Smart Connected Workers in Advanced Manufacturing

Project Lead: University of California Irvine
Partners: Atollogy, The Aerospace Corporation, General Mills, Honeywell, San Diego Supercomputer Center, California State University Northridge

Member % Cost Share: 32%
CESMII % Cost Share: 68%
Duration: 18 Months

Problem Statement

While large manufacturers have been able to capitalize on the tremendous advancements in digital technology, this digital revolution has bypassed many small to medium sized companies partly due to the high capital cost of transitioning from largely manual operations to a more automated environment.

Project Goal

The goal is to develop design tools and open source reference architectures that will enable engineers to reliably assess and implement data flow requirements for affordable, scalable, accessible, and portable smart manufacturing systems (A.S.A.P.) for smart connected workers across all manufacturing sectors.

Technical Approach

A Smart connected worker system (CWS) that utilizes vision based technology to characterize workflows that can be effectively combined with other sensor data; wireless mobile sensor system for electromagnetically noisy industrial environments; machine learning algorithms to model the correlated states of energy consumption and smart worker workflows; distributed computing environments for workflow optimization and process simulation; affordable, scalable infrastructures to create smart connected worker systems for SMEs.

Deliverables/Outcomes/SM Marketplace

  • Delivered Smart Connected Worker Platform Software

  • Delivered Smart Connected Worker Platform Hardware

  • Kepler Workflows for Electronic Component Manufacturing Processes

  • Edge Intelligence Platforms for Manufacturing Process Sensor Integration

Potential Impact

The proposed smart connected worker program seeks to impact the issue of bypassed advancements in digital technology in small to medium sized companies, by developing A.S.A.P. systems to enhance the intelligence of SME workers with information from which workflows can be analyzed and optimized for energy efficiency and waste stream reduction without the heavy infrastructure of PLC based automation and control.


  • Leveraging affordable open source compute resources that seamlessly connect through the SM Platform in order for SMEs to create and manage SCW systems
  • This also will help create custom human PLC control to improve energy efficiency and reduce waste streams
  • Allows for commercialization pathways for new devices, custom applications, and service based revenue models

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