Modeling of constitutive relationship of Ti-25V-15Cr-0.2Si alloy during hot deformation process by fuzzy-neural network

被引：31

作者：

Han, Yuanfei ^{[1
]}

Zeng, Weidong ^{[1
]}

Zhao, Yongqing ^{[2
]}

Zhang, Xuemin ^{[2
]}

Sun, Yu ^{[1
]}

Ma, Xiong ^{[1
]}

机构：

[1] Northwestern Polytech Univ, State Key Lab Solidificat Proc, Xian 710072, Peoples R China

[2] NW Inst Nonferrous Met Res, Xian 710016, Peoples R China

来源：

MATERIALS & DESIGN | 2010年 / 31卷 / 09期

关键词：

Titanium alloy; Constitutive relationship; Fuzzy-neural network; Deformation behavior; TITANIUM-ALLOYS; FLOW-STRESS; MICROSTRUCTURE; PREDICTION; BEHAVIOR;

D O I：

10.1016/j.matdes.2010.03.047

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this paper, an adaptive fuzzy-neural network model has been established to model the constitutive relationship of Ti-25V-15Cr-0.2Si alloy during high temperature deformation. The network integrates the fuzzy inference system with a back-propagation learning algorithm of neural network. The experimental results were obtained at deformation temperatures of 900-1100 degrees C, strain rates of 0.01-10 s(-1), and height reduction of 50%. After the training process, the fuzzy membership functions and the weight coefficient of the network can be optimized. It has shown that the predicted values are in satisfactory agreement with the experimental results and the maximum relative error is less than 10%. It proved that the fuzzy-neural network was an easy and practical method to optimize deformation process parameters. (C) 2010 Elsevier Ltd. All rights reserved.

引用

页码：4380 / 4385

页数：6

共 25 条

[1]

ANASTASIOS PV, 2008, COMP MATER SCI, V43, P1086

[2] Modelling of the rheological behaviour of aluminium alloys in multistep hot deformation using the multiple regression analysis and artificial neural network techniques [J].

Bruni, C. ;

Forcellese, A. ;

Gabrielli, E. ;

Simoncini, M. .

JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2006, 177 (1-3) :323-326

[3] Incremental learning of dynamic fuzzy neural networks for accurate system modeling [J].

Deng, Xingsheng ;

Wang, Xinzhou .

FUZZY SETS AND SYSTEMS, 2009, 160 (07) :972-987

[4] Application of evolving Takagi-Sugeno fuzzy model to nonlinear system identification [J].

Du, Haiping ;

Zhang, Nong .

APPLIED SOFT COMPUTING, 2008, 8 (01) :676-686

[5] New nested adaptive neural networks (NANN) for constitutive modeling [J].

Ghaboussi, J ;

Sidarta, DE .

COMPUTERS AND GEOTECHNICS, 1998, 22 (01) :29-52

[6] A comparative study of some network approaches to predict the effect of the reinforcement content on the hot strength of Al-base composites [J].

Jalham, IS .

JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2005, 166 (03) :392-397

[7] Prediction of the flow stress of 6061 Al-15% SiC - MMC composites using adaptive network based fuzzy inference system [J].

Kalaichelvi, V. ;

Sivakumar, D. ;

Karthikeyan, R. ;

Palanikumar, K. .

MATERIALS & DESIGN, 2009, 30 (04) :1362-1370

[8] Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems [J].

Kasabov, NK .

FUZZY SETS AND SYSTEMS, 1996, 82 (02) :135-149

[9] Prediction of flow stress for carbon steels using recurrent self-organizing neuro fuzzy networks [J].

Kumar, Shashi ;

Kumar, Sanjeev ;

Prakash ;

Shankar, Ravi ;

Tiwari, M. K. ;

Kumar, Shashi Bhushan .

EXPERT SYSTEMS WITH APPLICATIONS, 2007, 32 (03) :777-788

[10] Application of neural networks to predict the elevated temperature flow behavior of a low alloy steel [J].

Lin, Y. C. ;

Zhang, Jun ;

Zhong, Jue .

COMPUTATIONAL MATERIALS SCIENCE, 2008, 43 (04) :752-758

← 1 2 3 →