Current Research Projects

Research projects across the Center for Digital Twins in Manufacturing

Digital Twins for Worker Assistance in Flexible Manufacturing

PI: Wenlong Zhang; Co-PI: Dawn Tilbury
This project will develop human digital twins with uncertainty quantification and integrate it with a process digital twin for automated worker assistance.
- Problem: Humans remain an integral part in manufacturing, but they are not perfect. Robots can help, but need information on how to best to work with/help humans.
- Opportunity: Human Digital Twin is a digital representation of the twinned human, emulating their state and dynamics.
- Project Focus Points: Human behavior and action prediction; group conventions and individual variance; error detection

PI: Kira Barton; Co-PI: Wenlong Zhang; Graduate Student: Ali Bahrami
This project will investigate the use of foundational models with more traditional models within a digital twin framework. Data will be integrated across the design for manufacturing pipeline to identify potential failures and key system correlations for enhanced system design.
- Problem: Manufacturing systems need a unified digital framework that can use historical data, improve prediction, and support system-level process design and evaluation.
- Opportunity: Foundation models can connect design intent, process knowledge, and operational data for scalable manufacturing decision support.
- Project Focus Points: Training Methodology, Data Requirements, Validation and Evaluation

PI: Zhengtao Gan; Co-PI: Kira Barton; Graduate Student: Mehrdad Zomorodiyan
This project will deliver a physics-informed digital twin that enables inter-layer defect detection for certifiable metal additive manufacturing.
- Problem: Manufacturing processes lack robust digital twins capable of detecting, predicting, and ultimately preventing defects.
- Opportunity: This project establishes a framework for physics-informed learning with the model updating to develop robust digital twins for manufacturing defect detection and prediction.
- Project Focus Points: Sensing observability, Surrogate updating, Demonstration

PI: Dawn Tilbury; Co-PI: Giulia Pedrielli; Graduate Student: Tong Chen
This project will demonstrate a methodology for synchronizing a robotic digital twin with its physical counterpart in manufacturing.
- Problem: Digital Twins inevitably drift from the physical systems they mirror, yet current practice flags every deviation against fixed, hand-set tolerances - unable to separate normal run-to-run variation from meaningful change, or to tell whether the cause is the model, the environment, or a fault.
- Opportunity: By streaming live sensor data into a purpose-scoped twin and running parallel counterfactual models, a diagnostic twin can not only detect divergence but explain it - automatically deciding whether to recalibrate the model, reinterpret the environment, or flag a fault - turning digital-twin maintenance from a manual, reactive step into a continuous, self-diagnosing capability for manufacturing.
- Project Focus Points: Synchronization Methodology & Metrics, Noise-Floor/Minimum Detectable Deviation, Counterfactual Source Attribution, Time-Aligned Physical–Simulation

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