IoT-Enabled Manufacturing Testbeds for Democratizing SM Knowledge, Technology and Innovation
Project Lead: Auburn University
Partners: Rayonier & Linde
Member % Cost Share: 50.3%
CESMII % Cost Share: 49.7%
Duration: 12 Months
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.
Deliverables/Outcomes/SM Marketplace
- Delivered Complete Smart Manufacturing Testbed Documentation Package
- Delivered Complete Smart Manufacturing Profiles
- Delivered complete interactive Jupyter Notebook modules covering the entire data science life cycle
- Raw data, video and documentation packages are available for other CESMII members training and research needs
- Low-cost, noninvasive sensing system documentation package
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”.
