Prediction of melt temperature distribution in vibration induced polymer plasticization

ZHANG Dong-zhi; YANG Yan-juan; CAI Jun

doi:10.3788/OPE.20101807.1620

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Prediction of melt temperature distribution in vibration induced polymer plasticization

Article | 更新时间：2020-08-12

- Prediction of melt temperature distribution in vibration induced polymer plasticization
- Optics and Precision Engineering Vol. 18, Issue 7, Pages: 1620-1628(2010)
- 作者机构：
  
  华南理工大学机械与汽车工程学院,广东广州,510640
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20101807.1620
  CLC：
- Received：29 December 2009，
  
  Revised：02 March 2010，
  
  Published Online：30 July 2010，
  
  Published：20 July 2010
- 稿件说明：
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张冬至, 杨艳娟, 蔡军. 振动诱导聚合物塑化过程中熔体温度分布的预测[J]. 光学精密工程, 2010,18(7): 1620-1628

ZHANG Dong-zhi, YANG Yan-juan, CAI Jun. Prediction of melt temperature distribution in vibration induced polymer plasticization[J]. 光学精密工程, 2010,18(7): 1620-1628
张冬至, 杨艳娟, 蔡军. 振动诱导聚合物塑化过程中熔体温度分布的预测[J]. 光学精密工程, 2010,18(7): 1620-1628 DOI： 10.3788/OPE.20101807.1620.

ZHANG Dong-zhi, YANG Yan-juan, CAI Jun. Prediction of melt temperature distribution in vibration induced polymer plasticization[J]. 光学精密工程, 2010,18(7): 1620-1628 DOI： 10.3788/OPE.20101807.1620.

摘要

在振动力场诱导聚合物塑化成型作用下

建立了聚合物熔融挤出过程中的熔体温度分布模型

研究了模头温度、振动力场的振幅、频率等工艺参数对挤出过程中熔体温度的影响。提出了基于多项式和高斯RBF核函数变换的两种非线性岭回归模型(PT-RR和GRBF-RR)

并对具有非线性、非等温、强耦合特征的熔融过程熔体温度分布进行研究。该建模方法实现了多变量输入样本的高维特征空间非线性映射与重构

充分挖掘了多影响因素之间的耦合信息。仿真实验结果表明了PT-RR和GRBF-RR模型的有效性

其回归预测值与实验测量值之间的相关系数均值分别为0.994 0和1。由于GRBF-RR模型取得了满意的模型精度

本文基于对GRBF-RR模型的数值模拟分析了各影响因素对熔体温度分布的交叉耦合影响

表征了聚合物熔融挤出成型过程中熔体温度分布的规律。该项研究可为提高精密挤出制品质量及优化配置各工艺参数提供决策依据。

Abstract

Melt temperature distribution models for melting and plasticizing process of polymer materials were presented in vibration field induced extrusion

and the influences of process parameters such as die temperature

vibration amplitude and frequency on the melt temperature were investigated. Two kinds of ridge regression models based on the nonlinear transforms of polynomial and Gaussian RBF (PT-RR and GRBF-RR) were established to predict the melt temperature distribution with the nonlinear

non-isothermal properties and strong-coupling extrusion. The two models have fulfilled the nonlinear mapping and reconstruction of high-dimension feature space from multi-variable input samples

and obtained the coupling relations among multi-factor influences by the numerical simulation based on GRBF-RR. The simulation and experimental results show that the two models are valid and the correlation coefficients between the predicted values and that measured values are 0.994 0 and 1 for PT-RR and GRBF-RR

respectively. As the GRBF-RR can offer higher model precision

it was used to illuminate the influences of process parameters on the melt temperature distribution. Obtained results demonstrate that the model can provide a decision support for the quality control and process parameter optimization.

关键词

Keywords

references

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