Abstract
The intrinsic relationships between deforming parameters and microstructural mechanisms for Ti-6Al-4V alloy were analyzed by processing maps. A series of thermal compression tests were carried out in the temperatures range of 1023~1323 K (across β-transus) and strain rates range of 0.01~10 s-1 on a Gleeble-3500 thermo-mechanical simulator. Based on the stress-strain data collected from compression tests, a back-propagation artificial neural network (BP-ANN) model was developed, which presents reliable performance in tracking and predicting strain-stress data. By utilizing this model, the volume of stress-strain data was expanded. According to the intensive stress-strain data, the apparent activation energy was calculated to be 564.05 kJ mol-1 and 300.20 kJ mol-1 for α+β-phase field and single β-phase field, respectively. Moreover, the processing maps were constructed at finer intervals of temperature, from which, the stable regions with higher power dissipation efficiency (η > 0.3) and unstable regions with negative instability parameter (ξ < 0) were clarified clearly. By combining processing map with microstructure observations, two main stable softening mechanisms, i.e., globularization and dynamic recovery (DRV) were identified, and globularization-predominant (0.3 < η < 0.55) parameter domain (\(\dot \varepsilon \) < 0.1 s-1) in α+β-phase field and DRV-predominant (0.25 < η < 0.41) parameter domain (0.032 s-1 \(\dot \varepsilon \)<1 s-1) in β-phase field were recommended.
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Quan, Gz., Wen, Hr., Jia-Pan et al. Construction of processing maps based on expanded data by BP-ANN and identification of optimal deforming parameters for Ti-6Al-4V alloy. Int. J. Precis. Eng. Manuf. 17, 171–180 (2016). https://doi.org/10.1007/s12541-016-0022-z
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DOI: https://doi.org/10.1007/s12541-016-0022-z