Cyber Physical System and Big Data enabled energy efficient machining optimisation

Y. C. Liang, X. Lu, W. D. Li, S. Wang

Research output: Contribution to journalArticlepeer-review

96 Citations (Scopus)
488 Downloads (Pure)


Due to increasingly customised manufacturing, unpredictable ambient working conditions in shop floors and stricter requirements on sustainability, it is challenging to achieve energy efficient optimisation for machining processes. This paper presents a novel Cyber Physical System (CPS) and Big Data enabled machining optimisation system to address the above challenge. The innovations and characteristics of the system include the following four aspects: (1) a novel process of “scheduling, monitoring/learning, rescheduling” is designed to enhance system adaptability during manufacturing lifecycles; (2) an innovative energy model to support energy efficient optimisation over manufacturing lifecycles is developed. The energy model, which is enabled by CPS, Big Data analytics and intelligent learning algorithms, considers dynamic and aging conditions of machine tool systems during manufacturing lifecycles; (3) an effective evolutional algorithm based on Fruit Fly Optimisation (FFO), is applied to generate an adaptive energy efficient schedule, and improve schedule when there are significantly varying working conditions and adjustments on the schedule are necessary (that is rescheduling); (4) the system has been successfully deployed into European machining companies to verify capabilities. According to the results, around 40% energy saving and 30% productivity improvement have been achieved in the companies. A practical case study presented in this paper demonstrates the effectiveness and great potential of applicability of the system in practice.

Original languageEnglish
Pages (from-to)46-62
Number of pages18
JournalJournal of Cleaner Production
Early online date20 Mar 2018
Publication statusPublished - 20 Jun 2018

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Cleaner Production Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Cleaner Production, [187,] (2018)] DOI: [ 10.1016/j.jclepro.2018.03.149]


  • Big data
  • Cyber physical system
  • Energy efficient machining
  • Scheduling optimisation

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Environmental Science(all)
  • Strategy and Management
  • Industrial and Manufacturing Engineering


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