A Hybrid Learning-based Predictive Process Planning Mechanism for Cyber-Physical Production Systems

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Cyber-Physical Production Systems (CPPS), which pursue the implementation of machine intelligence in manufacturing systems, receive much attention as an advanced technology in Smart Factories. CPPS significantly necessitates the self-learning capability because this capability enables manufacturing objects to foresee performance results during their process planning activities and thus to make data-driven autonomous and collaborative decisions. The present work designs and implements a self-learning factory mechanism, which performs predictive process planning for energy reduction in metal cutting industries based on a hybrid-learning approach. The hybrid-learning approach is designed to accommodate traditional machine-learning and transfer-learning, thereby providing the ability of predictive modeling in both data sufficient and insufficient environments. Those manufacturing objects are agentized under the paradigm of Holonic Manufacturing Systems to determine the best energy-efficient machine tool through their self-decisions and interactions without the intervention of humans’ decisions. For such purpose, this paper includes: the proposition of the hybrid-learning approach, the design of system architecture and operational procedure for the self-learning factory, and the implementation of a prototype system.

Original languageEnglish
Pages (from-to)391-400
Number of pages10
JournalJournal of the Korean Society for Precision Engineering
Issue number4
StatePublished - 2019 Apr


  • Cyber-physical production systems
  • Holonic manufacturing systems
  • Machine learning
  • Process planning
  • Self-learning factory
  • Transfer learning


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