• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Materials Engineering and Performance >Prediction of Flow Stress in Cadmium Using Constitutive Equation and Artificial Neural Network Approach
【24h】

Prediction of Flow Stress in Cadmium Using Constitutive Equation and Artificial Neural Network Approach

机译：应用本构方程和人工神经网络方法预测镉的流变应力

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

A model is developed to predict the constitutive flow behavior of cadmium during compression test using artificial neural network (ANN). The inputs of the neural network are strain, strain rate, and temperature, whereas flow stress is the output. Experimental data obtained from compression tests in the temperature range -30 to 70℃, strain range 0.1 to 0.6, and strain rate range 10~(-3) to 1 s~(-1) are employed to develop the model. A three-layer feed-forward ANN is trained with Levenberg-Marquardt training algorithm. It has been shown that the developed ANN model can efficiently and accurately predict the deformation behavior of cadmium. This trained network could predict the flow stress better than a constitutive equation of the type ε = A sinh(α/σ)~n exp(-Q/RT).
机译：使用人工神经网络（ANN）开发了一种模型，以预测压缩试验期间镉的本构流动行为。神经网络的输入是应变，应变率和温度，而流应力是输出。通过在-30至70℃的温度范围，0.1至0.6的应变范围以及10〜（-3）至1 s〜（-1）的应变速率范围内的压缩试验获得的实验数据来开发模型。使用Levenberg-Marquardt训练算法训练三层前馈ANN。结果表明，所建立的人工神经网络模型可以有效，准确地预测镉的变形行为。该训练网络可以比ε= A sinh（α/σ）〜n exp（-Q / RT）类型的本构方程更好地预测流应力。

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