IoT-Enabled Manufacturing Testbeds for Democratizing SM Knowledge, Technology and Innovation

ProjectPageThumbnails_Auburn, Linde, Rayonier

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”.