版权说明 操作指南
首页 > 成果 > 成果详情

A back propagation artificial neural network prediction model of the gate freeze time for injection molded polypropylenes

认领
导出
下载 Link by 万方学术期刊
反馈
分享
QQ微信 微博
成果类型:
期刊论文
作者:
Wang, Rongji*;Feng, Xiaoxin;Xia, Yuejun;Zeng, Junliang
通讯作者:
Wang, Rongji
作者机构:
[Feng, Xiaoxin; Wang, Rongji; Zeng, Junliang; Xia, Yuejun] Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha, Hunan, Peoples R China.
通讯机构:
[Wang, Rongji] C
Cent South Univ Forestry & Technol, Coll Mech & Elect Engn, Changsha, Hunan, Peoples R China.
语种:
英文
关键词:
artificial neural network;gate freeze time;plastic injection molding
期刊:
JOURNAL OF MACROMOLECULAR SCIENCE PART B-PHYSICS
ISSN:
0022-2348
年:
2013
卷:
52
期:
10
页码:
1414-1426
机构署名:
本校为第一且通讯机构
院系归属:
机电工程学院
摘要:
This paper presents a back propagation artificial neural network (BP ANN) prediction model of the gate freeze time (tgf) for injection molded polypropylenes. An orthogonal design method was applied to enhance the BP ANN performance. The test results on the performance of the BP ANN prediction model showed that it can predict tgf with reasonable accuracy. Utilizing the BP ANN prediction model, the effects of the process factors, melt temperature (Tme), fill time (tf), gate area (A in), packing pressure (Pp), and mold temperature (T mo) on tgf we...

反馈

验证码:
看不清楚,换一个
确定
取消

成果认领

标题:
用户 作者 通讯作者
请选择
请选择
确定
取消

提示

该栏目需要登录且有访问权限才可以访问

如果您有访问权限,请直接 登录访问

如果您没有访问权限,请联系管理员申请开通

管理员联系邮箱:yun@hnwdkj.com