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Center Resources

Access research publications, tools, datasets, and educational materials from the Digital Twin Center.

Project Resources

Reports

Digital Twins and AI in Semiconductor Manufacturing

Comprehensive report on the applications of digital twin technologies and artificial intelligence in semiconductor manufacturing processes.

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Artificial Intelligence to Advance High-Mix Production: A Roadmap for the Semiconductor Industry

Roadmap outlining how artificial intelligence can advance high-mix production in the semiconductor industry.

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Digital Twin Framework Keynote

Keynote presentation covering the digital twin framework development and implementation strategies.

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Advanced Process Control - Smart Manufacturing

Presentation on advanced process control in smart manufacturing (October 2024).

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Publications & Research

A Requirements Driven Digital Twin Framework: Specification and Opportunities

J. Moyne, Y. Qamsane, E. C. Balta, I. Kovalenko, J. Faris, K. Barton, and D. M. Tilbury

A set of requirements for a Digital Twin (DT) framework has been derived from analysis of DT definitions, DTs in use today, expected DT applications in the near future, and longer-term DT trends and the DT vision in Smart Manufacturing. These requirements include elements of re-usability, interoperability, interchangeability, maintainability, extensibility, and autonomy across the entire DT lifecycle.

IEEE Access 8:107781-107801, June 2020 View Paper (opens in new tab)

A Methodology to Develop and Implement Digital Twin Solutions for Manufacturing Systems

Y. Qamsane, J. Moyne, M. Toothman, I. Kovalenko, E. C. Balta, J. Faris, D. M. Tilbury, and K. Barton

This paper introduces a Digital Twin (DT) solution development methodology as a generic procedure for analyzing and developing DTs for manufacturing systems. A case study illustrates the advantages of the proposed methodology in supporting manufacturing DT solutions.

IEEE Access 9(44247 - 44265), March 2021 View Paper (opens in new tab)

An Adaptive, State-Based Framework for Fault Prediction in Rotating Equipment

M. Toothman, B. Braun, S. J. Bury, J. Moyne, D. M. Tilbury, K. Barton

Advanced framework for predictive maintenance using adaptive state-based models for rotating manufacturing equipment.

IEEE CASEJoint work with Dow Auckland, NZ, August 2023 View Paper (opens in new tab)

Digital-Twin based Cyber-Attack Detection Framework for Cyber-Physical Manufacturing Systems

E. C. Balta, M. Pease, J. Moyne, K. Barton, and D. M. Tilbury

Novel cybersecurity framework leveraging digital twin technology for detecting and mitigating cyber attacks in manufacturing systems.

IEEE Trans. on ASEJoint work with NIST to appear, 2023 View Paper (opens in new tab)

Full Stack Virtual Commissioning: Requirements Framework to Bridge Gaps in Current Virtual Commissioning Process

J. B. Sim, K. N. Shah, M. Saez, J. Abell, Y. Zhou, J. Faris, D. M. Tilbury, and K. Barton

Comprehensive approach to virtual commissioning using full-stack digital twin implementations for manufacturing systems.

ASME MSECJoint work with General Motors Rutgers, NJ, June 2023 View Paper (opens in new tab)

A Digital Twin Framework for Mechanical System Health State Estimation

M. Toothman, B. Braun, S. J. Bury, M. Dessauer, K. Henderson, S. Phillips, Y. Yixin, D. M. Tilbury, J. Moyne, K. Barton

Digital twin framework for estimating mechanical system health states in manufacturing environments.

MECCJoint work with Dow Austin, TX, October 2021 View Paper (opens in new tab)

An Integrated Framework for Dynamic Manufacturing Planning to Obtain New Line Configurations

L. Poudel, I. Kovalenko, R. Geng, M. Takaharu, Y. Nonaka, N. Takahiro, U. Shota, D. M. Tilbury, and K. Barton

Integrated framework for dynamic manufacturing planning to achieve new production line configurations using digital twin technologies.

IEEE CASEJoint work with Hitachi Mexico City, August 2022 View Paper (opens in new tab)

A Digital Twin Framework for Performance Monitoring and Anomaly Detection in Fused Deposition Modeling

E. Balta, D. Tilbury, and K. Barton

Digital twin framework for performance monitoring and anomaly detection in additive manufacturing processes, specifically fused deposition modeling.

IEEE CASE Vancouver, August 2019 View Paper (opens in new tab)

Real-time manufacturing machine and system performance monitoring using Internet of Things

Miguel Saez, Francisco Maturana, Kira Barton, and Dawn Tilbury

Real-time performance monitoring system for manufacturing machines and systems using Internet of Things technologies and digital twin principles.

IEEE Trans. on ASEJoint work with Rockwell Automation 15(4):1735-1748, October 2018 View Paper (opens in new tab)

Digital Twin Framework for Reconfiguration Management: Concept & Evaluation

B. Caesar, K. Barton, D. M. Tilbury, A. Fay

Digital twin framework for managing reconfiguration in manufacturing systems, including concept development and evaluation methodologies.

IEEE Access vol. 11, pp. 127364-127387, 2023 View Paper (opens in new tab)

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