@inproceedings{279a22ef06e04b6da7a4c94334d50513,
title = "Flow stress prediction of high-Nb TiAl alloys under high temperature deformation",
abstract = "In this work, a back propagation artificial neural network (BP-ANN) model is conducted to predict the flow behaviors of high-Nb TiAl (TNB) alloys during high temperature deformation. The inputs of the neural network are deformation temperature, log strain rate and strain whereas flow stress is the output. There is a single hidden layer with 7 neutrons in the network, and the weights and bias of the network were optimized by Genetic Algorithm (GA). The comparison result suggests a very good correlation between experimental and predicted data. Besides, the non-experimental flow stress predicted by the network is shown to be in good agreement with the results calculated by three dimensional interpolation, which confirmed a good generalization capability of the proposed network.",
keywords = "Artificial neutral network, Flow stress, Genetic Algorithm, High-Nb TiAl alloys, Hot deformation",
author = "Liang Cheng and Hui Chang and Bin Tang and Hongchao Kou and Jinshan Li",
year = "2012",
doi = "10.4028/www.scientific.net/AMR.510.723",
language = "英语",
isbn = "9783037854099",
series = "Advanced Materials Research",
pages = "723--728",
booktitle = "Machinery, Materials Science and Engineering Applications",
note = "2012 2nd International Conference on Machinery, Materials Science and Engineering Applications, MMSE 2012 ; Conference date: 16-06-2012 Through 17-06-2012",
}