Fog-enabled architecture for data-driven cyber-manufacturing systems

Dazhong Wu, Janis Terpenny, Li Zhang, Robert Gao, Thomas Kurfess

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

Over the past few decades, both small- and medium-sized manufacturers as well as large original equipment manufacturers (OEMs) have been faced with an increasing need for low cost and scalable intelligent manufacturing machines. Capabilities are needed for collecting and processing large volumes of real-time data generated from manufacturing machines and processes as well as for diagnosing the root cause of identified defects, predicting their progression, and forecasting maintenance actions proactively to minimize unexpected machine down times. Although cloud computing enables ubiquitous and instant remote access to scalable information and communication technology (ICT) infrastructures and high volume data storage, it has limitations in latency-sensitive applications such as high performance computing and real-time stream analytics. The emergence of fog computing, Internet of Things (IoT), and cyber-physical systems (CPS) represent radical changes in the way sensing systems, along with ICT infrastructures, collect and analyze large volumes of real-time data streams in geographically distributed environments. Ultimately, such technological approaches enable machines to function as an agent that is capable of intelligent behaviors such as automatic fault and failure detection, self-diagnosis, and preventative maintenance scheduling. The objective of this research is to introduce a fogenabled architecture that consists of smart sensor networks, communication protocols, parallel machine learning software, and private and public clouds. The fog-enabled architecture will have the potential to enable large-scale, geographically distributed online machine and process monitoring, diagnosis, and prognosis that require low latency and high bandwidth in the context of data-driven cyber-manufacturing systems.

Original languageEnglish (US)
Title of host publicationMaterials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791849903
DOIs
StatePublished - 2016
EventASME 2016 11th International Manufacturing Science and Engineering Conference, MSEC 2016 - Blacksburg, United States
Duration: Jun 27 2016Jul 1 2016

Publication series

NameASME 2016 11th International Manufacturing Science and Engineering Conference, MSEC 2016
Volume2

Other

OtherASME 2016 11th International Manufacturing Science and Engineering Conference, MSEC 2016
CountryUnited States
CityBlacksburg
Period6/27/167/1/16

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

Fingerprint Dive into the research topics of 'Fog-enabled architecture for data-driven cyber-manufacturing systems'. Together they form a unique fingerprint.

Cite this