An artificial neural network for surrogate modeling of stress fields in viscoplastic polycrystalline materials

التفاصيل البيبلوغرافية
العنوان:	An artificial neural network for surrogate modeling of stress fields in viscoplastic polycrystalline materials
المؤلفون:	Mohammad S. Khorrami, Jaber R. Mianroodi, Nima H. Siboni, Pawan Goyal, Bob Svendsen, Peter Benner, Dierk Raabe
المصدر:	npj Computational Materials, Vol 9, Iss 1, Pp 1-10 (2023)
بيانات النشر:	Nature Portfolio, 2023.
سنة النشر:	2023
المجموعة:	LCC:Materials of engineering and construction. Mechanics of materials LCC:Computer software
مصطلحات موضوعية:	Materials of engineering and construction. Mechanics of materials, TA401-492, Computer software, QA76.75-76.765
الوصف:	Abstract The purpose of this work is the development of a trained artificial neural network for surrogate modeling of the mechanical response of elasto-viscoplastic grain microstructures. To this end, a U-Net-based convolutional neural network (CNN) is trained using results for the von Mises stress field from the numerical solution of initial-boundary-value problems (IBVPs) for mechanical equilibrium in such microstructures subject to quasi-static uniaxial extension. The resulting trained CNN (tCNN) accurately reproduces the von Mises stress field about 500 times faster than numerical solutions of the corresponding IBVP based on spectral methods. Application of the tCNN to test cases based on microstructure morphologies and boundary conditions not contained in the training dataset is also investigated and discussed.
نوع الوثيقة:	article
وصف الملف:	electronic resource
اللغة:	English
تدمد:	2057-3960
Relation:	https://doaj.org/toc/2057-3960
DOI:	10.1038/s41524-023-00991-z
URL الوصول:	https://doaj.org/article/dd6344fcbb0b40e585ce83212f4777ba
رقم الانضمام:	edsdoj.6344fcbb0b40e585ce83212f4777ba
قاعدة البيانات:	Directory of Open Access Journals

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الوصف
تدمد:	20573960
DOI:	10.1038/s41524-023-00991-z