Bingbing Li
Xiyi Hang
Mark R Rajai
Li Liu
Establishing Smart Connected Workers Infrastructure for Enabling Advanced Manufacturing: A Pathway to Implement Smart Manufacturing for Small to Medium Sized Enterprises (SMEs)
Project Type:
Project
Project Sponsors:
- UC Irvine
Project Award:
- $322,028
Project Timeline:
2019-04-01 – 2020-09-30
Lead Principal Investigator:
Project Team:
Principal Investigator |
Principal Investigator |
Principal Investigator |
The Project Goal of this proposed Smart Connected Workers program is to create affordable, scalable, accessible, and portable smart manufacturing systems (A.S.A.P. SM systems) through which advances in Internet of Things (IoT) technologies can be effectively integrated into mobile sensor platforms to augment the intelligence of workers and supervisors with smart manufacturing principles and methods. The project will leverage existing installed infrastructure, such as networked programmable logic controllers (PLCs) for equipment and Southern California Edison, advanced smart meters, to explore the use of affordable consumer grade hardware, scalable open source software, accessible intelligence from machine learning in cloud computing, and portable wireless solutions for implementation of A.S.A.P. SM. In this proposed project, UCI will partner with Atollogy, Aerospace Corporation, General Mills, Southern California Edison (SCE), CESMII system integrator resource providers, Google, San Diego Supercomputing Center (SDSC), The University of California Los Angeles (UCLA) and California State University Northridge (CSUN). The research, development and demonstration work will include: creation of an integrated energy consumption, life cycle and workflow assessment tool that intelligently combines data from existing advanced smart meters deployed in industrial facilities and manufacturing workflow characterization deployed by Atollogy; development of advanced wireless wearable sensor technologies for operations in environments prone to noise and electromagnetic interference (EMI); and development of workflow energy management systems based on distributed cognitive computing architectures.