Prediction of Flow Stress in Ti-6Al-4V Alloy with Hydrogen at High Temperature using Artificial Neural Network

机译：基于人工神经网络的高温含氢Ti-6Al-4V合金流动应力预测

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The hot deformation behaviour of Ti-6Al-4V alloy hydrogenated were studied and the artificial neural network (ANN) models were established to predict the flow stress of hot deformation of Ti-6Al-4V alloy with hydrogen in this paper. The inputs of the neural network (NN) are compression temperature, strain rate, strain and hydrogen content, and the output of NN model is flow stress at high temperature. The model is based on back propagation (BP) network, in which L-M algorithm and two hidden layers were used. A good performance of the neural network is achieved. The model can be used for the prediction of flow stress of Ti-6Al-4V alloy with different hydrogen content as function of hot deformation parameters.

机译：研究了氢化的Ti-6Al-4V合金的热变形行为，建立了人工神经网络模型，预测Ti-6Al-4V合金加氢热变形的流变应力。神经网络（NN）的输入是压缩温度，应变率，应变和氢含量，而NN模型的输出是高温下的流动应力。该模型基于反向传播（BP）网络，其中使用了L-M算法和两个隐藏层。实现了神经网络的良好性能。该模型可用于预测不同氢含量的Ti-6Al-4V合金的流变应力，作为热变形参数的函数。

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来源
《International Conference on Proceeding amp; Manufacturing of Advanced Materials; 20060704-08; Vancouver(CA)》|2006年|P.3696-3701|共6页
会议地点 Vancouver(CA)
作者
Q. Wang; T. Wu; D.L Sun; J. Lai;
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作者单位

School of Materials Science and Engineering, Harbin Institute of Technology, 92 Xidazhi Street, Harbin 150001, China;

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原文格式 PDF
正文语种 eng
中图分类工程材料一般性问题;
关键词
Ti-6Al-4V alloy; artificial neural network; flow stress of hot deformation; hydrogenation;

机译：Ti-6Al-4V合金;人工神经网络;热变形流应力;加氢;

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专利

1. 材料模型对高温Ti-6Al-4V合金理论成形极限图预测的影响 [J] . Nitin KOTKUNDE, Sashank SRINIVASAN, Geetha KRISHNA, 中国有色金属学报（英文版） . 2016,第003期
2. Prediction of flow stress in Ti-6Al-4V alloy with an equiaxed α + β microstructure by artificial neural networks [J] . N.S. Reddy, You Hwan Lee, Chan Hee Park, Materials Science and Engineering . 2008,第1a2期

机译：用人工神经网络预测等轴α+β微结构Ti-6Al-4V合金的流动应力。
3. Prediction of flow stress in a wide temperature range involving phase transformation for as-cast Ti-6Al-2Zr-1Mo-1V alloy by artificial neural network [J] . Guo-zheng Quan, Wen-quan Lv, Yuan-ping Mao, Materials & design . 2013,第sepa期

机译：利用人工神经网络预测Ti-6Al-2Zr-1Mo-1V铸态合金在涉及相变的宽温度范围内的流动应力
4. Prediction of flow stresses at high temperatures with artificial neural networks [J] . WANG Ling-yun, ZHENG Ting-shun, LIU Xue-feng Transactions of Nonferrous Metals Society of China . 2001,第2期

机译：用人工神经网络预测高温下的流动应力
5. Prediction of Flow Stress in Ti-6Al-4V Alloy with Hydrogen at High Temperature using Artificial Neural Network [C] . Q. Wang, T. Wu, D. L. Sun, International Conference on Proceeding Manufacturing of Advanced Materials . 2006

机译：使用人工神经网络预测高温氢气的Ti-6AL-4V合金的预测
6. An Artificial Neural Networks based Temperature Prediction Framework for Network-on-Chip based Multicore Platform. [D] . Aswath Narayana, Sandeep. 2016

机译：基于人工神经网络的片上网络多核平台的温度预测框架。
7. MQL Strategies Applied in Ti-6Al-4V Alloy Milling—Comparative Analysis between Experimental Design and Artificial Neural Networks [O] . Nelson Wilson Paschoalinoto, Gilmar Ferreira Batalha, Ed Claudio Bordinassi, 2020

机译：实验设计与人工神经网络的Ti-6Al-4V合金铣削比较分析中的MQL策略
8. Prediction of Forming Limit Diagram for Ti-6Al-4V Alloy Using Artificial Neural Network [O] . Kotkunde Nitin, Deole Aditya D., Gupta Amit Kumar 2014

机译：基于人工神经网络的Ti-6Al-4V合金成形极限图预测

Prediction of Flow Stress in Ti-6Al-4V Alloy with Hydrogen at High Temperature using Artificial Neural Network

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