期刊文献+
共找到4篇文章
< 1 >
每页显示 20 50 100
基于RBF神经网络的挤出吹塑中型坯尺寸的预测 被引量:1
1
作者 杨艳娟 黄汉雄 《塑料工业》 CAS CSCD 北大核心 2006年第10期36-38,共3页
在利用BP神经网络预测挤出吹塑中型坯尺寸工作的基础上,采用径向基神经网络(RBF)来预测挤出吹塑中型坯尺寸,并与BP神经网络的预测结果进行了比较。结果表明,虽然RBF与BP神经网络均能较好地预测挤出吹塑中型坯尺寸,RBF网络的训练时间比B... 在利用BP神经网络预测挤出吹塑中型坯尺寸工作的基础上,采用径向基神经网络(RBF)来预测挤出吹塑中型坯尺寸,并与BP神经网络的预测结果进行了比较。结果表明,虽然RBF与BP神经网络均能较好地预测挤出吹塑中型坯尺寸,RBF网络的训练时间比BP少很多,只是BP的0.7%。 展开更多
关键词 挤出吹塑 型坯尺寸 RBF神经网络
下载PDF
IBC内胆型坯初始尺寸预测与试验
2
作者 朱志松 顾菲菲 +1 位作者 郭东军 严晓照 《包装工程》 CAS CSCD 北大核心 2016年第17期139-143,共5页
目的鉴于壁厚控制的复杂性,建立基于有限元型坯尺寸的预测方法。方法采用WorkbenchPOLYFLOW分析软件模拟型坯吹胀的过程,获得均匀的型坯尺寸吹胀成型的IBC内胆壁厚分布;分析型坯初始尺寸对制件最终壁厚分布的影响;确定制件壁厚的薄弱部... 目的鉴于壁厚控制的复杂性,建立基于有限元型坯尺寸的预测方法。方法采用WorkbenchPOLYFLOW分析软件模拟型坯吹胀的过程,获得均匀的型坯尺寸吹胀成型的IBC内胆壁厚分布;分析型坯初始尺寸对制件最终壁厚分布的影响;确定制件壁厚的薄弱部位及其相对应的型坯位置;调整型坯轴线方向控制点的初始尺寸,获得改善制件最终壁厚的型坯轴线方向的初始壁厚曲线。结果该曲线仿真壁厚极差为2.27 mm,通过大型中空成型机试生产,确定了型坯初始壁厚实验曲线,验证了预测型坯曲线的正确性。结论该尺寸预测方法缩短了生产周期,准确性高,具有较好的实际应用价值。 展开更多
关键词 内胆 挤出吹塑 型坯尺寸 数值模拟
下载PDF
Soft measurement model of ring's dimensions for vertical hot ring rolling process using neural networks optimized by genetic algorithm 被引量:2
3
作者 汪小凯 华林 +3 位作者 汪晓旋 梅雪松 朱乾浩 戴玉同 《Journal of Central South University》 SCIE EI CAS CSCD 2017年第1期17-29,共13页
Vertical hot ring rolling(VHRR) process has the characteristics of nonlinearity,time-variation and being susceptible to disturbance.Furthermore,the ring's growth is quite fast within a short time,and the rolled ri... Vertical hot ring rolling(VHRR) process has the characteristics of nonlinearity,time-variation and being susceptible to disturbance.Furthermore,the ring's growth is quite fast within a short time,and the rolled ring's position is asymmetrical.All of these cause that the ring's dimensions cannot be measured directly.Through analyzing the relationships among the dimensions of ring blanks,the positions of rolls and the ring's inner and outer diameter,the soft measurement model of ring's dimensions is established based on the radial basis function neural network(RBFNN).A mass of data samples are obtained from VHRR finite element(FE) simulations to train and test the soft measurement NN model,and the model's structure parameters are deduced and optimized by genetic algorithm(GA).Finally,the soft measurement system of ring's dimensions is established and validated by the VHRR experiments.The ring's dimensions were measured artificially and calculated by the soft measurement NN model.The results show that the calculation values of GA-RBFNN model are close to the artificial measurement data.In addition,the calculation accuracy of GA-RBFNN model is higher than that of RBFNN model.The research results suggest that the soft measurement NN model has high precision and flexibility.The research can provide practical methods and theoretical guidance for the accurate measurement of VHRR process. 展开更多
关键词 vertical hot ring rolling dimension precision soft measurement model artificial neural network genetic algorithm
下载PDF
A Mathematical Model to Study the Effect of Pore Sizes Distribution on Densification Process in Ceramic Compacts
4
作者 Ahmad K. Ahmad Sadeem Abbas Fadhil Fadhil A. Rasen 《Journal of Physical Science and Application》 2016年第1期10-15,共6页
In this research the effect of pore size distribution on densification process during sintering of ceramic compacts is studied by assuming a Gaussian distribution of the pore sizes and depending on a mathematical mode... In this research the effect of pore size distribution on densification process during sintering of ceramic compacts is studied by assuming a Gaussian distribution of the pore sizes and depending on a mathematical model that was developed in a previous research in describing the densification process. 展开更多
关键词 SINTERING solid inclusions pore size distribution.
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部