Toward Energy Footprint Reduction of a Machining Process

被引:10
作者
Chen, Xingzheng [1 ]
Li, Congbo [2 ]
Yang, Qingshan [2 ]
Tang, Ying [3 ]
Li, Lingling [1 ]
Zhao, Xikun [2 ]
机构
[1] Southwest Univ, Coll Engn & Technol, Chongqing 400715, Peoples R China
[2] Chongqing Univ, State Key Lab Mech Transmiss, Chongqing 400044, Peoples R China
[3] Rowan Univ, Dept Elect & Comp Engn, Glassboro, NJ 08028 USA
基金
中国国家自然科学基金;
关键词
Machining; Optimization; Energy consumption; Process planning; Cutting tools; Machine tools; Tools; Cutting parameter optimization; energy footprint; machining process; process planning; CUTTING PARAMETERS; MULTIOBJECTIVE OPTIMIZATION; CONSUMPTION; EFFICIENCY; SELECTION; TAGUCHI; TOOL; SYSTEM;
D O I
10.1109/TASE.2021.3062648
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In a machining process, proper selection of process plans and cutting parameters can effectively reduce energy consumption and shorten production time. Traditionally, studies on process planning and cutting parameter optimization for energy saving are mostly concentrated on electrical energy consumption. Since the preparation process of cutting tools and cutting fluid consumes a considerable amount of energy, conservation of this part of energy consumption, namely, the embodied energy consumption, will achieve a more energy-efficient machining process. In this article, an integrated model for process planning and cutting parameter optimization is proposed to shorten production time and reduce the energy footprint (namely, electrical energy consumption and embodied energy consumption of cutting tools and cutting fluid) of a machining process. Considering that the optimization of process plan and cutting parameters in an integrated manner is a hybrid programming process, simulated annealing and quantum-behaved particle swarm optimization (SA-QPSO) hybrid algorithm is employed to solve the proposed model. Results of the case study show that: 1) embodied energy consumption of cutting tools and cutting fluid accounts for a nonnegligible proportion of energy footprint of the machining process and 2) there is a tradeoff between energy footprint and production time, and the balance of them is achieved through the proposed optimization approach.
引用
收藏
页码:772 / 787
页数:16
相关论文
共 46 条
[1]  
Ai X., 2002, BRIEF MANUAL CUTTING
[2]  
[Anonymous], 2018, KEY EN STAT 2018
[3]   Multi-objective optimization of cutting parameters to minimize power consumption in dry turning of stainless steel 316 [J].
Bagaber, Salem Abdullah ;
Yusoff, Ahmed Razlan .
JOURNAL OF CLEANER PRODUCTION, 2017, 157 :30-46
[4]   Energy centric selection of machining conditions for minimum cost [J].
Balogun, Vincent Aizebeoje ;
Edem, Isuamfon F. ;
Gu, Heng ;
Mativenga, Paul Tarisai .
ENERGY, 2018, 164 :655-663
[5]   A simulated annealing-based multiobjective optimization algorithm: AMOSA [J].
Bandyopadhyay, Sanghamitra ;
Saha, Sriparna ;
Maulik, Ujjwal ;
Deb, Kalyanmoy .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2008, 12 (03) :269-283
[6]   Optimization of energy consumption response parameters for turning operation using Taguchi method [J].
Bilga, Paramjit Singh ;
Singh, Sehijpal ;
Kumar, Raman .
JOURNAL OF CLEANER PRODUCTION, 2016, 137 :1406-1417
[7]   Optimization of cutting parameters using Response Surface Method for minimizing energy consumption and maximizing cutting quality in turning of AISI 6061 T6 aluminum [J].
Camposeco-Negrete, Carmita .
JOURNAL OF CLEANER PRODUCTION, 2015, 91 :109-117
[8]   Parameter optimization of multi-pass turning using chaotic PSO [J].
Chauhan, Pinkey ;
Pant, Millie ;
Deep, Kusum .
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2015, 6 (02) :319-337
[9]   Integrated optimization of cutting tool and cutting parameters in face milling for minimizing energy footprint and production time [J].
Chen, Xingzheng ;
Li, Congbo ;
Tang, Ying ;
Li, Li ;
Du, Yanbin ;
Li, Lingling .
ENERGY, 2019, 175 :1021-1037
[10]   Optimization of cutting parameters with a sustainable consideration of electrical energy and embodied energy of materials [J].
Chen, Xingzheng ;
Li, Congbo ;
Jin, Yan ;
Li, Li .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2018, 96 (1-4) :775-788