Condition-based Real-time Production Control for Smart Manufacturing Systems

Feifan Wang, Yan Lu, Feng Ju

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

3 Scopus citations

Abstract

In this paper, we present condition-based real-time production control for smart manufacturing which is aimed at improving system performance by automatically assessing a production system's condition and dynamically configuring the processing routes for smart products and parts. A ma-chine's degradation condition is defined in discrete states and modeled as a Markov chain. By taking into account machines' degradation and buffers' occupancy, an optimization problem is formulated to maximize the production rate using Markov Decision Processes. The effectiveness of the method has been demonstrated on a three-machine flexible production system. Traditionally, condition monitoring and production control are designed, developed, installed and managed separately by different domain experts. Hence, in this paper, the implementation challenges of condition-based production control are also discussed, with the existing and missing enabling standards identified and analyzed.

Original languageEnglish (US)
Title of host publication2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018
PublisherIEEE Computer Society
Pages1052-1057
Number of pages6
Volume2018-August
ISBN (Electronic)9781538635933
DOIs
StatePublished - Dec 4 2018
Event14th IEEE International Conference on Automation Science and Engineering, CASE 2018 - Munich, Germany
Duration: Aug 20 2018Aug 24 2018

Other

Other14th IEEE International Conference on Automation Science and Engineering, CASE 2018
CountryGermany
CityMunich
Period8/20/188/24/18

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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