An optimization approach of selective laser sintering considering energy consumption and material cost

被引:64
作者
Ma, Feng [1 ,2 ]
Zhang, Hua [1 ,2 ]
Hon, K. K. B. [2 ,3 ]
Gong, Qingshan [1 ,2 ]
机构
[1] Wuhan Univ Sci & Technol, Minist Educ, Key Lab Met Equipment & Control Technol, Wuhan 430081, Hubei, Peoples R China
[2] Wuhan Univ Sci & Technol, Hubei Key Lab Mech Transmiss & Mfg Engn, Wuhan 430081, Hubei, Peoples R China
[3] Univ Liverpool, Sch Engn, Liverpool L69 3GH, Merseyside, England
基金
中国国家自然科学基金; 湖北省教育厅重点项目;
关键词
Minimum cost; Minimum energy; Multi-objective optimization; Additive manufacturing;
D O I
10.1016/j.jclepro.2018.07.185
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Selective laser sintering technology is a method of additive manufacturing that is growing with widely application. Due to the increasing tense of energy situation, it is also timely to consider the economic and environmental issues of growth in additive manufacturing. The innovative selective laser sintering technology optimization approach proposed in this article encourages and enables the designers and users to obtain optimal sintering parameters and reduces energy consumption and cost in sintering process. This paper creates a potential approach for realizing the relationship between main sintering parameters and energy consumption and material cost. To achieve high efficiency of the process, optimization of parameters based on energy and cost consumption are investigated. A multi-objective model with optimized constraints is set up and solved by non-dominated sorting genetic algorithm II. Energy consumption and material cost are treated as the two objectives, which are affected by three variables, namely scanning speed, gap distance and layer thickness. The effectiveness of the multi-objective optimization model was verified experimentally and results are fully discussed. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码:529 / 537
页数:9
相关论文
共 34 条
[1]   Resource based process planning for additive manufacturing [J].
Ahsan, Amm Nazmul ;
Habib, Md Ahasan ;
Khoda, Bashir .
COMPUTER-AIDED DESIGN, 2015, 69 :112-125
[2]  
[Anonymous], 2012, EC ASPECTS ADDITIVE
[3]  
Bourhis EL, 2013, INT J ADV MANUF TECH, DOI [10.1007/x00170-013-5151-2, DOI 10.1007/X00170-013-5151-2]
[4]   Framework to predict the environmental impact of additive manufacturing in the life cycle of a commercial vehicle [J].
Burkhart, Mathias ;
Aurich, Jan C. .
22ND CIRP CONFERENCE ON LIFE CYCLE ENGINEERING, 2015, 29 :408-413
[5]  
Chin-Ching Yeh, 2014, International Journal of Automation and Smart Technology, V4, P1, DOI 10.5875/ausmt.v4i1.597
[6]   A fast and elitist multiobjective genetic algorithm: NSGA-II [J].
Deb, K ;
Pratap, A ;
Agarwal, S ;
Meyarivan, T .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (02) :182-197
[7]   An economic analysis comparing the cost feasibility of replacing injection molding processes with emerging additive manufacturing techniques [J].
Franchetti, Matthew ;
Kress, Connor .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2017, 88 (9-12) :2573-2579
[8]   A review of energy simulation tools for the manufacturing sector [J].
Garwood, Tom Lloyd ;
Hughes, Ben Richard ;
Oates, Michael R. ;
O'Connor, Dominic ;
Hughes, Ruby .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2018, 81 :895-911
[9]  
Griffiths G. A., 2016, J CLEAN PROD, DOI [10.1016/jjclepro.2016.07.182, DOI 10.1016/JJCLEPRO.2016.07.182]
[10]  
Gutowski T. G., 2009, ENVIRON SCI TECHNOL, DOI [10.1007/s00170-014-5835-2, DOI 10.1007/S00170-014-5835-2]