为了计算得到曲轴动态特性参数，并使用模态贡献因子理论对动态特性进行研究，基于研究结果能够更加有效的实现曲轴结构参数的优化，首先基于多体动力学理论建立了某V12型曲轴的刚柔耦合多体动力学模型。基于模态贡献因子理论对曲轴动态特性进行分析，将前15阶模态缩聚为前5阶模态以减少计算规模，最大应力值的误差为0.9%，扭振角位移的误差为0.16%。利用BP（back propagation）神经网络建立了曲轴动静特性与结构参数之间的数学模型，进行了最大应力值和扭振角位移的线性回归，其输出响应的复相关系数都在0.95以上，表明此网络的泛化能力和预测性能都很好。对神经网络建立的数学模型使用遗传算法进行优化，优化后此曲轴的扭振角位移减小了2.63%，最大应力值减小了3.98%。结果表明神经网络结合遗传算法的优化方法对曲轴结构参数的动态特性和静态特性的联合优化能够满足设计预期，并且具有高效性和可行性。%As the key component of most power machines, crankshaft is under heavy cycle impact load and torque in working process, and its working performance and reliability directly affect the work efficiency and work safety. The dynamic characteristics are the important indicators of working stability and reliability of crankshaft. In order to get better performance of the crankshaft, when doing the structure optimization design of crankshaft, it is necessary to take into account the dynamic characteristics together with the static characteristics as the optimization objectives. In order to achieve this goal, in this pater, dynamic characteristics of crankshaft were analyzed at first. Based on multi-body dynamics theory, a rigid-flexible coupling multi-body dynamic model of a V12 crankshaft was built on ADAMS software platform. Using the given data by manufacturer, the boundary conditions in the working process of the crankshaft were calculated, which were correctly applied on the rigid-flexible coupling multi-body dynamic model. The angular vibration of crankshaft was obtained by calculation, which can measure the dynamic characteristics of crankshaft. The static characteristics of crankshaft were measured by maximum stress value. To calculate the stress, the method of applying load boundary conditions on corresponding journal of crankshaft was in this way: along the axial line, the loads were uniformly distributed; along the circumferential direction, the loads were distributed on 120 degrees range, and could be expressed in cosine way. Based on the modal contribution factor theory to analyze the dynamic characteristics of crankshaft, it could get a conclusion that the first modal contribution to the crankshaft dynamic response was much greater than the other order. Compared with the calculation results contained the top fifteen modes, the results of just calculating the top five modes appeared acceptable, the error of the maximum stress value and angular vibration was 0.9 percent and 0.16 percent, respectively. Therefore, when using the orthogonal experimental method to collect the samples which needed for the artificial neural network modeling, only the top five modes were calculated, and in this way, the calculation scale was reduced while the calculation accuracy was guaranteed. Through training the sample, using gradient descent learning algorithm, a BP neural network model of two-input and two-output was established, which is with a single hidden layer contained six nodes. The linear regression between the control parameters and maximum stress value, and angular vibration was processed. The multiple correlation coefficient of output response was both larger than 0.95. The results showed that the network had good generalization ability and forecast performance. Using the neural network mathematical model as the constraints and objective function of performance optimization,the calibration was optimized by genetic algorithm. The optimized angular vibration and maximum stress value of crankshaft was reduced 2.63 percent and 3.98 percent, respectively, while the mass was also reduced 0.56 percent, which catered to the modern design requirements of crankshaft lightweight design. It turned out that the method based on BP neural network and genetic algorithm can satisfy the structure parameters optimization, which combined the dynamic characteristics and static characteristics, and is feasible and efficient.
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