Study on Optimization Design Method of PA66 Gear Injection Molding Process Parameter

Xu Cheng; Cheng Zhihu; Lu Jingui

doi:10.16578/j.issn.1004.2539.2018.01.015

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Study on Optimization Design Method of PA66 Gear Injection Molding Process Parameter

更新时间：2022-10-20

- Study on Optimization Design Method of PA66 Gear Injection Molding Process Parameter
- Vol. 42, Issue 1, Pages: 69-72(2018)
- 作者机构：
  
  1. 南京工业大学机械工程学院
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2018.01.015
  CLC：
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[1]徐铖,程智虎,陆金桂.聚酰胺66齿轮注塑工艺参数优化设计方法研究[J].机械传动,2018,42(01):69-72+149.

Xu Cheng, Cheng Zhihu, Lu Jingui. Study on Optimization Design Method of PA66 Gear Injection Molding Process Parameter. [J]. 42(1):69-72(2018)
[1]徐铖,程智虎,陆金桂.聚酰胺66齿轮注塑工艺参数优化设计方法研究[J].机械传动,2018,42(01):69-72+149. DOI： 10.16578/j.issn.1004.2539.2018.01.015.

Xu Cheng, Cheng Zhihu, Lu Jingui. Study on Optimization Design Method of PA66 Gear Injection Molding Process Parameter. [J]. 42(1):69-72(2018) DOI： 10.16578/j.issn.1004.2539.2018.01.015.

摘要

以降低聚酰胺66（PA66）齿轮的注塑成型体积收缩率为例,利用正交试验与Moldflow模拟软件获得数据作为神经网络的训练样本,建立PA66齿轮体积收缩率的神经网络预估模型,并通过样本检验了人工神经网络预估模型的准确性,同时缩短了优化工艺参数的时间,达到进一步优化了工艺参数的效果。结果表明,将正交试验法、数值模拟试验与人工神经网络相结合用于塑料齿轮成型工艺参数的优化,可以大大提高工艺设计的效率,并具有较高的准确性。

Abstract

In order to reduce the volume shrinkage of polyamide 66（PA 66） gear injection molding as an example,by using the orthogonal test and Moldflow simulation software to obtain data as training samples of neural network,the neural network prediction model of the volume shrinkage of the gear is established,and the accuracy of the artificial neural network model is tested by the sample. Therefore,the time of optimizing process parameters is shortened,and the process parameters are optimized. The results show that,the efficiency of process design can be greatly improved by using the optimization of molding process parameters of plastic gear with combining the orthogonal test,numerical simulation and artificial neural network,and the accuracy is high.

关键词

PA66齿轮正交试验法人工神经网络成型工艺优化

Keywords

PA66 gearOrthogonal test methodArtificial neural networkMolding processOptimization

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