| COMPUTATIONAL SIMULATION |
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Flow Stress Prediction Model and Processing Map of Mg-Sm-Zn-Zr Alloy
Based on GA-BP Neural Network |
| CHANG Ruohan1, CAI Zhongyi1, CHENG Liren2, CHE Chaojie1, CHI Jiaxuan1
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1 Roll Forging Research Institute, Jilin University, Changchun 130025;
2 State Key Laboratory of Rare Earth Resources
Utilization, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022 |
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Abstract The flow stress behavior of Mg-Sm-Zn-Zr alloy was studied by isothermal compression experiment on Gleeble-1500D thermal-mechanical test machine at deformation temperatures of 350-450 ℃ and strain rates of 0.001-1 s-1. The genetic algorithm BP neural network (GA-BP) was developed to predict the flow stress, and the comparative study on GA-BP model and strain compensated Arrhenius-type constitutive model was presented. Based on the prediction stress, the processing map was established under instability criteria of Murthy, finally the rationality of the designed processing map was verified by microstructure. The results showed that the correlation coefficient was 0.999 and the average relative error was 1.469% for the GA-BP model, which indicated that the GA-BP model could be more accurate in predicting the flow stress than constitutive model considering the compensation of strain. The processing map was properly designed, and the map confirmed the temperatures of 400-450 ℃ and strain rates of 0.001-0.03 s-1 as the optimum process parameters. The dynamic recrystallization (DRX) occurred in the deformed samples under the above parameters.
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Published: 02 May 2018
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2017, Vol. 31 
