基于模糊RBF神经元网络PID控制对FDM成型中温度控制的研究  

Research on Temperature Control in FDM Molding Based on Fuzzy RBF Neural Network PID Control

作　　者：郭凯[1] GUO Kai(School of Mechanical and Electronic Engineering,Suzhou University,Suzhou 234000,China)

机构地区：[1]宿州学院机械与电子工程学院,安徽宿州234000

出　　处：《宿州学院学报》2021年第6期4-7,共4页Journal of Suzhou University

基　　金：安徽省质量工程项目(2017jyxm0512);宿州学院科研平台开放课题项目(2019ykf26,2019ykf10,2016ykf06,2017ykf08,2019ykf31,2019ykf27)。

摘　　要：FDM 3D打印过程中熔料温度会影响工件精度,同时熔料温度又受热床温度、成型室温度、喷嘴温度和室温影响,为降低温度对工件精度的影响,提出了一种基于模糊RBF神经网络自适应PID的控制策略。该策略是通过神经网络自学习能力在温度控制中在线调整PID控制器的参数,结合了神经网络对非线性自适应强和普通PID控制结构简单易实现等特点,进而提高了对FDM打印过程中熔料温度的控制和整体过程的稳定性和准确性。In the process of FDM 3D printing,the melting temperature will affect the workpiece precision.Meanwhile,the melting temperature is affected by the temperature of the hot bed,the forming chamber,the nozzle and the room.The control strategy based on fuzzy RBF neural network adaptive PID is proposed to reduce the influence of temperature on workpiece precision.The strategy is to adjust the parameters of the PID controller on-line through the neural network self-learning ability in the temperature control,and it combines the characteristics that the neural network are highly adaptive to nonlinearity and the common PID control structure is simple and easy to realize.It is then to improve the stability and accuracy of the melting temperature in the process of FDM printing.

关 键 词：熔料温度 模糊RBF神经网络 PID控制 打印精度 

分 类 号：TF06[冶金工程—冶金物理化学]