • Amit Sheth (Co-Principal Investigator)
  • Paul Ziehl (Co-Principal Investigator)
  • Thorsten Wuest (Co-Principal Investigator)
  • Zhichao Liu (Co-Principal Investigator)
Recipient Sponsored Research Office: University of South Carolina at Columbia
1600 HAMPTON ST
COLUMBIA
SC  US  29208-3403
(803)777-7093
Sponsor Congressional District: 06
Primary Place of Performance: University of South Carolina at Columbia
Columbia
SC  US  29208-0001
Primary Place of Performance
Congressional District:
06
Unique Entity Identifier (UEI): J22LNTMEDP73
Parent UEI: Q93ZDA59ZAR5
NSF Program(s): EPSCoR RII: Focused EPSCoR Col,
EPSCoR Research Infrastructure
Primary Program Source: 01002425DB NSF RESEARCH & RELATED ACTIVIT
01002425DB NSF RESEARCH & RELATED ACTIVIT

01002122DB NSF RESEARCH & RELATED ACTIVIT

01002324DB NSF RESEARCH & RELATED ACTIVIT

01002223DB NSF RESEARCH & RELATED ACTIVIT

01002223DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 8037, 075Z, 9150, 7715, 7569
Program Element Code(s): 194Y00, 721700
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.083

ABSTRACT

Cyber infrastructure and artificial intelligence (AI) are core components of smart manufacturing in South Carolina (SC), West Virginia (WV), and the United States. To drive radical transformation of industry, factories must securely expand beyond their physical boundaries. These Future Factories (FF) consume and create interdisciplinary knowledge along with the ability to forge innovative technological transformations. This project introduces a novel future cyber-manufacturing paradigm ? the Factory-to-Factory (F2F) network framework. Geared towards automation, F2F networks require interoperability of stakeholders and efficient understanding systems for data, information, and knowledge. This collaboration between academia ? led by the University of South Carolina and West Virginia University - and industry will produce advanced smart manufacturing technologies and an educated upskilled workforce in SC and WV. Furthermore, it will create a blueprint model for future manufacturing technologies that can be integrated with a F2F network to increase small-scale and industrial manufacturing capabilities across the US. To expand our workforce infrastructure, we will establish a lifelong learning pipeline for smart manufacturing ranging from K-12 education, higher education, to professional development support for scholars and industrial professionals. Particularly, we will create online learning resources and STEM-oriented smart manufacturing summer programs for K-12 students and provide internships for college and graduate students through our industrial partners.

This project will adapt, enhance, and integrate informational technologies (IT) and operational technologies (OT) such as real-time secured sensing, high performance computing, wireless communications, and AI, to support process optimization among distributed smart manufacturing systems for F2F. Convergence and true progress can only be achieved by fusing expert knowledge of manufacturing processes with newly emerged hardware and software technologies. The project focuses on manufacturing knowledge stemming from: (1) autonomous feature extraction and recognition from product ?manufacturing DNAs? as a novel manufacturing knowledge representation among distributed systems, (2) architecture of interactive cyber spaces that combines cross-platform simulation results within product lifecycles, (3) data-driven control theories during process monitoring leading to rapid autonomous decision-making in replacement of manual input/output modules, and (4) robust business models and operations research (information service-oriented) concerning autonomous Key Performance Indicator (KPI) decomposition among distributed sub-systems and rapid feedback control loops. This will build a foundation for real-time production information sharing and control platforms and facilitate manufacturing knowledge generation and utilization among networked systems to address manufacturing management challenges. Furthermore, it enables human interventions and interoperations in the development and decision-making process of these highly collaborative networked smart manufacturing systems. This project will showcase several novel cyber manufacturing implementations and establish a roadmap towards a universal digital F2F standard.

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 11)
Venkataramanan, Revathy and Tripathy, Aalap and Foltin, Martin and Yip, Hong Yung and Justine, Annmary and Sheth, Amit "Knowledge Graph Empowered Machine Learning Pipelines for Improved Efficiency, Reusability, and Explainability" IEEE Internet Computing , v.27 , 2023 https://doi.org/10.1109/MIC.2022.3228087 Citation Details
Wickramarachchi, Ruwan and Henson, Cory and Sheth, Amit "Knowledge-infused Learning for Entity Prediction in Driving Scenes" Frontiers in Big Data , v.4 , 2021 https://doi.org/10.3389/fdata.2021.759110 Citation Details
Xia, Kaishu and Wuest, Thorsten and Harik, Ramy "Automated manufacturability analysis in smart manufacturing systems: a signature mapping method for product-centered digital twins" Journal of Intelligent Manufacturing , 2022 https://doi.org/10.1007/s10845-022-01991-4 Citation Details
Rahman, Md Habibor and Wuest, Thorsten and Shafae, Mohammed "Manufacturing cybersecurity threat attributes and countermeasures: Review, meta-taxonomy, and use cases of cyberattack taxonomies" Journal of Manufacturing Systems , v.68 , 2023 https://doi.org/10.1016/j.jmsy.2023.03.009 Citation Details
Kalach, Fadi El and Wickramarachchi, Ruwan and Harik, Ramy and Sheth, Amit "A Semantic Web Approach to Fault Tolerant Autonomous Manufacturing" IEEE Intelligent Systems , v.38 , 2023 https://doi.org/10.1109/MIS.2023.3235677 Citation Details
Jaimini, Utkarshani and Zhang, Tongtao and Brikis, Georgia Olympia and Sheth, Amit "i MetaverseKG: I ndustrial Metaverse Knowledge Graph to Promote Interoperability in Design and Engineering Applications" IEEE Internet Computing , v.26 , 2022 https://doi.org/10.1109/MIC.2022.3212085 Citation Details
Jaimini, Utkarshani and Sheth, Amit "CausalKG: Causal Knowledge Graph Explainability Using Interventional and Counterfactual Reasoning" IEEE Internet Computing , v.26 , 2022 https://doi.org/10.1109/MIC.2021.3133551 Citation Details
Era, Israt Zarin and Grandhi, Manikanta and Liu, Zhichao "Prediction of mechanical behaviors of L-DED fabricated SS 316L parts via machine learning" The International Journal of Advanced Manufacturing Technology , v.121 , 2022 https://doi.org/10.1007/s00170-022-09509-1 Citation Details
Liu, Zhichao and Grandhi, Manikanta and Era, Israt Zarin and Zhang, Hong-Chao "Analytical modeling and experimental investigation of the particle scale energy absorption mechanism in laser-based directed energy deposition" Journal of Manufacturing Processes , v.88 , 2023 https://doi.org/10.1016/j.jmapro.2023.01.030 Citation Details
Samaha, Philip and El_Kalach, Fadi and Harik, Ramy "Leveraging the usage of blockchain toward trust-dominated manufacturing systems" Journal of Manufacturing Systems , v.77 , 2024 https://doi.org/10.1016/j.jmsy.2024.10.010 Citation Details
Wickramarachchi, Ruwan and Henson, Cory and Sheth, Amit "Knowledge-Based Entity Prediction for Improved Machine Perception in Autonomous Systems" IEEE Intelligent Systems , v.37 , 2022 https://doi.org/10.1109/MIS.2022.3181015 Citation Details
(Showing: 1 - 10 of 11)

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