The major challenge while using sintering models for simulation of densification in multi-component alloys is finding the correct transport parameters, which are affected by not only temperature but also chemical composition and phase dispersion. A novel approach for determining the effective self-diffusivity and hence modeling the densification of engineering alloys during sintering is proposed. The approach integrates computational thermodynamics and simulation of diffusion-controlled transformations in multi-component alloys together with a low-order model for solid-state sintering. Computational thermodynamics, using the CALPHAD method, is used to predict microstructural phase stability, which is then used by diffusion simulation models to evaluate the effective transport properties for the sintering model. The modeling approach is validated by comparing results for densification of precipitation-hardened and austenitic stainless-steel alloys during an iso-rate sintering schedule with data from the literature. It is shown that the model can capture experimental observations very well. The modeling approach can thus be used in the development of an efficient search methodology for particulate materials within the context of an integrated computational materials engineering (ICME) frameworks.
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This work was funded by The University of Melbourne.
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Manuscript submitted June 12, 2019.
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Molla, T.T., Liu, J.Z. & Schaffer, G.B. Computational Efficient Modeling of Sintering in Multi-component Alloys for ICME Applications. Metall and Materi Trans B 51, 54–60 (2020). https://doi.org/10.1007/s11663-019-01755-1