An artificial neural network approach to predict the relationship between the processing parameters and properties of TC21 titanium alloy

摘要：

This paper develops a three-layer back-propagation artificial neural network model to analyze and predict the correlation between processing parameters and properties of the damage tolerance type titanium alloy TC21. The inputs of the ANN are working temperatures, deformation extent, deformation rate and heat treatment conditions. And the outputs are mechanical properties namely ultimate strength, yield strength, elongation, reduction of area, plane strain fracture toughness and microstructure concerned parameters such as β phase fraction, β phase grain size, substructure length and thickness. The ANN is trained with experimental data and achieves a very good performance, which has already been applied to the optimization of processing for forging of aero-parts.

关键词： Titanium alloy Damage tolerance Back-propagation (BP) Artificial Neural Network (ANN) processing parameter

作者: M.H.Chen J.H.Li Z.S.Zhu

作者单位: School of Mechanical and Electrical Engineering,Nanjing University of Aeronautics and Astronautics,N Beijing Institute of Aeronautical Materials,Beijing 100095,P. R. China

会议类型: 国际会议

会议名称: The 3rd Conference of Cross-Strait Engineering Education and Ceeusro & 1st International Conference on Engineering Technologies and Ceeusro(ICETC2009)(第三届工程技术与产学研研讨会暨第一届国际功能制造技术学术会议)

会议地点: 常州

会议语种:英文

页码: 709-713

在线出版日期: 2009-11-19（万方平台首次上网日期，不代表论文的发表时间）