Modelling beta transus temperature of titanium alloys using artificial neural network

被引：100

作者：

Guo, Z ^{[1
]}

Malinov, S ^{[1
]}

Sha, W ^{[1
]}

机构：

[1] Queens Univ Belfast, Metals Res Grp, Sch Civil Engn, Belfast BT9 5AG, Antrim, North Ireland

来源：

COMPUTATIONAL MATERIALS SCIENCE | 2005年 / 32卷 / 01期

基金：

英国工程与自然科学研究理事会;

关键词：

titanium alloys; beta transus temperature; neural network;

D O I：

10.1016/j.commatsci.2004.05.004

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

An artificial neural network (ANN) model is developed to simulate the non-linear relationship between the beta transus (beta(tr)) temperature of titanium alloys and the alloy chemistry. The input parameters to the model consist of the concentration of nine elements, i.e. Al, Cr, Fe, Mo, Sri, Si, V, Zr and O, whereas the model output is the beta(tr) temperature. Good performance of the ANN model was achieved. The interactions between the alloying elements were estimated based on the obtained ANN model. The results showed good agreement with experimental data. The influence of the database scale on ANN model performance was also discussed. Estimation of beta(tr) temperature through thermodynamic calculation was carried out as a comparison. (C) 2004 Elsevier B.V. All rights reserved.

引用

页码：1 / 12

页数：12