Dual-objective program and improved artificial bee colony for the optimization of energy-conscious milling parameters subject to multiple constraints

Wenjie Wang; Guangdong Tian; Maoning Chen; Fei Tao; Chaoyong Zhang; Abdulraham AI-Ahmari; Zhiwu Li; Zhigang Jiang

doi:10.1016/j.jclepro.2019.118714

Dual-objective program and improved artificial bee colony for the optimization of energy-conscious milling parameters subject to multiple constraints

Wenjie Wang
, Guangdong Tian^*
, Maoning Chen
, Fei Tao
, Chaoyong Zhang
, Abdulraham AI-Ahmari
, Zhiwu Li
, Zhigang Jiang

^*Corresponding author for this work

International Institute for Interdisciplinary and Frontiers

Shandong University
Northeastern University China
Huazhong University of Science and Technology
King Saud University
Macau University of Science and Technology
Xidian University
Wuhan University of Science and Technology

Research output: Contribution to journal › Article › peer-review

94 Scopus citations

Abstract

Selecting a set of reasonable milling parameters of computerized numerical control (CNC) machines is of great importance in decreasing energy consumption and enhancing processing productivity. However, existing works pay little attention to the optimization of energy-conscious milling parameters. This work establishes a dual-objective optimization model for the selection of milling parameters such that power consumption and process time are minimized. With multiple constraints of milling processing conditions, an improved artificial bee colony (ABC) intelligent algorithm is used to handle the proposed dual-objective optimization model. Compared with the non-dominated sorting genetic algorithm (NSGA-II), our improved algorithm has good performance.

Original language	English
Article number	118714
Journal	Journal of Cleaner Production
Volume	245
DOIs	https://doi.org/10.1016/j.jclepro.2019.118714
State	Published - 1 Feb 2020

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Dual-objective optimization
Energy consumption model
Intelligent algorithm
Milling process model
Scheduling and optimization

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10.1016/j.jclepro.2019.118714

Cite this

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title = "Dual-objective program and improved artificial bee colony for the optimization of energy-conscious milling parameters subject to multiple constraints",

abstract = "Selecting a set of reasonable milling parameters of computerized numerical control (CNC) machines is of great importance in decreasing energy consumption and enhancing processing productivity. However, existing works pay little attention to the optimization of energy-conscious milling parameters. This work establishes a dual-objective optimization model for the selection of milling parameters such that power consumption and process time are minimized. With multiple constraints of milling processing conditions, an improved artificial bee colony (ABC) intelligent algorithm is used to handle the proposed dual-objective optimization model. Compared with the non-dominated sorting genetic algorithm (NSGA-II), our improved algorithm has good performance.",

keywords = "Dual-objective optimization, Energy consumption model, Intelligent algorithm, Milling process model, Scheduling and optimization",

author = "Wenjie Wang and Guangdong Tian and Maoning Chen and Fei Tao and Chaoyong Zhang and Abdulraham AI-Ahmari and Zhiwu Li and Zhigang Jiang",

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T1 - Dual-objective program and improved artificial bee colony for the optimization of energy-conscious milling parameters subject to multiple constraints

AU - Wang, Wenjie

AU - Tian, Guangdong

AU - Chen, Maoning

AU - Tao, Fei

AU - Zhang, Chaoyong

AU - AI-Ahmari, Abdulraham

AU - Li, Zhiwu

AU - Jiang, Zhigang

PY - 2020/2/1

Y1 - 2020/2/1

N2 - Selecting a set of reasonable milling parameters of computerized numerical control (CNC) machines is of great importance in decreasing energy consumption and enhancing processing productivity. However, existing works pay little attention to the optimization of energy-conscious milling parameters. This work establishes a dual-objective optimization model for the selection of milling parameters such that power consumption and process time are minimized. With multiple constraints of milling processing conditions, an improved artificial bee colony (ABC) intelligent algorithm is used to handle the proposed dual-objective optimization model. Compared with the non-dominated sorting genetic algorithm (NSGA-II), our improved algorithm has good performance.

AB - Selecting a set of reasonable milling parameters of computerized numerical control (CNC) machines is of great importance in decreasing energy consumption and enhancing processing productivity. However, existing works pay little attention to the optimization of energy-conscious milling parameters. This work establishes a dual-objective optimization model for the selection of milling parameters such that power consumption and process time are minimized. With multiple constraints of milling processing conditions, an improved artificial bee colony (ABC) intelligent algorithm is used to handle the proposed dual-objective optimization model. Compared with the non-dominated sorting genetic algorithm (NSGA-II), our improved algorithm has good performance.

KW - Dual-objective optimization

KW - Energy consumption model

KW - Intelligent algorithm

KW - Milling process model

KW - Scheduling and optimization

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DO - 10.1016/j.jclepro.2019.118714

M3 - 文章

AN - SCOPUS:85073711085

SN - 0959-6526

VL - 245

JO - Journal of Cleaner Production

JF - Journal of Cleaner Production

M1 - 118714

ER -

Dual-objective program and improved artificial bee colony for the optimization of energy-conscious milling parameters subject to multiple constraints

Abstract

UN SDGs

Keywords

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