Big Data enabled Intelligent Immune System for energy efficient manufacturing management

Sheng Wang, Yuchen Liang, Weidong Li, Xiantao Cai

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    57 Citations (Scopus)
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    The Big Data driven approach has become a new trend for manufacturing optimisation. In this paper, an innovative Big Data enabled Intelligent Immune System (I2S) has been developed to monitor, analyse and optimise machining processes over lifecycles in order to achieve energy efficient manufacturing. There are two major functions in I2S: (1) an Artificial Neural Networks (ANNs)-based algorithm and statistical analysis tools are used to identify the abnormal electricity consumption patterns of manufactured components from monitored Big Data. An intelligent immune mechanism is devised to adapt to the condition changes and process dynamics of machining systems; (2) a re-scheduling algorithm is triggered if abnormal manufacturing conditions are detected thereby achieving multi-objective optimisation in terms of energy consumption and manufacturing performance. In this research, Computer Numerical Controlled (CNC) machining processes and industrial case studies have been used for system validation. The novelty of I2S is that Big Data analytics and intelligent immune mechanisms have been integrated systematically to achieve condition monitoring, analysis and energy efficient optimisation over manufacturing execution lifecycles. The applicability of the system has been validated by multiple industrial trials in European factories. Around 30% energy saving and over 50% productivity improvement have been achieved by adopting I2S in the factories.
    Original languageEnglish
    Pages (from-to)507-520
    Number of pages13
    JournalJournal of Cleaner Production
    Early online date29 May 2018
    Publication statusPublished - 10 Sept 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, [195, (2018)] DOI: 10.1016/j.jclepro.2018.05.203

    © 2018, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International


    • Big Data
    • CNC machining
    • Energy efficient manufacturing
    • Intelligent immune mechanism


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