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浙江大学学报(工学版)
JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE)
    
Optimal design of luffing mechanism of portal crane based on genetic algorithm and finite element analysis
ZHAO Qiong1, TONG Shui-guang2, ZHONG Wei2, GE Jun-xu3
1.Institute of Chemical Machinery, Zhejiang University, Hangzhou 310027, China; 2.Institute of Thermal Engineering and Power System, Zhejiang University, Hangzhou 310027, China; 3.Department of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
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Abstract  

 Four-bar luffing mechanism was optimized to reduce manufacturing cost and operating cost of portal crane. An optimal design model with key parameters as design variables was established, And an optimization method based on genetic algorithm and finite element analysis was proposed, to minimize the weight, the unbalanced moment and the vertical drop of the cargo during the luffing process, while accomplish the technical parameters of portal crane. The parameterized finite element model generator was developed for four-bar luffing mechanism of portal crane. The performance constraint conditions obtained from finite element solver and the boundary constraint conditions were used to evaluate the optimal targets. The results indicate that the model and method is suitable for the structure and performance optimal design of the four-bar luffing mechanism of portal crane, and can improve the design quality effectively.

Published: 26 December 2015
CLC:  TH 213.4  
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Cite this article:

ZHAO Qiong, TONG Shui-guang, ZHONG Wei, GE Jun-xu. Optimal design of luffing mechanism of portal crane based on genetic algorithm and finite element analysis. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2015, 49(5): 880-886.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2015.05.010     OR     http://www.zjujournals.com/eng/Y2015/V49/I5/880


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［1］ 陆国贤, 倪庆兴, 张荣康, 等. 门座起重机设计［M］. 北京: 人民交通出版社, 1985: 36-127．
［2］ 计三有, 苏运波, 刘清. 门座起重机臂架结构有限元分析［J］. 起重运输机械, 2007, 8: 59-61．
XU San-you, SU Yun-bo, LIU Qing. Finite element analysis on the jib structure of portal crane ［J］. Hoisting and Conveying Machinery, 2007, 8: 59-61．
［3］ 郭金泉, 杨晓翔, 刘思靓, 等. M02-10t门座起重机整机结构有限元分析与应变测试［J］. 机电工程, 2012, 29(3) : 253-256．
GUO Jin-quan, YANG Xiao-xiang, LIU Si-liang, et al. Finite element analysis and strain measurement of a M02-10t gantry crane ［J］. Journal of Mechanical & Electrical Engineering, 2012, 29(3) : 253-256．
［4］ 何希杰, 朱广奇, 劳学苏. 遗传算法在离心泵优化设计中的应用［J］. 排灌机械, 2008, 26(2): 40-44．
HE Xi-jie, ZHU Guang-qi, LAO Xue-su. Application of genetic algorithm in optimal design of centrifugal pump ［J］. Drainage and Irrigation Machinery, 2008, 26(2): 40-44．
［5］ WU-Yan-ling, ZHONG Wei, TONG Shui-guang. Subsystem Optimization of Tail Boiler Heating Surface via Genetic Algorithm ［J］. Proceedings of the CSEE, 2010(8): 56-62．
［6］ 郭辰光, 王鹏家, 田鹏, 等. 基于遗传算法的数控机床主轴优化设方法［J］. 东北大学学报:自然科学版, 2011, 32(6): 850-853．
GUO Chen-guang, WANG Peng-jia, TIAN Peng, et al. Optimization design of CNC machine tool spindle based on genetic algorithm ［J］. Journal of Northeastern University :Natural Science, 2011, 32(6): 850-853．
［7］ 周明, 孙树栋.遗传算法原理及应用［M］.北京: 国防工业出版社, 1999: 25-30.
［8］ GB38112-83. 起重机设计规范［S］. 北京: 北京起重运输机构研究所, 1983: 545．
GB38112-83. Design rules for cranes\[S\]. Beijing:Beijing Materials Handling Research Institute.1983:545.
［9］ 王定标, 郭茶秀, 向飒. CAD/CAE/CAM技术与应用［M］. 北京: 化学工业出版社, 2005: 25-78．
［10］ ANGEL E. Interactive computer graphics: a top-down approach using opengl, 5/e ［M］. Plano:Pearson Education India, 2008: 47-159．
［11］ Electronic Data Systems Corporation. NX Nastran lnear static analysis users guide ［M］. Texas: Siemens PLM Software, 2003: 31-75．
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