Modeling of the Hot Flow Behaviors for Ti-6Al-4V-0.1Ru Alloy by GA-BPNN Model and Its Application
| Autor: | Lai Jiang, Yuting Zhou, Yu-feng Xia, Shuai Long, Dong Yang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
010302 applied physics
Materials science Artificial neural network Alloy 02 engineering and technology engineering.material 021001 nanoscience & nanotechnology Condensed Matter Physics 01 natural sciences Flow (mathematics) Mechanics of Materials 0103 physical sciences Genetic algorithm engineering General Materials Science Ti 6al 4v Physical and Theoretical Chemistry 0210 nano-technology Biological system |
| Zdroj: | High Temperature Materials and Processes. 37:551-562 |
| ISSN: | 2191-0324 0334-6455 |
| Popis: | A series of compression tests were performed on Ti-6Al-4V-0.1Ru titanium alloy in nine temperatures between 750 and 1150 °C and a strain rate range of 0.01 to 10s−1. The hot deformation behaviors of Ti-6Al-4V-0.1Ru showed highly non-linear intrinsic relationships with temperature, strain and strain rate. The flow curves exhibited different softening mechanisms, dynamic recrystallization (DRX) and dynamic recovery (DRV). In this study, the rheological behaviors of Ti-6Al-4V-0.1Ru were modeled using a special hybrid prediction model, where genetic algorithm (GA) was implemented to do a back-propagation neural network (BPNN) weights optimization, namely GA-BPNN. Subsequently, the predicted results were compared with experimental values and GA-BPNN model showed the ability to predict the flow behaviors of Ti-6Al-4V-0.1Ru with superior accuracy. Then a 3-D continuous interaction space was constructed to visually reveal the successive relationships among processing parameters. Finally, the predicted data were applied to process simulation and accuracy results were achieved. |
| Databáze: | OpenAIRE |
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