Author: jhersh8

Planning Meeting for the CDFI Draws Executives from Industry and the National Labs to Georgia Tech on September 20-21, 2922

Georgia Tech Hosted more than 20 industry executives at a 2-day planning meeting for the Center for Digital Factory Innovations (CDFI)—a partnership of the University of New Hampshire, the University of North Carolina at Charlotte and Georgia Tech. The planning meeting showcased the Center’s research portfolio to prospective industry and national lab partners.  The mission of CDFI is to coalesce expertise and innovations from manufacturing and artificial intelligence, along with networking and communications, to speed adoption of the digital factory by industry partners and boost economic growth across the manufacturing sector.

Manufacturing companies, both large and small, face common challenges in digitizing their factory operations. During the meeting participants from across the country discussed the status of digitalization in their companies, shared their research ideas, and brainstormed how to address unmet industry needs. The event was supported by an IUCRC planning grant from the National Science Foundation awarded to principal investigator Professor Nicholas Kirsch at UNH, along with co-principal investigators Professor Christopher Saldana at GT and Professor Noel Greis at UNCC. NSF created the IUCRC program with the goal of generating breakthrough research through engagement among industry, researchers at leading universities and government agencies.

After a welcome by Co-PI host Saldana and an introduction to the IUCRC program by NSF program director Crystal Leach, PI Kirsch and Co-PI Greis kicked off the meeting with an overview of the CDFI mission and goals.  Ten research presentations spanning the four CDFI research thrust areas were presented—self-aware processes, augmented AI, protocols and interoperability, and self-organizing systems.  Specific projects ranged from applications of machine learning for in-process machine monitoring to building an ecosystem of collaborative digital twins for manufacturing control to the development of shared control between robot and operator for shop floor operations.

Active discussions among the CDFI team and potential industry partners helped to ensure that CDFI research not only focuses on broad, cutting-edge, and high-impact challenges but also responds directly to industry needs. Completion of the planning meeting sets the stage for a December proposal to NSF for Phase I funding.  Phase I funding is the final step in establishing CDFI as a robust and sustainable center of digital factory excellence that will deliver economic, quality, and technical advantages to industry and serve as a collaborative forum for academic and industrial researchers.

The project presentations can be viewed by clicking here.

Digital Twins Team Up at UNC Charlotte — A Collaborative Digital Twin Ecosystem for Manufacturing Control

The promise of digital twins has been their ability to test and validate a process or product before it even exists in the real world—and thus reduce engineering errors, inefficiencies, and costs.  While digital twins are still in their infancy, they have already demonstrated benefits in applications ranging from fault prediction for machinery on the factory floor to performance optimization of wind turbines.  The extension of digital twins to manufacturing control across multiple machines on the factory floor, or to life cycle management of a complex system, presents new challenges.  The question this research addresses is how to enable a collaboration of digital twins so that they can communicate with each other and share relevant data in order to make even better decisions in extended applications.

A digital twin typically comprises the semantic description and modeling of a single asset—for example a machine or robot—in order to simulate its performance virtually.  A collaboration of digital twins would require the sharing of information among a group of digital twins—for example a cell of turning machines and tending robots.  Collaborative digital twins could also model cross-company decision-making that includes suppliers, vendors and customers.  This research explores the creation of a cloud-based and learning-enabled collaborative ecosystem of digital twins to achieve even broader information exchange and benefits.

While there are many challenges in building a collaboration of digital twins, one of the most immediate is the creation of an appropriate data management framework—to include multiple data models and the communication protocols among them.  Currently, real-time data acquisition and transmission technology is focused on interactions between a single physical asset and its digital representation. However, feedback control in a shared environment of multiple twins and real-time exchange and analysis remain a challenge.  Other concerns include privacy, security, transparency, and ownership of these data models. Specifically, this research develops a collaborative data management framework for testing strategies of digital twin collaboration for manufacturing control.

PI.  Noel Greis, Mechanical Engineering and Engineering Science, University of North Carolina at Charlotte