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Lossless multi-scale constitutive elastic relations with artificial intelligence

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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 elusive.Here 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 output.Trained 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 effects.To 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 simulation.The 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 calculations.On the other hand,the CNN model is about 230 times faster than the MS calculation and does not require changing simulation methods between different scales.The 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

TEXTURE AND CRYSTALLINITY EVOLUTION IN ISOTACTIC POLYPROPYLENE INDUCED BY ROLLING AND THEIR INFLUENCE ON MECHANICAL PROPERTIES

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Chinese Journal of Polymer Science 2006年第4期24卷 403-411页

作者： dierk raabe 贾涓 Max-Planck-Institut für Eisenforschung Max-Planck-Str.1 Düsseldorf 40237Germany Department of Materials University of Science and Technology Beijing Beijing 100083 China Max-Planck-Institutfiir Eisenforschung Max-Planck-Str. 1 Dusseldorf 40237 Germany

The orientation and crystallinity evolution of isotactic polypropylene （iPP） induced by rolling were studied using wide angle X-ray scattering with an area detector. The tensile mechanical properties of rolled isotactic polypropylene sheets were also measured in this work. The texture component method was used to analyze the rolling texture. The rolling texture consists mainly of （010）[001], （130）[001] and [001]//RD fiber components in the sample with a rolling true strain of 1.5. The results reveal that crystallinity drastically decreases during rolling. It is suggested that amorphization is a deformation mechanism which takes place as an alternative to crystallographic intralamellar slip depending on the orientation of the lamellae. Both the orientation and crystallinity affect the tensile mechanical properties of rolled polypropylene. Crystallinity influences the elastic modulus on both directions and yield strength on transverse direction at the first stage of deformation. Orientation is the main reason for the changes of mechanical properties, especially at the latter part of deformation. The changes of both tensile strength and elongation percentage on rolling direction are larger than those on transverse direction, which results from the orientation. At last, the anisotropic mechanical properties occur on the rolling and transverse direction： high tensile strength with low elongation percentage on rolling direction and low tensile strength with high elongation percentage on transverse direction.

关键词： Isotactic polypropylene （iPP） Texture Crystallinity Orientation Mechanical properties.

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 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.

关键词： methods boundary faster

Changes of crystallinity and spherulite morphology in isotactic polypropylene after rolling and heat treatment

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Journal of University of Science and Technology Beijing 2008年第4期15卷 514-520页

作者： Juan Jia Weimin Mao dierk raabe School of Materials Science and Engineering University of Science and Technology Beijing Beijing 100083 China Max-Planck-Institut fur Eisenforschung Max-Planck-Str. 1 Düsseldorf 40237 Germany

The spherulite morphology of the rolled and subsequent heat-treated isotactic polypropylene （iPP） was observed by polarized microscopy, and the crystallinity evolution of materials was also measured by the wide angle X-ray scattering （WAXS）. Rolling led to the oblate spherulites in the deformed iPP samples. The sheared crystalline lamellae broke apart into sets of crystalline blocks during rolling. As a result, the crystallinity of the iPP samples was greatly reduced during deformation, which induced the unclear spherulites and spherulite boundaries. Subsequent heat treatment resulted in the strong reerystallization of the rolled iPP samples. But the recrystaUizatinn in this work only meant the rearrangement of the macromolecule along the unbroken crystalline lamellae and the existing small crystalline blocks in the deformed spherulites. Heat treatment did not change the shape of the spherulites formed during deformation. The recrystallization also resulted in very clear spherulites and spherulite boundaries.

关键词： isotaetic polypropylene （iPP） rolling heat treatment X-ray diffraction crystallinity spherulites

Hydrogen-based direct reduction of iron oxide at 700℃:Heterogeneity at pellet and microstructure scales

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International Journal of Minerals,Metallurgy and Materials 2022年第10期29卷 1901-1907页

作者： Yan Ma Isnaldi R.Souza Filho Xue Zhang Supriya Nandy Pere Barriobero-Vila Guillermo Requena Dirk Vogel Michael Rohwerder Dirk Ponge Hauke Springer dierk raabe Max-Planck-Institut für Eisenforschung Max-Planck-Straße 140237 DüsseldorfGermany Corrosion Center Institute of Metal ResearchChinese Academy of SciencesShenyang 110016China Institute of Materials Research German Aerospace Center(DLR)Linder Höhe51147 CologneGermany Lehr-und Forschungsgebiet Metallische Strukturen und Werkstoffsysteme für die Luft-und Raumfahrt RWTH Aachen University52062 AachenGermany Institut für Bildsame Formgebung RWTH Aachen UniversityIntzestraße 1052072 AachenGermany

Steel production causes a third of all industrial CO_(2) emissions due to the use of carbon-based substances as reductants for iron ores,making it a key driver of global warming.Therefore,research efforts aim to replace these reductants with sustainably produced hydrogen.Hydrogen-based direct reduction(HyDR)is an attractive processing technology,given that direct reduction(DR)furnaces are routinely operated in the steel industry but with CH_(4) or CO as reductants.Hydrogen diffuses considerably faster through shaft-furnace pellet agglomerates than carbon-based reductants.However,the net reduction kinetics in HyDR remains extremely sluggish for high-quantity steel production,and the hydrogen consumption exceeds the stoichiometrically required amount substantially.Thus,the present study focused on the improved understanding of the influence of spatial gradients,morphology,and internal microstructures of ore pellets on reduction efficiency and metallization during HyDR.For this purpose,commercial DR pellets were investigated using synchrotron high-energy X-ray diffraction and electron microscopy in conjunction with electron backscatter diffraction and chemical probing.Revealing the interplay of different phases with internal interfaces,free surfaces,and associated nucleation and growth mechanisms provides a basis for developing tailored ore pellets that are highly suited for a fast and efficient HyDR.

关键词： hydrogen-based direct reduction iron oxide microstructure spatial gradient metallization

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 materials.We 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 solvers.The DNN model proves suited for reproducing the stress distribution in geometries different from those used for training.In 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 iteration.The 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

The role of Ca,Al and Zn on room temperature ductility and grain boundary cohesion of magnesium

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Journal of Magnesium and Alloys 2021年第5期9卷 1536-1551页

作者： Supriya Nandy Shao-Pu Tsai Leigh Stephenson dierk raabe Stefan Zaefferer Max-Planck Institut für Eisenforschung Düsseldorf 40237Germany

It is know from literature that small additions(<1 wt%)of Ca,Al and Zn significantly improve the intrinsic ductility of Mg.The exact role of each element,both qualitatively and quantitatively,and their combined effects,however,are poorly understood.Here we achieved a much clearer view on the quantitative role of each element with respect to ductility improvement and on the collaborative effect,particularly of Ca and Zn in Mg.Some of our findings and conclusions are in disagreement with data and interpretation found in literature.Four different alloys,namely,Mg-0.1 Ca,Mg-0.1 Ca-1 Al,Mg-0.05 Ca-1 Al,Mg-0.1 Ca-2 Al-1 Zn(all are in wt%)were selected for this investigation.All alloys were treated such that approx.similar grain sizes and textures were obtained.This largely excludes the effect of extrinsic factors on ductility.EBSD-guided slip trace analyses reveal that the addition of Ca eases activation of prismatic and pyramidal II slip systems.Using in-situ deformation experiments in SEM and atom probe tomography observations of grain boundaries direct evidence is given for the individual and synergetic effects of Ca and Zn on grain boundary cohesion as an important contribution to improve the ductility of these alloys.We conclude that Ca reduces the slip anisotropy and ameliorates ductility,however,the weak grain boundary cohesion in the Mg-0.1 wt%Ca alloy limits the material’s tensile ductility.The addition of Zn alters the Ca segregation at the grain boundaries and helps to retain their cohesive strength,an effect which thus enables higher ductility and strength.The further addition of Al primarily improves the strength.The results show that the balanced influence of reduced slip anisotropy on the one hand and increased grain boundary cohesion on the other hand allow to design a high strength high ductility rare-earth free Mg alloy.

关键词： Magnesium–Calcium–Zinc alloys Ductility Slip system determination grain boundary cohesion

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轧制过程中等规聚丙烯的织构进展

高分子材料科学与工程 2011年第1期27卷 116-119页

作者：贾涓毛卫民 dierk raabe 武汉科技大学钢铁冶金及资源利用省部共建教育部重点实验室湖北武汉430081 北京科技大学材料科学与工程学院北京100083 马克思普朗克钢铁研究所

借助广角X射线衍射法对轧制过程中等规聚丙烯(iPP)材料的织构进展进行了极图的测量和取向分布函数(ODF)的计算,分析了冷轧过程中iPP的塑性变形机制。结果发现,轧制过程中iPP的主要塑性变形机制仍然是晶体学滑移,首先启动的滑移系是(010)... 详细信息

借助广角X射线衍射法对轧制过程中等规聚丙烯(iPP)材料的织构进展进行了极图的测量和取向分布函数(ODF)的计算,分析了冷轧过程中iPP的塑性变形机制。结果发现,轧制过程中iPP的主要塑性变形机制仍然是晶体学滑移,首先启动的滑移系是(010)[001]链滑移,其次是(100)[001]链滑移,期间没有发现明显的(110)[001]链滑移。轧制后iPP材料中形成的是含有几种(hk0)[001]织构组分的[001]//RD丝织构,大分子链沿轧制方向排列。

关键词：等规聚丙烯织构取向分布函数

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轧制和退火过程中等规聚丙烯的力学性能

高分子材料科学与工程 2008年第2期24卷 75-78页

作者：贾涓毛卫民 dierk raabe 北京科技大学材料科学与工程学院北京100083 马克思普朗克钢铁研究所杜塞尔多夫德国40237

借助拉伸试验对轧制和退火后的等规聚丙烯进行了轧向及横向上力学性能的研究。结果发现,结晶度和大分子链的取向是影响材料力学性能的主要原因,力学性能的变化主要发生在轧制过程中。轧制后,大分子链沿轧向排列,出现各向异性:轧向上拥... 详细信息

借助拉伸试验对轧制和退火后的等规聚丙烯进行了轧向及横向上力学性能的研究。结果发现,结晶度和大分子链的取向是影响材料力学性能的主要原因,力学性能的变化主要发生在轧制过程中。轧制后,大分子链沿轧向排列,出现各向异性:轧向上拥有高的拉伸强度和低的延伸率。结晶度的变化主要是影响材料的弹性模量和横向上的屈服强度。退火后,大分子链的取向发生较小的变化。非晶部分的再晶化与无序化共同影响材料的性能,在伸拉强度没有减少的基础上增大了材料轧向上的延伸率。

关键词：等规聚丙烯力学性能轧制退火