【摘要】 针对真空耙式干燥机搅拌结构在长期高温工况及物料作用力下引发搅拌结构疲劳损伤与共振问题，提出基于实例的神经网络结合多目标优化方法。选取轴径、叶片倾角等5个关键结构变量，以挠度和一阶固有频率为目标函数，建立了多目标优化数学模型。利用Matlab绘制优化过程中结果图来确定优化方法的准确性，并得出最优参数数据。运用ANSYS对数据仿真验证，发现数据值与仿真值误差均不高于2%,验证了方法的有效性。研究结果可为干燥机搅拌结构设计提供参考。更多还原

【Abstract】 To address the fatigue damage and resonance of the stirring structure of the vacuum rake dryer under prolonged high-temperature and material forces, this paper proposes a case-based neural network combined with a multi-objective optimization method. First, five key structural variables including the shaft diameter and the blade inclination angle are selected. With deflection and first-order natural frequency as the objective functions, a multi-objective optimization mathematical model is built. Then, the results during the optimization process are plotted using Matlab to verify the accuracy of the optimization method, and the optimal parameter values are obtained. Both the deflection and the first-order natural frequency after optimization are improved. Finally, the ANSYS simulation software is employed to simulate and verify the data. Results show the error between the optimized values and the simulation values is no more than 2%, proving its effectiveness. This paper may provide some insights into designing the stirring structure of vacuum rake dryers.更多还原