کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1559863 1513892 2016 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
3D connectivity of eutectic Si as a key property defining strength of Al–Si alloys
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مکانیک محاسباتی
پیش نمایش صفحه اول مقاله
3D connectivity of eutectic Si as a key property defining strength of Al–Si alloys
چکیده انگلیسی


• Different Si morphologies are generated using stochastic microstructure model.
• Effectiveness of combining stochastic model and FEM simulations is shown.
• Strength of the material increases with the connectivity and the branching of Si.
• Strength of the material increases with the number of Si particles.
• Euler number is an effective measure of connectivity.

The relationship between microstructure and mechanical behavior of the eutectic phase in hypoeutectic Al–Si alloys is analyzed empirically using two experimental and thirteen synthetic microstructures. For all microstructures, a morphological analysis is combined with mechanical stress–strain simulations performed via finite element method (FEM). The synthetic microstructures are generated by a stochastic microstructure model that gives a realistic description of the eutectic Si in Al–Si alloys. The stochastic model was developed on the basis of a 3D image of a real Sr-modified Al–Si alloy and is used to generate a large variety of virtual 3D structures of eutectic Si that differ from each other by the number of Si particles, their degree of branching, and connectivity. In the simulation study, it is shown that highly connected and branched morphologies of Si are beneficial to the strength of the material. Besides, when the connectivity of Si is low, i.e. when an Al matrix is reinforced by discrete (disconnected) particles of Si, the strength of the material increases with the number of those particles. The Euler number is shown to be highly effective in characterizing the connectivity and is closely related to the strength of the material.

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ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Materials Science - Volume 120, July 2016, Pages 99–107
نویسندگان
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