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Understanding the microstructural influence on the failure mechanisms in multi-phase materials calls for the identification of the worst-case scenario. This necessitates a statistical approach. By performing simulations directly based on…

材料科学 · 物理学 2016-12-06 T. W. J. de Geus , J. E. P. van Duuren , R. H. J. Peerlings , M. G. D. Geers

Fracture processes in heterogeneous materials comprise a large number of disordered spatial degrees of freedom, representing the dynamical state of a sample over the entire domain of interest. This complexity is usually modeled directly,…

统计力学 · 物理学 2014-08-25 Yon Visell , Guillaume Millet

The precise mechanisms underlying the failure of multi-phase materials may be strongly dependent on the material's microstructural morphology. Micromechanical modeling has provided much insight into this dependence, but uncertainties remain…

材料科学 · 物理学 2016-04-14 T. W. J. de Geus , R. H. J. Peerlings , M. G. D. Geers

Heterogeneous materials are often organized in a hierarchical manner, where a basic unit is repeated over multiple scales.The structure then acquires a self-similar pattern. Examples of such structure are found in various biological and…

统计力学 · 物理学 2019-08-28 Soumyajyoti Biswas , Michael Zaiser

Quasi-brittle materials endowed with (statistically) self-similar hierarcical microstructures show distinct failure patterns that deviate from the standard scenario of damage accumulation followed by crack nucleation-and-growth. Here we…

We propose a machine learning approach to address a key challenge in materials science: predicting how fractures propagate in brittle materials under stress, and how these materials ultimately fail. Our methods use deep learning and train…

Disorder and long-range interactions are two of the key components that make material failure an interesting playfield for the application of statistical mechanics. The cornerstone in this respect has been lattice models of the fracture in…

统计力学 · 物理学 2009-11-11 Mikko J. Alava , Phani K. V. V. Nukala , Stefano Zapperi

We study precursors of failure in hierarchical random fuse network models which can be considered as idealizations of hierarchical (bio)materials where fibrous assemblies are held together by multi-level (hierarchical) cross-links. When…

无序系统与神经网络 · 物理学 2019-07-10 Paolo Moretti , Bastien Dietemann , Michael Zaiser

Multi-phase material are frequently applied in a wide variety of products, as they posses a unique set of properties by combining two or more distinct phases at the level of the microstructure. Although the macroscopic stiffness and…

材料科学 · 物理学 2016-04-13 T. W. J. de Geus , R. H. J. Peerlings , M. G. D. Geers

The correlations among elements that break in random fuse network fracture are studied, for disorder strong enough to allow for volume damage before final failure. The growth of microfractures is found to be uncorrelated above a…

统计力学 · 物理学 2009-11-10 F. Reurings , M. J. Alava

The damage and fracture of materials are technologically of enormous interest due to their economic and human cost. They cover a wide range of phenomena like e.g. cracking of glass, aging of concrete, the failure of fiber networks in the…

材料科学 · 物理学 2015-06-24 D. Sornette

Multiscale techniques have been widely shown to potentially overcome the limitation of homogenization schemes in representing the microscopic failure mechanisms in heterogeneous media as well as their influence on their structural response…

数值分析 · 数学 2021-08-10 Fabrizio Greco , Lorenzo Leonetti , Paolo Lonetti , Raimondo Luciano , Andrea Pranno

The failure of mechanical metamaterials is a function of the interplay between the properties of the base material and the microstructural geometry. Stochastic failure properties of the base material and disordered microstructural…

材料科学 · 物理学 2025-09-01 Sage Fulco , Prashant K. Purohit , Michal K. Budzik , Kevin T. Turner

Using large scale numerical simulations we analyze the statistical properties of fracture in the two dimensional random spring model and compare it with its scalar counterpart: the random fuse model. We first consider the process of crack…

材料科学 · 物理学 2009-11-11 Phani Kumar V. V. Nukala , Stefano Zapperi , Srdan Simunovic

Polycrystalline metal failure often begins with stress concentration at grain boundaries. Identifying which microstructural features trigger these events is important but challenging because these extreme damage events are rare and the…

应用统计 · 统计学 2025-10-28 Yinling Zhang , Samuel D. Dunham , Curt A. Bronkhorst , Nan Chen

Spatial and temporal features are studied with respect to their predictive value for failure time prediction in subcritical failure with machine learning (ML). Data are generated from simulations of a novel, brittle random fuse model (RFM),…

材料科学 · 物理学 2022-08-16 Stefan Hiemer , Paolo Moretti , Stefano Zapperi , Michael Zaiser

Multi-phase materials are key for modern engineering applications. They are generally characterized by a high strength and ductility. Many of these materials fail by ductile fracture of the, generally softer, matrix phase. In this work we…

材料科学 · 物理学 2016-04-12 T. W. J. de Geus , R. H. J. Peerlings , M. G. D. Geers

We investigate the role of architected thin films in the interfacial failure properties of bi-layer composites. Our results show that, while graded structures can be used to prescribe failure at the interface, they do not offer significant…

材料科学 · 物理学 2026-03-10 Christian Greff , Leon Pyka , Michael Zaiser , Paolo Moretti

Structural damage due to excessive loading or environmental degradation typically occurs in localized areas in the absence of collapse. This prior information about the spatial sparseness of structural damage is exploited here by a…

应用统计 · 统计学 2015-03-29 Yong Huang , James L. Beck

In brittle fracture applications, failure paths, regions where the failure occurs and damage statistics, are some of the key quantities of interest (QoI). High-fidelity models for brittle failure that accurately predict these QoI exist but…

计算工程、金融与科学 · 计算机科学 2018-08-01 M. K. Mudunuru , N. Panda , S. Karra , G. Srinivasan , V. T. Chau , E. Rougier , A. Hunter , H. S. Viswanathan
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