Numerical Description of Hot Flow Behaviors at Ti-6Al-2Zr-1Mo-1V Alloy By GA-SVR and Relative Applications
| Autor: | Guo-Zheng Quan, Zhi-hua Zhang, Yuting Zhou, Tong Wang, Yu-feng Xia |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: | |
| Zdroj: | Materials Research, Vol 19, Iss 6, Pp 1253-1269 (2016) |
| Druh dokumentu: | article |
| ISSN: | 1516-1439 1980-5373 |
| DOI: | 10.1590/1980-5373-mr-2016-0280 |
| Popis: | Hot compression tests of as-cast Ti-6Al-2Zr-1Mo-1V alloy in a wide temperature range of 1073-1323 K and strain rate range of 0.01-10 s-1 were conducted by a servo-hydraulic and computer-controlled Gleeble-1500 machine. The hot flow behaviors of Ti-6Al-2Zr-1Mo-1V alloy show highly non-linear relationships with strain, strain rate and temperature. In order to accurately and effectively characterize the complex flow behaviors, support vector regression (SVR) which is a machine learning method was combined with Genetic Algorithm (GA) to characterize the flow behaviors, namely, the GA-SVR. The study abilities, generation abilities, and modeling efficiencies of the improved Arrhenius-type constitutive model, ANN, and GA-SVR for flow behaviors of as-cast Ti-6Al-2Zr-1Mo-1V alloy were detailedly compared. Comparison results show that the study ability of the GA-SVR is as strong as the ANN. The generation abilities and modeling efficiencies of these models were shown as follows in ascending order: the improved Arrhenius-type constitutive model < ANN < GA-SVR. Based on the established GA-SVR, the continuously three-dimensional relationships among flow stress, temperature, strain, and strain rate were constructed, which improve the simulation accuracy and related research fields where stress-strain data play important roles. |
| Databáze: | Directory of Open Access Journals |
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