Diffusive and massive phase transformations in Ti–Al–Nb alloys – Modelling and experiments
Source abstract
Abstract
Abstract The thermodynamic properties of the Ti–Al–Nb system are obtained from recently published thermodynamic assessments. Based on these data the phase boundaries of the (α-Ti + γ-TiAl) two phase region are calculated by utilizing the CALPHAD approach and are compared to those, obtained by ab-initio calculations. It is found that the ab-initio phase boundaries deviate significantly from those based on the CALPHAD fit to experimental data which can be rationalized by the lack of vibrational entropy contributions in the present approach. Consequently a thermodynamic description based on the CALPHAD approach is used to further investigate the kinetics of the massive α → γ m phase transformation in the Ti–Al–Nb system by means of a recently developed thick-interface model. Simulation of the transformation kinetics results in a massive transformation in the single-phase region only. However, very thin mole fraction spikes are obtained due to comparatively high interface velocities. It is likely that these spikes cannot be fully developed in experiments meaning that diffusion processes are partly suppressed (quasi-diffusionless transformation). A massive transformation in the two-phase region would then be possible. The theoretical predictions are compared to experimental studies performed on a Ti–45Al–5Nb alloy (composition in atomic percent). The alloy is heat treated slightly above the α-transus temperature and subsequently oil quenched to room temperature to generate γ m –α 2 interfaces. Energy-dispersive X-ray spectroscopy measurements were performed across γ m –α 2 interfaces in a scanning transmission electron microscope to search for chemical spikes.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Intermetallics and Advanced Alloy Properties · MXene and MAX Phase Materials · Semiconductor materials and interfaces
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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