Advances in Delamination Modeling of Metal/Polymer Systems: Atomistic Aspects

Sluis, O. van der; Iwamoto, Nancy; Qu, Jianmin; Yang, Shaorui; Yuan, Chengye; Driel, W.D. van; Zhang, G. Q.

doi:10.1007/978-3-319-90362-0_4

Cited by 3 publications

(1 citation statement)

References 65 publications

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“…The range of applications varies from composite structures at the macro-scale, see, e.g., [1,2], to grain boundaries at the micro-scale, as presented in, e.g., [3]. For example, in laminate composite mate-rials the modeling of the mechanical behavior at the interface is of great importance, see [4][5][6]. A cohesive zone formulation is frequently utilized to describe the interface constitutive behavior in terms of traction (projection of the stress tensor on the crack plane) versus separation (displacement jump).…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural networks for the prediction of interface mechanics: a study on grain boundary constitutive behavior

Fernández

Rezaei

Mianroodi

et al. 2020

Adv. Model. and Simul. in Eng. Sci.

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The present work aims at the identification of the effective constitutive behavior of 5 aluminum grain boundaries (GB) for proportional loading by using machine learning (ML) techniques. The input for the ML approach is high accuracy data gathered in challenging molecular dynamics (MD) simulations at the atomic scale for varying temperatures and loading conditions. The effective traction-separation relation is recorded during the MD simulations. The raw MD data then serves for the training of an artificial neural network (ANN) as a surrogate model of the constitutive behavior at the grain boundary. Despite the extremely fluctuating nature of the MD data and its inhomogeneous distribution in the traction-separation space, the ANN surrogate trained on the raw MD data shows a very good agreement in the average behavior without any data-smoothing or pre-processing. Further, it is shown that the trained traction-separation ANN captures important physical properties and is able to predict traction values for given separations not contained in the training data. For example, MD simulations show a transition in traction-separation behaviour from pure sliding mode under shear load to combined GB sliding and decohesion with intermediate hardening regime at mixed load directions. These changes in GB behaviour are fully captured in the ANN predictions. Furthermore, by construction, the ANN surrogate is differentiable for arbitrary separation and also temperature, such that a thermo-mechanical tangent stiffness operator can always be evaluated. The trained ANN can then serve for large-scale FE simulation as an alternative to direct MD-FE coupling which is often infeasible in practical applications.

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Section: Introductionmentioning

confidence: 99%

Application of artificial neural networks for the prediction of interface mechanics: a study on grain boundary constitutive behavior

Fernández

Rezaei

Mianroodi

et al. 2020

Adv. Model. and Simul. in Eng. Sci.

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Simulation of Delamination Processes of Multilayer Mechanical Engineering Structures

Gondlyakh

Chemeris

Kolosov

et al. 2021

Lecture Notes in Mechanical Engineering

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Structural characterization of inkjet printed capacitor layers in various technological conditions

Kiliszkiewicz

Przybylski

Felba

et al. 2020

SSMT

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Purpose The purpose of this paper is to analyze the individual steps during the printing of capacitor structures. The method of substrate preparation, the obtained roughness of conductive and dielectric layers are examined. Moreover, the capacitances of the obtained capacitors were examined. Design/methodology/approach Surface roughness and microscopic analysis were used to assess the quality of printed conductive structures. Two criteria were used to assess the quality of printed dielectric structures: the necessary lack of discontinuity of layers and minimal roughness. To determine the importance of printing parameters, a draft experimental method was proposed. Findings The optimal way to clean the substrate has been determined. The most important parameters for the dielectric layer (i.e. drop-space, table temperature, curing time and temperature) were found. Research limitations/implications If dielectric layers are printed correctly, most problems with printing complex electronic structures (transistors, capacitors) will be eliminated. Practical implications The tests performed identified the most important factors for dielectric layers. Using them, capacitors of repeatable capacity were printed. Originality/value In the literature on this subject, no factors were found which were responsible for obtaining homogeneous dielectric layers.

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Advances in Delamination Modeling of Metal/Polymer Systems: Atomistic Aspects

Cited by 3 publications

References 65 publications

Application of artificial neural networks for the prediction of interface mechanics: a study on grain boundary constitutive behavior

Application of artificial neural networks for the prediction of interface mechanics: a study on grain boundary constitutive behavior

Simulation of Delamination Processes of Multilayer Mechanical Engineering Structures

Structural characterization of inkjet printed capacitor layers in various technological conditions

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