Prediction of specific energy consumption during face milling of steel

被引:1
|
作者
Prisco, Umberto [1 ]
Astarita, Antonello [1 ]
机构
[1] Univ Napoli Federico II, Dept Chem Mat & Prod Engn, Piazzale Tecchio 80, I-80125 Naples, Italy
关键词
Face; milling; specific; energy; power; consumption; thermal; softening; DRY MACHINING PARAMETERS; HIGH-PURITY GRAPHITE; POWER-CONSUMPTION; TOOL WEAR; OPTIMIZATION; MODEL;
D O I
10.1080/10426914.2023.2254370
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
A novel empirical equation is presented to evaluate the Specific Energy Consumption (SEC) in face milling of steel. The proposed approach introduces a corrective factor to estimate the softening of work materials due to thermal effects at large cutting velocity. An experimental campaign on AISI 304 was carried out to investigate the predictive capability of the proposed model. A complete design matrix varying cutting velocity, radial depth of cut and feed rate was performed while measuring the average power consumed as response variable. Experimental results show that SEC reaches a maximum with the increase in the cutting speed. It is demonstrated that the SEC reduction observed at high cutting speeds is due to thermal softening induced by the higher temperatures attained in the work material. The formula proposed aims at providing a simple and accurate expression to select the process parameters in an energy saving perspective, especially in high-speed milling.
引用
收藏
页码:711 / 719
页数:9
相关论文
共 50 条
  • [31] Investigating the Surface Roughness of Hardened Tool Steel (2379) during Face Milling Operation
    Phokobye, Solomon
    Desai, Dawood
    Tlhabadira, Isaac
    Sadiku, Rotimi
    Daniyan, Ilesanmi
    INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND ROBOTICS RESEARCH, 2022, 11 (01): : 22 - 30
  • [32] Form Error Prediction of Gearcases’ Face Milling
    J. V. Le Lan
    A. Larue
    P. Lorong
    G. Coffignal
    International Journal of Material Forming, 2008, 1 : 543 - 546
  • [33] Form Error Prediction of Gearcases' Face Milling
    Le Lan, J. V.
    Larue, A.
    Lorong, P.
    Coffignal, G.
    INTERNATIONAL JOURNAL OF MATERIAL FORMING, 2008, 1 (Suppl 1) : 543 - 546
  • [34] Energy consumption model for milling processes considering auxiliary load loss and its applications
    Wang, Qi
    Zhang, Dinghua
    Tang, Kai
    Zhang, Ying
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2019, 105 (10) : 4309 - 4323
  • [35] A mechanistic model of energy consumption in milling
    Asrai, Reza Imani
    Newman, Stephen T.
    Nassehi, Aydin
    INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2018, 56 (1-2) : 642 - 659
  • [36] Prediction Models for the Milling of Heat-Treated Beech Wood Based on the Consumption of Energy
    Koleda, Peter
    Cuchor, Tomas
    Koleda, Pavol
    Rajko, L'ubomir
    APPLIED SCIENCES-BASEL, 2024, 14 (20):
  • [37] Optimal Workpiece Orientation to Reduce the Energy Consumption of a Milling Process
    Campatelli, Gianni
    Scippa, Antonio
    Lorenzini, Lorenzo
    Sato, Ryuta
    INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING-GREEN TECHNOLOGY, 2015, 2 (01) : 5 - 13
  • [38] Specific cutting energy index (SCEI)-based process signature for high-performance milling of hardened steel
    Zhu, Zerun
    Peng, Fangyu
    Tang, Xiaowei
    Yan, Rong
    Li, Zepeng
    Chen, Chen
    Sun, Hao
    INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2019, 103 (1-4) : 1 - 13
  • [39] Study on the Power Consumption of Different Milling Modes and Number of Inserts in Face Milling Processes
    Meng, Leilei
    Zhang, Chaoyong
    Ren, Yaping
    Luo, Min
    Tian, Guangdong
    2017 13TH IEEE CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE), 2017, : 1475 - 1480
  • [40] Optimization and Prediction of Specific Energy Consumption in Ball-End Milling of Ti-6Al-4V Alloy Under MQL and Cryogenic Cooling/Lubrication Conditions
    Sasa Tesic
    Djordje Cica
    Stevo Borojevic
    Branislav Sredanovic
    Milan Zeljkovic
    Davorin Kramar
    Franci Pusavec
    International Journal of Precision Engineering and Manufacturing-Green Technology, 2022, 9 : 1427 - 1437