An improved cutting power-based model for evaluating total energy consumption in general end milling process

被引:46
作者
Shi, K. N. [1 ]
Ren, J. X. [1 ]
Wang, S. B. [2 ]
Liu, N. [3 ]
Liu, Z. M. [2 ]
Zhang, D. H. [1 ]
Lu, W. F. [3 ]
机构
[1] NPU, Minist Educ, Key Lab Contemporary Design & Integrated Mfg Tech, Xian 710072, Shaanxi, Peoples R China
[2] Chongqing Univ, Coll Mech Engn, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China
[3] Natl Univ Singapore, Dept Mech Engn, 9 Engn Dr 1, Singapore 117576, Singapore
基金
中国国家自然科学基金;
关键词
Energy efficiency; Energy consumption modelling; Cutting power; Slot milling; Partial-immersion milling; PREDICTION; PARAMETERS; FORCES; OPTIMIZATION; EFFICIENCY; REQUIREMENTS; VIBRATIONS; TAGUCHI; DEMAND;
D O I
10.1016/j.jclepro.2019.05.323
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Modern manufacturing enterprises are consuming a considerable amount of energy every year. Improving energy efficiency will not only benefit the enterprises economically, but also help the world to overcome various problems such as energy crisis and air pollution. To achieve this, an accurate energy consumption model is essential. The main objective of this paper is to develop an improved cutting power-based energy consumption model for general end milling process. The proposed model consists of an idle part due to auxiliary components and spindle rotation, and an additional part due to cutting workpiece materials. The first part is modelled as a function of spindle rotation speed, and the other part is considered proportional to the cutting power. Experiments under various milling conditions have demonstrated the effectiveness and efficacy of the proposed model. Comparative studies show that the proposed model is more accurate than other models. Although calibrated from slotting experiments when cutting aluminium alloy, the proposed model is applicable for general milling process. Partial immersion milling experiments show that the prediction error of the proposed model is as low as 1.74%. When workpiece material changes to titanium alloy, its performance remains decent, with low prediction error of 2.81%. This reveals its capability to provide reliable estimation for different workpiece materials. As such, it could help avoid tedious model calibration, thus saving time, material, and energy. Finally, the energy efficiency of general end milling process is investigated through numerical experiments with the proposed model. By revealing the relationship between energy consumption and various cutting parameters, the proposed model could serve as an excellent platform towards energy-efficient manufacturing/cleaner production. (C) 2019 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1330 / 1341
页数:12
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