• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of advanced intelligence paradigms >Application of artificial neural network on deformation and densification behaviour of sintered Fe-C steel under cold upsetting
【24h】

Application of artificial neural network on deformation and densification behaviour of sintered Fe-C steel under cold upsetting

机译：人工神经网络在冷镦型下烧结Fe-C钢变形及致密化行为的应用

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Cold upsetting is one of the densification processes used in Podwer Metallurgy (P/M) materials to achieve the desired density by applying required amount of load. The present work aims to study the deformation and densification characteristics of plain carbon steel (Fe-C) containing various levels of carbon viz. 0.2%, 0.5% and 1% under cold upsetting. The sintered preforms of various compositions of Fe-C were subjected to cold upset. The axial and lateral deformations were calculated from the physical measurements taken from the deformed and non-deformed specimens and the density of the deformed preforms was measured by Archimedes' principle. The experimental data were used further to generate the deformation and densification model using Artificial Neural Network (ANN). It is observed from the experimental results that increasing carbon content improves the deformation and densification properties of iron material as it behaves like a lubricant and increases the binding strength between the grains.
机译：冷镦令是通过施加所需的负载量来实现所需密度的培养皿冶金（P / M）材料中的一种致密化方法之一。目前的工作旨在研究含有各种碳纤维的普通碳钢（Fe-C）的变形和致密化特性。在冷镦型下0.2％，0.5％和1％。将各种组合物的Fe-C组合物的烧结预制件进行冷镦。根据从变形和非变形样品所取出的物理测量来计算轴向和横向变形，通过Archimedes的原理测量变形预制件的密度。使用人工神经网络（ANN）进一步使用实验数据来产生变形和致密化模型。从实验结果中观察到，随着润滑剂的表现，增加碳含量提高了铁材料的变形和致密化性能，并增加了晶粒之间的结合强度。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号