Research Papers

Energy-Aware Material Selection for Product With Multicomponent Under Cloud Environment

[+] Author and Article Information
Luning Bi, Ying Zuo, Zhuqing Liu

School of Automation Science and
Electrical Engineering,
Beihang University,
Beijing 100191, China

Fei Tao

School of Automation Science and
Electrical Engineering,
Beihang University,
Beijing 100191, China
e-mail: ftao@buaa.edu.cn

T. W. Liao

Department of Mechanical and
Industrial Engineering,
Louisiana State University,
Baton Rouge, LA 70803

1Corresponding author.

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received February 19, 2016; final manuscript received December 23, 2016; published online February 16, 2017. Assoc. Editor: Jitesh H. Panchal.

J. Comput. Inf. Sci. Eng 17(3), 031007 (Feb 16, 2017) (14 pages) Paper No: JCISE-16-1773; doi: 10.1115/1.4035675 History: Received February 19, 2016; Revised December 23, 2016

Energy consumption in manufacturing has risen to be a global concern. Material selection in the product design phase is of great significance to energy conservation and emission reduction. However, because of the limitation of the current life-cycle energy analysis and optimization method, such concerns have not been adequately addressed in material selection. To fill in this gap, a process to build a comprehensive multi-objective optimization model for automated multimaterial selection (MOO–MSS) on the basis of cloud manufacturing is developed in this paper. The optimizing method, named local search-differential group leader algorithm (LS-DGLA), is a hybrid of differential evolution and local search with the group leader algorithm (GLA), constructed for better flexibility to handle different needs for various product designs. Compared with a number of evolutionary algorithms and nonevolutionary algorithms, it is observed that LS-DGLA performs better in terms of speed, stability, and searching capability.

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Fig. 1

Life-cycle energy analysis of different materials in BIW design (source: adapted from Ref. [11])

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Fig. 2

Framework of the MOO–MMS problem in a cloud manufacturing system

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Fig. 3

Transformation between requirements and constraints

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Fig. 5

Flowchart for the proposed LS-DGLA

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Fig. 7

Population initialization

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Fig. 8

Strategy of local search

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Fig. 9

Parameter transfer from other groups

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Fig. 10

Experimental verification of the LS-DGLA

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Fig. 11

Average fitness with different number of candidate materials

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Fig. 12

Comparison among GA, DE, PSO, ABCA, COA, and LS-DGLA




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