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

About This Grant

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.