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.

中文翻译：

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用于 ICME 应用的多组分合金烧结的计算高效建模

使用烧结模型模拟多组分合金的致密化时的主要挑战是找到正确的传输参数，该参数不仅受温度的影响，还受化学成分和相分散的影响。提出了一种确定有效自扩散率的新方法，从而模拟烧结过程中工程合金的致密化。该方法将计算热力学和多组分合金中扩散控制转变的模拟与固态烧结的低阶模型相结合。计算热力学，使用 CALPHAD 方法，用于预测微观结构相稳定性，然后由扩散模拟模型用于评估烧结模型的有效传输特性。通过将沉淀硬化和奥氏体不锈钢合金在等速烧结过程中的致密化结果与文献数据进行比较，验证了建模方法。结果表明，该模型可以很好地捕捉实验观察结果。因此，建模方法可用于在集成计算材料工程 (ICME) 框架的背景下开发针对颗粒材料的有效搜索方法。