| Summary: | Abstract Aluminum alloys are widely used in transportation and aerospace industry because of their high strength-weight ratio and great formability. Compared with tuning grain refinement and solid solution, regulating precipitation strengthening through heat treatment is the most effective method to improve the yield strength of aluminum alloys. However, for the existing models, 3D precipitates are still simplified to 2D shape in aluminum alloys, and this trend causes the low accurate microstructure design. To address this issue, we develop a novel probability-dependent statistical model to predict the strength of aluminum alloys, considering the statistical distribution of the precipitate size and the relative spatial position of dislocations and precipitates. Compared with the classical model, the yield strength calculated from the current model is in good agreement with the experimental measurements, and the prediction accuracy is improved from 84.9% to 95.15%. In addition, the optimal size of precipitate is obtained for maximizing the strengthening effect. Our model not only provides a useful tool for the design of high-strength aluminum alloys, but also give a promising way to maximize the strength by changing the size distribution of the precipitate through a reasonable heat treatment process.
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