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.
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