Prediction of flow stress of Ti-15-3 alloy with artificial neural network

被引:0
作者
李萍
单德彬
薛克敏
吕炎
许沂
机构
关键词
artificial neural network; Ti; 15; 3; alloy; flow stress;
D O I
暂无
中图分类号
TG302 [压力加工设计与计算];
学科分类号
080201 ; 080503 ;
摘要
Hot compression experiments were conducted on Ti 15 3 alloy specimens using Gleeble 1500 Thermal Simulator.These tests were focused to obtain the flow stress data under various conditions of strain,strain rate and temperature. On the basis of these data, the predicting model for the nonlinear relation between flow stress and deformation strain,strain rate and temperature for Ti 15 3 alloy was developed with a back propagation artificial neural network method. Results show that the neural network can reproduce the flow stress in the sampled data and predict the nonsampled data well. Thus the neural network method has been verified to be used to tackle hot deformation problems of Ti 15 3 alloy. [
引用
收藏
页码:95 / 97
页数:3
相关论文
共 9 条
  • [1] ZHANG Xing-quan,PENG Ying-hong and RUAN Xueyu.A eigen-constitution relationship model of Ti-17 alloy based on artificial neural network. The Chinese Journal of Nonferrous Metals . 1999
  • [2] Rao K P and Prasad Y K D V.Neural network approach to flow stress evaluation in hot deformation. Journal of Materials . 1995
  • [3] HAN Xu,ZHANG Qi-zhi and TONG Qing-li.Study on critical deposition velocity of slurry flow in large pipe by artificial neural network. The Chinese Journal of Nonferrous Metals . 1997
  • [4] FENG Xia-ting,Webber S,Ozboy M U,et al.Neural network assessment of rockburst risks for deep gold mines in south africa. Transactions of Nonferrous Metals Society of China . 1998
  • [5] Takuda H,Fujimoto H and hatta N.Modeling on flow stress of Mg-Al-Zn alloys at elevated temperature. Journal of Materials . 1998
  • [6] EzugwuEQ,WangZM.Titaniumalloysandtheirmachinability—areview. JournalofMaterialsPro cessingTechnology . 1997
  • [7] WANG Xue-ye,QIU Guan-zhou and WANG Dian-zuo.Molten salt phase diagrams calculation using artificial neural network or pattern recognition bond parameters. Transactions of Nonferrous Metals Society of China . 1998
  • [8] LIU Qing-bin,JI Zhong,LIU Ma-bao,et al.Acquiring the eigen-constitution relationship for a thermal viscoplastic material using an artificial neural network. Journal of Materials . 1996
  • [9] QI Le-hua,HOU Jun-jie,YANG Fang,et al.Application of artificial neural network in process of direct extrusion during solidification of liquid metal. The Chinese Journal of Nonferrous Metals . 1999