基于木塑专用同向双螺杆挤出机螺杆预测的神经网络模型及其应用

Neural Network Model of WPC Twin-Screw Extruder Screw Forecast and Its Application

基于传统双螺杆挤出机设计理论和木塑复合材料性质,建立了神经网络BP模型,对木塑复合材料专用同向双螺杆挤出机螺杆直径和转速关系进行预测。首先,以木塑复合材料的粘度、机头处压力、螺杆计量段温差和目标产量作为输入变量,螺杆的直径和转速作为输出变量,传统同向双螺杆挤出机设计理论作为动量方程,建立神经网络BP模型。然后,经过样本输入,对模型进行数值拟合训练,直到满足误差要求。最终,利用模型对双螺杆直径、转速进行预测,输出最佳结果。结果表明,结合传统设计理论,由材料性质和产量整合的样本输入建立的智能网络模型能较好地模拟实际生产时的螺杆运动的复杂情况。

The BP neural network model is established based on the design theory of traditional twin-screw extrusion machine and the property of wood-plastic composite. The relationship of screw＇ s diameter and rotating speed of twin- screw extruder special for wood-plastic composite material are predicted. Firstly, taking the viscosity of wood plastic composite material, the pressure of extruder＇ s head, the temperature of screw＇ s metering section and the target yield as input variables, the screw＇ s diameter and rotating speed as output variables and the design theory of traditional double screw extrusion machine as the momentum equation, establishes the BP neural network model. Then, through the sample inputting, numerical fitting training of the model is conducted until the error requirement is met. Finally the model is applied to forecast the diameter and rotating speed of twin-crew, and the optimal result is output. The result shows that combining with traditional design theory, the intelligent network model established through material properties and yield integrated sample input can better simulate the complicate conditions of screw motion in practical production.