Specific energy consumption prediction model of CNC machine tools based on tool wear

被引:12
|
作者
Zhao, Guoyong [1 ]
Li, Chunxiao [1 ]
Lv, Zhe [1 ]
Cheng, Xiang [1 ]
Zheng, Guangming [1 ]
机构
[1] Shandong Univ Technol, Inst Adv Mfg, Zibo, Peoples R China
关键词
Processing parameters; tool wear; cutting power; specific energy consumption; prediction accuracy; POWER-CONSUMPTION; MATERIAL REMOVAL; REQUIREMENTS; OPTIMIZATION; PARAMETERS; EFFICIENCY;
D O I
10.1080/0951192X.2020.1718763
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The global energy crisis and climate warming are becoming more and more serious. How to reduce energy consumption and environmental pollution to achieve low-carbon manufacturing is an urgent problem to solve. Tool wear leads to the increase of cutting force and cutting power of machine tools obviously. So the influence of tool wear on energy consumption of machine tools cannot be ignored. Firstly, the power of CNC machine tool in cutting stage is divided into standby power, cutting material power and spindle no-load power in the paper. Then, the specific energy consumption prediction model of CNC machine tools based on tool wear is developed. Furthermore, the proposed model is verified with dry milling 45# steel experiments, and the prediction accuracy can reach 98.2% according to material removal rate, spindle speed and tool wear. Finally, the influence of processing parameters and tool wear on specific energy consumption of machine tools is studied. The research is helpful to optimize the processing parameters and tool conditions to reduce the energy consumption of machine tools.
引用
收藏
页码:159 / 168
页数:10
相关论文
共 50 条
  • [1] Prediction models for specific energy consumption of machine tools and surface roughness based on cutting parameters and tool wear
    Su, Yu
    Li, Congbo
    Zhao, Guoyong
    Li, Chunxiao
    Zhao, Guangxi
    [J]. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE, 2021, 235 (6-7) : 1225 - 1234
  • [2] Electrical energy consumption of CNC machine tools based on empirical modeling
    Jiang, Zhipeng
    Gao, Dong
    Lu, Yong
    Kong, Linghao
    Shang, Zhendong
    [J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2019, 100 (9-12): : 2255 - 2267
  • [3] Review on Design Research in CNC Machine Tools Based on Energy Consumption
    Wu, Hongyi
    Wang, Xuanyi
    Deng, Xiaolei
    Shen, Hongyao
    Yao, Xinhua
    [J]. SUSTAINABILITY, 2024, 16 (02)
  • [4] Electrical energy consumption of CNC machine tools based on empirical modeling
    Zhipeng Jiang
    Dong Gao
    Yong Lu
    Linghao Kong
    Zhendong Shang
    [J]. The International Journal of Advanced Manufacturing Technology, 2019, 100 : 2255 - 2267
  • [5] Prediction and analysis of energy consumption of cnc machine tools and research on key energy saving technologies
    Wu, Xinzhu
    [J]. Academic Journal of Manufacturing Engineering, 2019, 17 (04): : 123 - 131
  • [6] A practical energy consumption prediction method for CNC machine tools: cases of its implementation
    Shen, Nanyan
    Cao, Yanling
    Li, Jing
    Zhu, Kai
    Zhao, Chen
    [J]. INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2018, 99 (9-12): : 2915 - 2927
  • [7] A practical energy consumption prediction method for CNC machine tools: cases of its implementation
    Nanyan Shen
    Yanling Cao
    Jing Li
    Kai Zhu
    Chen Zhao
    [J]. The International Journal of Advanced Manufacturing Technology, 2018, 99 : 2915 - 2927
  • [8] Prediction of the CNC Tool Wear Using the Machine Learning Technique
    Lee, Kangbae
    Park, Sungho
    Sung, Sangha
    Park, Domyeong
    [J]. 2019 6TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE (CSCI 2019), 2019, : 296 - 299
  • [9] Application of a Hybrid Improved Particle Swarm Algorithm for Prediction of Cutting Energy Consumption in CNC Machine Tools
    Du, Jidong
    Wang, Yan
    Ji, Zhicheng
    [J]. INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, 2024, 22 (07) : 2327 - 2340
  • [10] Study on the Impact of Cutting Tool Wear on Machine Tool Energy Consumption
    Roszkowski, Andrzej
    Piorkowski, Pawel
    Skoczynski, Waclaw
    Borkowski, Wojciech
    Jankowski, Tomasz
    [J]. ADVANCES IN SCIENCE AND TECHNOLOGY-RESEARCH JOURNAL, 2020, 14 (03) : 158 - 164