Transverse Failure of Unidirectional Composites: Sensitivity to Interfacial Properties

Zacek, Scott; Brandyberry, David; Klepacki, Anthony; Montgomery, Chris; Shakiba, Maryam; Rossol, Michael; Najafi, Ahmad R.; Zhang, Xiang; Sottos, Nancy R.; Geubelle, Philippe H.; Przybyla, Craig; Jefferson, George

doi:10.1007/978-3-030-40562-5_12

Cited by 7 publications

(6 citation statements)

References 22 publications

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“…The portions of an element traversed by interfaces can have different constitutive behaviors. For more details on the formulation and implementation of nonlinear cohesive IGFEM, the reader can refer to [38,39,29]. The described method was implemented within an IGFEM solver, developed using the C++ language.…”

Section: Generator 2 Training Resultsmentioning

confidence: 99%

A Data-Driven Approach to Full-Field Damage and Failure Pattern Prediction in Microstructure-Dependent Composites using Deep Learning

Sepasdar,

Karpatne,

Shakiba

2021

Preprint

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An image-based deep learning framework is developed in this paper to predict damage and failure in microstructure-dependent composite materials. The work is motivated by the complexity and computational cost of high-fidelity simulations of such materials. The proposed deep learning framework predicts the post-failure full-field stress distribution and crack pattern in two-dimensional representations of the composites based on the geometry of microstructures. The material of interest is selected to be a high-performance unidirectional carbon fiber-reinforced polymer composite. The deep learning framework contains two stacked fully-convolutional networks, namely, Generator 1 and Generator 2, trained sequentially. First, Generator 1 learns to translate the microstructural geometry to the full-field post-failure stress distribution. Then, Generator 2 learns to translate the output of Generator 1 to the failure pattern. A physics-informed loss function is also designed and incorporated to further improve the performance of the proposed framework and facilitate the validation process. In order to provide a sufficiently large data set for training and validating the deep learning framework, 4500 microstructural representations are synthetically generated and simulated in an efficient finite element framework. It is shown that the proposed deep learning approach can effectively predict the composites' post-failure full-field stress

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Section: Generator 2 Training Resultsmentioning

confidence: 99%

A Data-Driven Approach to Full-Field Damage and Failure Pattern Prediction in Microstructure-Dependent Composites using Deep Learning

Sepasdar,

Karpatne,

Shakiba

2021

Preprint

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“…As described earlier, the IGFEM relies on meshes that do not conform to the material interfaces and introduces enrichments in the elements traversed by these material interfaces. To model the debonding of the fiber‐matrix interfaces, we adopt in this work a particular form of the IGFEM based on C −1 enrichment functions to incorporate cohesive debondings including the possibility of two cohesive interfaces within the same nonconforming element . Elements with two cohesive interfaces provide the capability for the cohesive failure of two adjacent fiber/matrix interfaces with nonconforming elements that span the separation between the two fibers to address the challenge of simulating the transverse failure of composite layers with high fiber volume fractions.…”

Section: Carbon Fiber Composite ‐ Microstructure and Numerical Methodsmentioning

confidence: 99%

“…Other researchers have developed methodologies to determine the sensitivity of the global composite behavior, eg, deformations, with respect to the local characteristics such as the material properties of the microconstituents . In two recent publications related to the present work, the authors computed the sensitivity of the transverse failure response of the [0/90/0] T composite laminate to the cohesive properties of the fiber/matrix interfaces. The authors presented an analytical sensitivity formulation based on the direct differentiation method implemented in a nonlinear cohesive interface‐enriched generalized finite element method (IGFEM) solver.…”

Section: Introductionmentioning

confidence: 99%

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Transverse failure of carbon fiber composites: Analytical sensitivity to the distribution of fiber/matrix interface properties

Shakiba

Brandyberry

Zacek

et al. 2019

Numerical Meth Engineering

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An analytic differentiation method is presented to calculate the sensitivity of the transverse failure response of carbon fiber composite laminates to the distribution parameters of the fiber/matrix interface properties. The method starts with the evaluation of the sensitivities of the transverse failure response with respect to the interface properties of each fiber, ie, the cohesive failure strength and the critical displacement jump. These individual sensitivities are then used to calculate the sensitivities with respect to the mean and standard deviation of the interface properties. The derived sensitivities are implemented in a nonlinear interface-enriched generalized finite element method solver specially developed for this application. The interface-enriched generalized finite element method solver combines a cohesive modeling of the fiber/matrix interface failure with finite element meshes that do not conform to the composite microstructure. The approach is first demonstrated on a model material involving a one-dimensional domain containing N cohesive interfaces described by randomly selected cohesive failure properties. The method is then applied to the more complex problem of a composite laminate involving a large number of fibers.

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“…Herein, we develop and implement a shape optimization method to design composite material microstructures based on a NURBS-based interface-enriched generalized finite element method (NIGFEM) to achieve a prescribed macroscopic behavior. This work builds on our previous studies 71,72 that introduced two Eulerian-based shape optimization schemes by incorporating the IGFEM [68][69][70]73 and NIGFEM, [74][75][76][77] respectively. Similar studies are performed to design microvascular panels for active cooling applications.…”

Section: Introductionmentioning

confidence: 99%

Multiscale design of nonlinear materials using a Eulerian shape optimization scheme

Najafi

Safdari

Tortorelli

et al. 2021

Numerical Meth Engineering

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Motivated by recent advances in manufacturing, the design of materials is the focal point of interest in the material research community. One of the critical challenges in this field is finding optimal material microstructure for a desired macroscopic response. This work presents a computational method for the mesoscale‐level design of particulate composites for an optimal macroscale‐level response. The method relies on a custom shape optimization scheme to find the extrema of a nonlinear cost function subject to a set of constraints. Three key “modules” constitute the method: multiscale modeling, sensitivity analysis, and optimization. Multiscale modeling relies on a classical homogenization method and a nonlinear NURBS‐based generalized finite element scheme to efficiently and accurately compute the structural response of particulate composites using a nonconformal discretization. A three‐parameter isotropic damage law is used to model microstructure‐level failure. An analytical sensitivity method is developed to compute the derivatives of the cost/constraint functions with respect to the design variables that control the microstructure's geometry. The derivation uncovers subtle but essential new terms contributing to the sensitivity of finite element shape functions and their spatial derivatives. Several structural problems are solved to demonstrate the applicability, performance, and accuracy of the method for the design of particulate composites with a desired macroscopic nonlinear stress‐strain response.

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Transverse Failure of Unidirectional Composites: Sensitivity to Interfacial Properties

Cited by 7 publications

References 22 publications

A Data-Driven Approach to Full-Field Damage and Failure Pattern Prediction in Microstructure-Dependent Composites using Deep Learning

A Data-Driven Approach to Full-Field Damage and Failure Pattern Prediction in Microstructure-Dependent Composites using Deep Learning

Transverse failure of carbon fiber composites: Analytical sensitivity to the distribution of fiber/matrix interface properties

Multiscale design of nonlinear materials using a Eulerian shape optimization scheme

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