基于模糊神经网络的TC4-DT钛合金高温变形本构关系模型(英文)  

Modeling the High Temperature Deformation Constitutive Relationship of TC4-DT Alloy Based on Fuzzy-neural Network

作　　者：唐波[1] 唐斌[1] 李金山[1] 张丰收[2] 杨冠军[1] 

机构地区：[1]西北工业大学凝固技术国家重点实验室,陕西西安710072 [2]西部超导材料科技股份有限公司,陕西西安710018

出　　处：《稀有金属材料与工程》2013年第7期1347-1351,共5页Rare Metal Materials and Engineering

基　　金：National Natural Science Foundation of China (50824001)

摘　　要：通过分析研究变形温度、应变速率及变形程度参数对TC4-DT钛合金高温变形行为的影响，建立了一种基于自适应模糊神经网络的TC4-DT钛合金高温变形本构关系预测模型。高温变形热模拟压缩试验的变形温度为750～1150℃，应变率为O．001～10s^（-1），试样高度压缩率为50％。本研究中建立的网络模型集成了模糊推理系统误差反向传播（BP）神经网络的学习算法。结果表明，该模型的预测值与实验结果比较吻合，最大相对误差小于6％。本研究证明模糊神经网络是一种优化TC4-DT钛合金本构关系模型和优化变形工艺参数的有效、实用方法。By analyzing the high temperature TC4-DT titanium alloys＇ deformation temperature, strain rate and deformation degree with the parameters of the experimental data flow stress, an adaptive fuzzy-neural network model has been established to predict flow stress data to model the high temperature deformation constitutive relationship of TC4-DT titanium alloy. The experimental results were obtained at deformation temperature of 750-1150 ℃, strain rates of 0.001- 10 s1, and height reduction of 50%. The network integrates the fuzzy inference system with a back-propagation （BP） learning algorithm of neural network. Results show that the predicated values are in satisfactory agreement with the experimental results and the maximum relative error is less than 6%. It proves that the fuzzy-neural network is a very effective and practical method to achieve more optimized TC4 - DT titanium alloy constitutive relation model and optimize deformation process parameters.

关 键 词：TC4-DT钛合金 模糊神经网络 本构关系 

分 类 号：TG146.23[一般工业技术—材料科学与工程] TP183[金属学及工艺—金属材料]