Efficiency Optimization for Disassembly Tools via Using NN-GA Approach
Joint Authors
Chu, Jiangwei
Qiang, Tianggang
Xu, Guan
Zhou, Wei
Tian, Guangdong
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-11-24
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Disassembly issues have been widely attracted in today’s sustainable development context.
One of them is the selection of disassembly tools and their efficiency comparison.
To deal with such issue, taking the bolt as a removal object, this work designs their removal experiments for different removal tools considering some factors influencing its removal process.
Moreover, based on the obtained experimental data, the removal efficiency for different removal tools is optimized by a hybrid algorithm integrating neural networks (NN) and genetic algorithm (GA).
Their efficiency comparison is discussed.
Some numerical examples are given to illustrate the proposed idea and the effectiveness of the proposed methods.
American Psychological Association (APA)
Tian, Guangdong& Qiang, Tianggang& Chu, Jiangwei& Xu, Guan& Zhou, Wei. 2013. Efficiency Optimization for Disassembly Tools via Using NN-GA Approach. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-8.
https://search.emarefa.net/detail/BIM-1031716
Modern Language Association (MLA)
Tian, Guangdong…[et al.]. Efficiency Optimization for Disassembly Tools via Using NN-GA Approach. Mathematical Problems in Engineering No. 2013 (2013), pp.1-8.
https://search.emarefa.net/detail/BIM-1031716
American Medical Association (AMA)
Tian, Guangdong& Qiang, Tianggang& Chu, Jiangwei& Xu, Guan& Zhou, Wei. Efficiency Optimization for Disassembly Tools via Using NN-GA Approach. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-8.
https://search.emarefa.net/detail/BIM-1031716
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1031716