ZHU Di, YAO Yuan, PENG Xiongqi. An Optimization Algorithm for CAE Design of Carbon Fiber Reinforced Composite Chassis Longitudinal Arms[J]. Applied Mathematics and Mechanics, 2018, 39(8): 925-934. doi: 10.21656/1000-0887.390001
Citation: ZHU Di, YAO Yuan, PENG Xiongqi. An Optimization Algorithm for CAE Design of Carbon Fiber Reinforced Composite Chassis Longitudinal Arms[J]. Applied Mathematics and Mechanics, 2018, 39(8): 925-934. doi: 10.21656/1000-0887.390001

An Optimization Algorithm for CAE Design of Carbon Fiber Reinforced Composite Chassis Longitudinal Arms

doi: 10.21656/1000-0887.390001
  • Received Date: 2018-01-02
  • Rev Recd Date: 2018-01-14
  • Publish Date: 2018-08-15
  • The rear longitudinal arm is one of the main structures of the automobile chassis. Design of the rear longitudinal arm with carbon fiber reinforced polymer (CFRP) can reduce its weight effectively. However, the application of composite materials also brings great challenges to the optimization design process, such as complex multiple conditions and a large number of design variables. The secondary development of ABAQUS was conducted with Python to fulfill the global ergodic search for thickness ratios of different ply angles to find the effective range and the optimum solution. In order to reduce the long running time under multi working conditions, the treebased algorithms, such as XGBoost, DART and random forest, were introduced into the thickness ratio calculation. In view of both the running time and the computation accuracy, for 0 or 10 cases of calculation under the new condition, the accuracy rate of the TsaiWu factor can reach 96.3% and 98.3% (compared with failure value 1). If the number of cases under new working conditions increases to 40 while existing working conditions decreases by half, the accuracy rate can reach 95.0%. The developed algorithm provides a useful reference for reducing the running time of optimization design of composite parts under multi working conditions.
  • loading
  • [1]
    杨小平, 隋刚. 碳纤维复合材料在新能源产业中的应用进展[J]. 新材料产业, 2012(2): 20-24.(YANG Xiaoping, SUI Gang. Application of carbon fiber composite materials in new energy industry[J]. Advanced Materials Industry,2012(2): 20-24.(in Chinese))
    [2]
    冯美斌. 汽车轻量化技术中新材料的发展及应用[J]. 汽车工程, 2006,28(3): 213-220.(FENG Meibin. Development and application of new materials in automotive lightweighting technologies[J]. Automotive Engineering,2006,28(3): 213-220.(in Chinese))
    [3]
    PARPINELLI R S, LOPES H S, FREITAS A A. Data mining with an ant colony optimization algorithm[J]. IEEE Transactions on Evolutionary Computation,2 002,6(4): 321-332.
    [4]
    肖书敏, 闫云聚, 姜波澜. 基于小波神经网络方法的桥梁结构损伤识别研究[J]. 应用数学和力学, 2016,37(2): 149-159.(XIAO Shumin, YAN Yunju, JIANG Bolan. Damage identification for bridge structures based on the wavelet neural network method[J]. Applied Mathematics and Mechanics,2016,37(2): 149-159.(in Chinese))
    [5]
    DILEEP P N, KUMAR RR, RAO G V. A neural-genetic algorithm approach for evaluation of notched strength of laminate[J]. Journal of the Institution of Engineers(India),2 002.
    [6]
    刘振国, 胡杰, 胡龙. 基于遗传算法的层合板分级铺层全局优化[J]. 北京航空航天大学学报, 2013,39(4): 478-483.(LIU Zhenguo, HU Jie, HU Long. Global optimization of classified composite laminated structures based on genetic algorithms[J]. Journal of Beijing University of Aeronautics and Astronautics,2013,39(4): 478-483.(in Chinese))
    [7]
    RAO A R M, LAKSHMI K. Optimal design of stiffened laminate composite cylinder using a hybrid SFL algorithm[J].Journal of Composite Materials,2012,46(24): 3031-3055.
    [8]
    SALAMAT A R, RAIESINEZHAD M. Optimum design ofantisymmetric cross-ply and angle-ply laminate with bees algorithm[Z]. 2012.
    [9]
    YAO Y, WANG T, GONG Y, et al. Development of a carbon fiber reinforced composite chassis longitudinal arm[J]. Science of Advanced Materials,2016,8(11): 2133-2141.
    [10]
    龚友坤, 王韬, 姚远, 等. 汽车底盘碳纤维后纵臂成形实验与分析[J]. 汽车工程, 2016,38(2): 248-251.(GONG Youkun, WANG Tao, YAO Yuan, et al. Forming experiment and analysis of vehicle rear longitudinal arm of carbon fiber reinforced composite[J]. Automotive Engineering,2016,38(2): 248-251.(in Chinese))
    [11]
    ROKACH L, MAIMON O. Data Mining With Decision Trees: Theory and Applications [M]. Singapore: World Scientific Publishing Company, 2008.
    [12]
    KIM Y, PERRIG A, TSUDIK G. Tree-based group key agreement[J]. Acm Transactions on Information & System Security,2004,7(1): 60-96.
    [13]
    LIANG X, QU F, YANG Y, et al. An improved ID3 decision tree algorithm based on attribute weighted[C]// International Conference on Civil, Materials and Environmental Sciences.Paris, France, 2015.
    [14]
    LU G, KRISHNAMACHARI B, RAGHAVENDRA C S. An adaptive energy-efficient and low-latency MAC for tree-based data gathering in sensor networks: research articles[J]. Wireless Communications & Mobile Computing,2007,7(7): 863-875.
    [15]
    QUINLAN J R. Induction on decision tree[J]. Machine Learning,1986,1(1): 81-106.
    [16]
    CHEN T, GUESTRIN C. XGBoost: a scalable tree boosting system[C]// ACM Sigkdd International Conference on Knowledge Discovery and Data Mining. San Francisco, California, USA, 2016: 785-794.
    [17]
    VINAYAK R K, GILAD-BACHRACH R. DART: Dropouts meet multiple additive regression trees[C]// Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics. Vol38. San Diego, California, USA, 2015: 489-497.
    [18]
    SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout: a simple way to prevent neural networks fromoverfitting[J]. Journal of Machine Learning Research,2014,15(1): 1929-1958.
    [19]
    GUYON I, ELISSEEFF A. An introduction to variable and feature selection[J]. Journal of Machine Learning Research,2 003,3(6): 1157-1182.
    [20]
    ERNST D, GEURTS P, WEHENKEL L. Tree-based batch mode reinforcement learning[J]. Journal of Machine Learning Research,2005,6(2): 503-556.
    [21]
    史忠植. 知识发现[M]. 北京: 清华大学出版社, 2011.(SHI Zhongzhi. Knowledge Discovery [M]. Beijing: Tsinghua University Press, 2011.(in Chinese))
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (1531) PDF downloads(707) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return