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Lossless multi-scale constitutive elastic relations with artificial intelligence

npj Computational Materials 2022年第1期8卷 638-649页

作者： jaber rezaei mianroodi Shahed rezaei Nima H.Siboni Bai-Xiang Xu Dierk Raabe Microstructure Physics and Alloy Design Max-Planck-Institut für EisenforschungDüsseldorfGermany Ergodic Labs Lohmühlenstraße 6512435BerlinGermany Mechanics of Functional Materials Division Institute of Materials ScienceTechnische Universität DarmstadtDarmstadtGermany

A seamless and lossless transition of the constitutive description of the elastic response of materials between atomic and continuum scales has been so far *** we show how this problem can be overcome by using artificial intelligence(AI).A convolutional neural network(CNN)model is trained,by taking the structure image of a nanoporous material as input and the corresponding elasticity tensor,calculated from molecular statics(MS),as *** with the atomistic data,the CNN model captures the size-and pore-dependency of the material’s elastic properties which,on the physics side,derive from its intrinsic stiffness as well as from surface relaxation and non-local *** demonstrate the accuracy and the efficiency of the trained CNN model,a finite element method(FEM)-based result of an elastically deformed nanoporous beam equipped with the CNN as constitutive law is compared with that obtained by a full atomistic *** trained CNN model predicts the elasticity tensor in the test dataset with a root-mean-square error of 2.4 GPa(3.0%of the bulk modulus)when compared to atomistic *** the other hand,the CNN model is about 230 times faster than the MS calculation and does not require changing simulation methods between different *** efficiency of the CNN evaluation together with the preservation of important atomistic effects makes the trained model an effective atomistically informed constitutive model for macroscopic simulations of nanoporous materials,optimization of nanostructures,and the solution of inverse problems.

关键词： constitutive artificial elasticity

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Modeling and simulation of microstructure in metallic systems based on multi-physics approaches

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npj Computational Materials 2022年第1期8卷 867-881页

作者： jaber rezaei mianroodi Pratheek Shanthraj Chuanlai Liu Samad Vakili Sharan Roongta Nima Hamidi Siboni Nathan Perchikov Yang Bai Bob Svendsen Franz Roters Dierk Raabe Martin Diehl Microstructure Physics and Alloy Design Max-Planck-Institut für Eisenforschung40237DüsseldorfGermany Ergodic Labs Lohmühlenstraße12435BerlinGermany The Department of Materials The University of ManchesterManchesterM139PLUK Material Mechanics RWTH Aachen University52062AachenGermany Department of Computer Science KU LeuvenCelestijnenlaan 200A3001LeuvenBelgium Department of Materials Engineering KU LeuvenKasteelpark Arenberg 443001LeuvenBelgium

The complex interplay between chemistry,microstructure,and behavior of many engineering materials has been investigated predominantly by experimental *** to the increase in computer power,advances in computational modeling methods have resulted in a level of sophistication which is comparable to that of *** the continuum level,one class of such models is based on continuum thermodynamics,phase-field methods,and crystal plasticity,facilitating the account of multiple physical mechanisms (multi-physics) and their interaction during microstructure *** paper reviews the status of simulation approaches and software packages in this field and gives an outlook towards promising research directions.

关键词： computer microstructure metallic

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Teaching solid mechanics to artificial intelligence—a fast solver for heterogeneous materials

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npj Computational Materials 2021年第1期7卷 899-908页

作者： jaber rezaei mianroodi Nima H.Siboni Dierk Raabe Microstructure Physics and Alloy Design Max-Planck-Institut für EisenforschungDüsseldorfGermany DeepMetis BerlinGermany

We propose a deep neural network(DNN)as a fast surrogate model for local stress calculations in inhomogeneous non-linear *** show that the DNN predicts the local stresses with 3.8%mean absolute percentage error(MAPE)for the case of heterogeneous elastic media and a mechanical contrast of up to factor of 1.5 among neighboring domains,while performing 103 times faster than spectral *** DNN model proves suited for reproducing the stress distribution in geometries different from those used for *** the case of elasto-plastic materials with up to 4 times mechanical contrast in yield stress among adjacent regions,the trained model simulates the micromechanics with a MAPE of 6.4%in one single forward evaluation of the network,without any *** results reveal an efficient approach to solve non-linear mechanical problems,with an acceleration up to a factor of 8300 for elastic-plastic materials compared to typical solvers.

关键词： artificial network faster

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

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npj Computational Materials 2023年第1期9卷 1981-1990页

作者： Mohammad S.Khorrami jaber R.mianroodi Nima H.Siboni Pawan Goyal Bob Svendsen Peter Benner Dierk Raabe Microstructure Physics and Alloy Design Max-Planck-Institut für EisenforschungDüsseldorfGermany Ergodic Labs Lohmühlenstraße 6512435 BerlinGermany Computational Methods in Systems and Control Theory Max Planck Institute for Dynamics of Complex Technical SystemsMagdeburgGermany Material Mechanics RWTH Aachen UniversityAachenGermany

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 *** 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 *** 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 *** of the tCNN to test cases based on microstructure morphologies and boundary conditions not contained in the training dataset is also investigated and discussed.

关键词： methods boundary faster

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