Modelling of carbon utilisation efficiency and its application in milling parameters optimisation

被引:17
作者
Deng, Zhaohui [1 ,2 ]
Lv, Lishu [1 ,2 ]
Huang, Wenliang [1 ,2 ]
Wan, Linlin [1 ,2 ]
Li, Shichun [1 ,2 ]
机构
[1] Hunan Univ Sci & Technol, Intelligent Mfg Inst HNUST, Xiangtan 411201, Peoples R China
[2] Hunan Univ Sci & Technol, Hunan Prov Key Lab High Efficiency & Precis Mach, Xiangtan 411201, Peoples R China
基金
中国国家自然科学基金;
关键词
Low carbon manufacturing; milling process; carbon utilisation efficiency; particle swarm optimisation; process parameters optimisation; CUTTING PARAMETERS; ENERGY-CONSUMPTION; GENETIC ALGORITHM; REDUCTION; EMISSION; SELECTION;
D O I
10.1080/00207543.2019.1633026
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
At present, the evaluation index for low carbon manufacturing was mainly focused on the relationship between carbon emission and economic benefits or removal volume. But there was rarely comprehensive evaluation index to evaluate the carbon utilisation level of processing. Based on this, this paper presented the carbon utilisation efficiency as the comprehensive evaluation index in low carbon manufacturing. The carbon utilisation efficiency was defined as the ratio of the carbon emission of materials removal to the whole carbon emission in manufacturing process. A carbon utilisation efficiency model was established in milling process, based on flow characteristics and removal mechanism of carbon emission during milling. Then a multi-objective optimisation model was established based on Particle Swarm Optimisation, and the minimum processing time and high carbon utilisation efficiency were set as the optimisation objectives. And the experiment was performed to confirm the undetermined constant in the optimisation model and verify the effectiveness of the optimisation model. The optimised milling parameters were verified to reduce the processing time and improve the carbon utilisation efficiency.
引用
收藏
页码:2406 / 2420
页数:15
相关论文
共 35 条
[1]   Modelling and optimization of energy consumption for feature based milling [J].
Altintas, Resul Sercan ;
Kahya, Muge ;
Unver, Hakki Ozgur .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2016, 86 (9-12) :3345-3363
[2]   Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method [J].
Asilturk, Ilhan ;
Cunkas, Mehmet .
EXPERT SYSTEMS WITH APPLICATIONS, 2011, 38 (05) :5826-5832
[3]   A mechanistic model of energy consumption in milling [J].
Asrai, Reza Imani ;
Newman, Stephen T. ;
Nassehi, Aydin .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2018, 56 (1-2) :642-659
[4]   Optimization of feedrate in a face milling operation using a surface roughness model [J].
Baek, DK ;
Ko, TJ ;
Kim, HS .
INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, 2001, 41 (03) :451-462
[5]   Optimisation approach to target costing under uncertainty with application to ICT-service [J].
Becker, Denis M. ;
Gaivoronski, Alexei A. .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2018, 56 (05) :1904-1917
[6]   Optimization of cutting parameters to minimize energy consumption during turning of AISI 1018 steel at constant material removal rate using robust design [J].
Camposeco-Negrete, Carmita ;
Calderon Najera, Juan de Dios ;
Carlos Miranda-Valenzuela, Jose .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2016, 83 (5-8) :1341-1347
[7]  
Cao Hongrui, 2013, Journal of Mechanical Engineering, V49, P161, DOI 10.3901/JME.2013.05.161
[8]   Simulation-based approach to modeling the carbon emissions dynamic characteristics of manufacturing system considering disturbances [J].
Cao, Huajun ;
Li, Hongcheng .
JOURNAL OF CLEANER PRODUCTION, 2014, 64 :572-580
[9]   Genetic algorithm-based optimization of cutting parameters in turning processes [J].
D'Addona, Doriana M. ;
Teti, Roberto .
FORTY SIXTH CIRP CONFERENCE ON MANUFACTURING SYSTEMS 2013, 2013, 7 :323-328
[10]  
Dahmus J.B., 2004, ASME INT MECH ENG C, DOI [DOI 10.1115/IMECE2004-62600, 10.1115/IMECE2004-62600]