Energy-oriented joint optimization of machine maintenance and tool replacement in sustainable manufacturing

被引:38
|
作者
Xia, Tangbin [1 ]
Shi, Guo [1 ]
Si, Guojin [1 ]
Du, Shichang [1 ]
Xi, Lifeng [1 ]
机构
[1] Shanghai Jiao Tong Univ, SJTU Fraunhofer Ctr, Sch Mech Engn, State Key Lab Mech Syst & Vibrat, Shanghai 200240, Peoples R China
基金
上海市自然科学基金; 中国国家自然科学基金;
关键词
Sustainable manufacturing; Energy consumption mechanism; Joint maintenance opportunity; Tool wear; Preventive replacement; POWER-CONSUMPTION; HEALTH MANAGEMENT; CARBON TAX; WEAR; MODEL; PREDICTION;
D O I
10.1016/j.jmsy.2021.01.015
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
With the increasing attention on sustainable manufacturing, operation and maintenance (O&M) management focuses on not only budget limit, but also energy saving. For modern CNC systems, besides the energy consumption to operate and maintain the machine, a majority of energy consumption generated from tool wear should be considered. It means both machine degradation and tool wear are required to be modelled for the global saving energy. Thus, this paper proposes an energy-oriented joint optimization of machine maintenance and tool replacement (EJMR) policy by integrating energy consumption mechanisms and joint maintenance opportunities in a machine-tool system. The key issue is to combine the preventive maintenance (PM) scheduling of the machine and the polish/preventive replacement (PR) optimization of sequential tools to form energyeffective schemes. Therefore, joint maintenance opportunities of PM actions are utilized to perform tool polish/PR based on energy consumption mechanisms. Four successive procedures (energy consumption analysis, energy-oriented PM scheduling, machine-tool PR model and integrated decision-making process) are developed. Thereby optimal intervals of machine PM and tool polish/PR are obtained to save energy. The case study illustrates that compared with conventional maintenance policies, this proposed EJMR policy can significantly reduce the total non-value-added energy consumption (TNVE) in sustainable manufacturing.
引用
收藏
页码:261 / 271
页数:11
相关论文
共 34 条
  • [21] Machining process parameters optimization for heavy-duty CNC machine tools in sustainable manufacturing
    Xiong, Yao
    Wu, Jun
    Deng, Chao
    Wang, Yuanhang
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2016, 87 (5-8) : 1237 - 1246
  • [22] Joint optimization of preventive maintenance and production rescheduling with new machine insertion and processing speed selection
    An, Youjun
    Chen, Xiaohui
    Hu, Jiawen
    Zhang, Lin
    Li, Yinghe
    Jiang, Junwei
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2022, 220
  • [23] Structural design optimization of moving component in CNC machine tool for energy saving
    Ji, Qianqian
    Li, Congbo
    Zhu, Daoguang
    Jin, Yan
    Lv, Yan
    He, Jixiang
    JOURNAL OF CLEANER PRODUCTION, 2020, 246 (246)
  • [24] Genetic Optimization for the Design of a Machine Tool Slide Table for Reduced Energy Consumption
    Triebe, Matthew J.
    Zhao, Fu
    Sutherland, John W.
    JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME, 2021, 143 (10):
  • [25] Modeling and Multi-objective Optimization Method of Machine Tool Energy Consumption Considering Tool Wear
    Bo Li
    Xitian Tian
    Min Zhang
    International Journal of Precision Engineering and Manufacturing-Green Technology, 2022, 9 : 127 - 141
  • [26] A machine learning framework for explainable knowledge mining and production, maintenance, and quality control optimization in flexible circular manufacturing systems
    Koulinas, Georgios K.
    Paraschos, Panagiotis D.
    Koulouriotis, Dimitrios E.
    FLEXIBLE SERVICES AND MANUFACTURING JOURNAL, 2024, 36 (03) : 737 - 759
  • [27] Cutting Energy Consumption Modeling by Considering Tool Wear and Workpiece Material Properties for Multi-Objective Optimization of Machine Tools
    Meng, Yue
    Dong, Shengming
    Sun, Xinsheng
    Wei, Shiliang
    Liu, Xianli
    COATINGS, 2024, 14 (06)
  • [28] Energy Resources Intelligent Management using on line real-time simulation: A decision support tool for sustainable manufacturing
    Cassettari, Lucia
    Bendato, Ilaria
    Mosca, Marco
    Mosca, Roberto
    APPLIED ENERGY, 2017, 190 : 841 - 851
  • [29] Toward high-performance energy and power battery cells with machine learning-based optimization of electrode manufacturing
    Duquesnoy, Marc
    Liu, Chaoyue
    Kumar, Vishank
    Ayerbe, Elixabete
    Franco, Alejandro A.
    JOURNAL OF POWER SOURCES, 2024, 590
  • [30] Customer demand prediction of service-oriented manufacturing using the least square support vector machine optimized by particle swarm optimization algorithm
    Cao, Jin
    Jiang, Zhibin
    Wang, Kangzhou
    ENGINEERING OPTIMIZATION, 2017, 49 (07) : 1197 - 1210