Smart connected worker edge platform for smart manufacturing: Part 1—Architecture and platform design
[1];
[3];
- Calit2 University of California Irvine Irvine California USA
- EECS University of California Irvine Irvine California USA
- Department of Mathematics University of California Los Angeles Los Angeles California USA
- Autonomy Research Center for STEAHM California State University Northridge Los Angeles California USA
- San Diego Supercomputer Center University of California San Diego La Jolla California USA
- Engineering and Technology Group The Aerospace Corporation El Segundo California USA
- Manufacturing Systems Engineering and Management California State University Northridge Los Angeles California USA
Abstract The challenge of sustainably producing goods and services for healthy living on a healthy planet requires simultaneous consideration of economic, societal, and environmental dimensions in manufacturing. Enabling technology for data driven manufacturing paradigms like Smart Manufacturing (a.k.a. Industry 4.0) serve as the technological backbone from which sustainable approaches to manufacturing can be implemented. Unfortunately, these technologies are typically associated with broader and deeper factory automation that is often too expensive and complex for the small and medium sized manufacturers (SMMs) that comprise the majority of manufacturing business in the USA and for whom their most valuable asset are the people whose jobs automation while replace. This paper describes an edge intelligent platform to integrate internet‐of‐things technologies with computing hardware, software, computational workflows for machine learning, and data ingestion, enabling SMMs to transition into smart manufacturing paradigms by leveraging the intelligence of their people. The platform leverages consumer grade electronics and sensors (affordable and portable), customized software with open source software packages (accessible), and existing communication network infrastructures (scalable). The software systems are implemented via Kubernetes orchestration of Docker containerization to ensure scalability and programmability. The platform is adaptive via computational workflow engines that produce information from data by processing with low‐cost edge computing devices while efficiently accessing resources of cloud servers as needed. The proposed edge platform connects workers to technological resources that provide computational intelligence (i.e., silicon‐based sensing and computation for data collection and contextualization) to enable decision making at the edge of advanced manufacturing.
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- EE0007613
- OSTI ID:
- 1872233
- Journal Information:
- Journal of Advanced Manufacturing and Processing, Journal Name: Journal of Advanced Manufacturing and Processing Journal Issue: 4 Vol. 4; ISSN 2637-403X
- Publisher:
- Wiley Blackwell (John Wiley & Sons)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
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