An Artificial Neural Network Approach to Predict the Relationship between the Processing Parameters and Properties of TC21 Titanium Alloy
| Periodical | Key Engineering Materials (Volumes 426 - 427) |
|---|---|
| Main Theme | Functional Manufacturing Technologies and Ceeusro I |
| Edited by | Dunwen Zuo, Hun Guo, Guoxing Tang, Weidong Jin, Chunjie Liu and Chun Su |
| Pages | 709-713 |
| DOI | 10.4028/www.scientific.net/KEM.426-427.709 |
| Citation | M.H. Chen et al., 2010, Key Engineering Materials, 426-427, 709 |
| Online since | January, 2010 |
| Authors | M.H. Chen, J.H. Li, Zhi Shou Zhu |
| Keywords | Artificial Neural Network (ANN), Back-Propagation, Damage Tolerance, Process Parameter, Titanium Alloy |
| Price | US$ 28,- |
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, βphase 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.
