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Center for Digital Twins in Manufacturing (CDTM)

Center Overview

A collaboration between the University of Michigan and Arizona State University, the Center is focused on developing digital twin (DT) solutions for manufacturing. The potential benefits of the Center's research are significant and expansive. Improvements in re-usability, extensibility, interoperability and maintainability of solutions will improve manufacturing throughput and quality and reduce cost directly. Key DT framework capabilities such as virtual commissioning will facilitate faster and lower-cost ramp-up, while cutting edge research will explore the ways in which AI and machine learning can accelerate the modeling, estimation and prediction capabilities of DTs. A common framework will allow the benefits to extend to the entire manufacturing ecosystem and will enhance capabilities such as security and customer responsiveness. Lastly, the Center will promote workforce development and empowerment by establishing environments and solutions for training (including the appropriate usage of AI/ML), benchmarking and collaboration between competitors, suppliers, and customers in a technical, pre-competitive forum.

Universities

University of Michigan

View Center Website

Center Personnel

Dr. Dawn Tilbury
Director and Ronald D. and Regina C. McNeil Department Chair of Robotics, University of Michigan

tilbury@umich.edu

Dr. Kira Barton
Site Director and Professor, Robotics and Mechanical Engineering, University of Michigan

bartonkl@umich.edu

Research Focus

Thrust 1: Digital Twin Frameworks and Standards

Creating standardized, interoperable digital twin frameworks that can be adopted across diverse manufacturing environments and industries, moving beyond siloed solutions.

Thrust 2: Digital Twin Applications

Developing practical applications of digital twin technology across various manufacturing sectors including automotive, aerospace, and other industrial domains. Artificial intelligence and machine learning are leveraged to create and update models within DTs, leading to improved estimation and prediction capabilities.

Thrust 3: Digital Twin Tools and Workforce Development

Creating advanced tools for digital twin implementation while training the next generation of engineers and researchers in digital twin technologies, including the use of AI/ML for modeling, estimation and prediction.

Awards

2436195

IUCRC Phase I University of Michigan: Center for Digital Twins in Manufacturing (CDTM)

The Industry-University Cooperative Research Center for Digital Twins in Manufacturing (IUCRC-DTM) will address fundamental research challenges for developing, deploying, maintaining, updating, and evaluating Digital Twins in manufacturing domains. A Digital Twin is a purpose-driven replica of some physical component, such as a machine or a process. A Digital Twin collects real-time data from its physical counterpart, and uses this data, together with one or more models, to estimate or make predictions about important metrics, such as system health and product quality. Using these estimates or predictions, decisions can be made to improve quality and cost-effectiveness through reduced downtime and wasted raw materials. For example, machines in a factory may degrade slowly, resulting in inferior product quality, but such degradations may not be noticed right away. Predictions from Digital Twins can be used to alert manufacturers to degrading health of their machines, before significant quality issues arise. The mission of the Center is to generate pre-competitive research outcomes for its members (and the manufacturing industry in general) towards the advancement of Digital Twin technology, and consolidate many current Digital Twin solutions around a common framework to advance extensibility, reusability, and interoperability of the solution components across different manufacturing settings. To empower the manufacturing workforce, materials will be developed for education, training, and workforce development. The IUCRC-DTM will initially propose three thrust areas. (1) Digital Twin Frameworks and Standards: development of a Digital Twin framework that is maintainable and extensible, incorporates existing solutions and solution threads, is reusable (within and across domains), and exhibits inheritance and generalization, interoperability and exploration into new domains. (2) Digital Twin Applications: improving key components of Digital Twins such as reconfiguration, composition, and human interfaces, as well as identification of key use cases of Digital Twins models in various manufacturing sectors. (3) Digital Twin Tools and Workforce Development: creation of software tools, workforce development materials/programs, and high-quality datasets to prepare future workforces and successfully deploy Digital Twins in the manufacturing industry. Through integration of research projects across these three thrusts, the Center seeks to advance several transformative concepts in Digital Twins, including novel knowledge representations, aggregation and composition methods for Digital Twins, and automatic generation and maintenance of Digital Twins. The University of Michigan Site will leverage the system-level manufacturing automation research testbed, with additive and subtractive processes, collaborative robots, and integrated data collection and visualization. The U-M affiliated faculty contribute expertise in manufacturing, robotics, control, uncertainty quantification, through relationships with the automotive, chemical, and aerospace industries. 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. Read More >

2435465

IUCRC Phase I Arizona State University: Center for Digital Twins in Manufacturing (CDTM)

The Industry-University Cooperative Research Center for Digital Twins in Manufacturing (IUCRC-DTM) will address fundamental research challenges for developing, deploying, maintaining, updating, and evaluating Digital Twins in manufacturing domains. A Digital Twin is a purpose-driven replica of some physical component, such as a machine or a process. A Digital Twin collects real-time data from its physical counterpart, and uses this data, together with one or more models, to estimate or make predictions about important metrics, such as system health and product quality. Using these estimates or predictions, decisions can be made to improve quality and cost-effectiveness through reduced downtime and wasted raw materials. For example, machines in a factory may degrade slowly, resulting in inferior product quality, but such degradations may not be noticed right away. Predictions from Digital Twins can be used to alert manufacturers to degrading health of their machines, before significant quality issues arise. The mission of the Center is to generate pre-competitive research outcomes for its members (and the manufacturing industry in general) towards the advancement of Digital Twin technology, and consolidate many current Digital Twin solutions around a common framework to advance extensibility, reusability, and interoperability of the solution components across different manufacturing settings. To empower the manufacturing workforce, materials will be developed for education, training, and workforce development. The IUCRC-DTM will initially propose three thrust areas. (1) Digital Twin Frameworks and Standards: development of a Digital Twin framework that is maintainable and extensible, incorporates existing solutions and solution threads, is reusable (within and across domains), and exhibits inheritance and generalization, interoperability and exploration into new domains. (2) Digital Twin Applications: improving key components of Digital Twins such as reconfiguration, aggregation, and human interfaces, as well as identification of key use cases of Digital Twins models in various manufacturing sectors. (3) Digital Twin Tools and Workforce Development: creation of software tools, workforce development materials/programs, and high-quality datasets to prepare future workforces and successfully deploy Digital Twins in the manufacturing industry. Through integration of research projects across these three thrusts, the Center seeks to advance several transformative concepts in Digital Twins, including novel knowledge representations, aggregation and composition methods for Digital Twins, and automatic generation and maintenance of Digital Twins. The Arizona State University (ASU) Site will leverage existing testbeds including a connected robotic manufacturing system and wire-laser metal deposition system. The ASU affiliated faculty brings in synergistic expertise on multi-physics simulation, stochastic modeling, robotics, and machine learning, with projects addressing the needs of aerospace, semiconductor, and energy sectors. 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. Read More >

Member Organizations

IUCRC affiliated member organizations are displayed as submitted by the Center. Non-federal organizations are not selected, approved, or otherwise endorsed by the U.S. National Science Foundation.

The opinions, findings, and conclusions or recommendations expressed are those of the Center author(s) and do not necessarily reflect the views of the U.S. National Science Foundation.