PROJECT LEAD: Auburn University

PARTNERS: Rayonier, Linde

PROBLEM STATEMENT: There is a lack of interactive hands-on SM training materials based on real SM data and applications, which presents a challenge to advancing CESMII’s goal of democratizing SM knowledge, technology and innovation.

PROJECT GOAL: To establish and validate a novel model of SM training, including content creation, delivery and evaluation. Specifically, a suite of interactive SM training modules using real data and applications will be developed. In addition, we propose a metacognitive awareness gain (MAG) metric for evaluating the effectiveness of the SM training modules.

TECHNICAL APPROACH: The team leverages on two Internet-of-Things (IoT) enabled SM testbeds (SMTT) previously developed at Auburn, which provide real data and applications for SM training modules. We propose to develop data-enabled engineering project (DEEP) based SM (DEEP-SM) modules on Jupyter Notebook platform and integrated into the SM PlatformTM to train future and current SM workforces, respectively.

KEY TASKS AND MILESTONES:

  • Complete planned experiments and collect all data ​
  • All DEEP-SM Jupyter Notebook modules delivered to CESMII ​
  • Complete SM Profiles on SM Platform for the two SMTTs ​
  • Complete DEEP-SM modules on SM Platform ​
  • A Metacognitive Awareness Inventory (MAI) questionnaire for SM ​
POTENTIAL IMPACT:

  • The Jupyter SM training modules can be easily incorporated into existing education programs, therefore significantly advance the preparation of future SM workforce with new SM skill set and expertise;
  • The SM Platform training modules with real industrial applications can be easily adopted by different member organizations for re-training of current SM workforce.

BENEFITS:

  • A validated effective model for SM training, including content creation, delivery and evaluation;
  • The SMTTs and their associated applications are not commercial but rather educational, which allow full access to all CESMII members for adaptation and generalization to their specific applications;
  • The open source DEEP-SM Jupyter Notebook modules make them easily adoptable by other universities, which will help CESMII achieve the goal of “increasing the SM workforce capability by five-fold by 2030”.​​​
Member % Cost Share CESMII % Cost Share Duration
50.3% 49.7% 12 Months

 

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Headquarters: Los Angeles, California
(888) 720-8096 | info@cesmii.org