摘要
基于神经网络的风电塑料齿轮箱优化模型,通过设计并行神经网络捕获抽象、全面、非线性、大感受野的翘区、体积收缩语义特征信息。经仿真可知,当塑料熔体温度为240℃、模具温度为52.58℃、注射时间为1.01 s时,齿轮箱具备较小翘曲量和体积收缩率。应用Pro/E软件、工艺参数模拟齿轮圈注塑过程,仿真可知,注塑位置为c时,齿轮圈的注塑时长最小,注塑温度分布较均匀,不易出现翘曲、变形等缺陷。
The optimization model of wind power plastic gearbox based on neural network,through the design of parallel neural network,captures the semantic feature information of abstract,comprehensive,nonlinear,large receptive field warping area and volume shrinkage.The simulation shows that when the plastic melt temperature is 240 ℃,the mold temperature is 52.58 ℃,and the injection time is 1.01 s,the gearbox has a smaller amount of warpage and volume shrinkage.Use Pro/E software and process parameters to simulate the injection molding process of the gear ring.The simulation shows that when the injection position is c,the injection time of the gear ring is the smallest,the plasticizing pressure distribution is small and warping and deformation are not easy to occur.defect.
作者
刘书伦
李飞
彭高辉
LIU Shu-lun;LI Fei;PENG Gao-hui(Jiyuan Vocational and Technical College,Jiyuan 459000,China;School of Mathematics and Statistics,North China University of Water Resources and Electric Power,Zhengzhou 450046,China)
出处
《塑料科技》
CAS
北大核心
2021年第1期113-116,共4页
Plastics Science and Technology
关键词
神经网络
风电塑料齿轮箱
注塑工艺
PRO/E
Neural network
Wind power plastic gearbox
Injection molding process
Pro/E
