高熵合金是以多種主元素混合的新型合金，其中最具代表性的為Cantor合金，由等原子CoCrFeNiMn五種主要元素組成，此合金雖由多種元素組成，卻可以形成簡單的固溶相，成為近年來熱門的研究項目，研究發現CoCrFeNiMn合金在特定熱處理條件下會有析出物產生，使合金具備更高的強度及良好的硬化性能，學者們也開始針對其不同元素成分含量、不同熱處理過程、不同添加物進行各種不同的研究，以提升合金析出強化效果。
CoCrFeNiMn合金的強化機制主要源自於析出物與差排間的交互作用，因此，本研究中採用一套基於差排密度的晶體塑性有限元素模型，用以描述合金中因析出物而發生的強化機制，利用多晶代表性體積元素模型，針對非等原子CoCrFeMnNi合金進行拉伸模擬分析，並導入能夠考慮微觀下差排作用及析出物效應的加工硬化模型，用以描述合金中因析出物效應而產生的強化機制。
為了更好的模擬出析出物效應對CoCrFeMnNi合金的影響，我們利用文獻中取得的合金拉伸實驗的數據及SEM-BSE圖，進行TEM分析以得到析出物平均尺寸，並考慮析出物顆粒分布為高斯分布的情況下計算出相關參數，將其與ImageJ圖像分析所得析出物實際分布計算的結果做比較，利用參數模擬反映出應力應變曲線加工硬化行為及析出物對差排的影響，以探討不同的退火溫度下考慮不同析出物顆粒分布的差排密度增長，並解釋材料硬化行為。
最後，調整高斯分布的標準差擬合實驗數據的應力應變曲線，並觀察其拉伸時的微觀變化，並特別討論差排、析出強化效應和析出物顆粒分佈對CoCrFeMnNi合金的影響，利用模擬結果的參數值，迴歸出預測方程式，預測出沒有實驗數據之不同退火溫度下的應力應變曲線，以減少實驗成本並提供合金熱處理設計的參考。

In this study, a set of crystal plasticity finite element methods was established using computational mechanics. We performed tensile simulation analysis of anisoatomic CoCrFeMnNi alloys using a polycrystalline representative volume element model. This simulation is imported into a hardening model that can consider microscopic dislocation and precipitate effects to describe the precipitates strengthening mechanism in the alloy, to be closer to the real mechanical behavior. In previous studies, in order to simplify the analysis we assumed that each precipitate particle was the same size for the calculation, but this approach led to an overestimation of the strength of the alloy. To better express the actual microstructure of the alloy, we consider the particle size distribution of precipitate as Gaussian distribution.
We used the experimental data and SEM-BSE image of the CoCrFeMnNi alloy obtained in the literature, and the data of the precipitates can be obtained by TEM analysis. The parameters related to the precipitates are calculated considering that the particle size distribution of the precipitates is Gaussian distribution. Then, we compare the simulation results with the results of the real distribution obtained by the ImageJ image analysis, and use the parameters to reflect the work hardening behavior of the alloy and the effect of precipitates on dislocation, to explore the dislocation density growth considering different particle size distributions at different annealing temperatures, and to explain the material hardening behavior.
Finally, stress-strain curves are fitted by adjusting the standard deviation of the Gaussian distribution and the prediction equations are regressed using the parameter values of the simulation results that are closest to the experimental stress-strain curves to predict in the absence of experimental stress-strain curve. Using this simulation method can reduce the experimental cost and provide a reference for alloy heat treatment design.

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