Autonomous Development of a Machine-Learning Model for the Plastic Response of Two-Phase Composites from Micromechanical Finite Element Models

Marshall, Andrew; Kalidindi, Surya R.

doi:10.1007/s11837-021-04696-w

Cited by 18 publications

(9 citation statements)

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“…In the late 1900s, [11] has related the n-point correlation functions [12] to the effective material properties at the microscale. This later inspired the materials knowledge system (MKS), which approximates the material properties by reduced coefficients of the two-point correlation function (2PCF) [13,14]. This, however, has been shown to be limited with respect to prediction accuracy [1,36], lastly, since the 2PCF is not injective [37].…”

Section: Microstructure Modelingmentioning

confidence: 99%

“…Some data-driven approaches utilize properties of the n-point correlation function [11,12], where the materials knowledge system (MKS) approach uses reduced coefficients/principal components of the 2-point correlation function [13][14][15], e.g., in a subsequent artificial neural network [1] to directly predict the material's characteristics. The list of features used for the prediction can be extended [9,16,17], e.g., by making use of the lineal path function [18], which is also suitable in a slightly different context for generating statistically similar microstructural elements according to [19].…”

Section: Introductionmentioning

confidence: 99%

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Microstructure Homogenization - Human vs Machine

Lißner,

Fritzen

2024

Preprint

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Two approaches are presented to improve the capabilities of machine learning models in multi- scale modeling for microstructure homogenization (graphical abstract in Fig. 1). The first approach features a Bayesian data mining scheme with a human in the loop, halving the prediction error com- pared to [1] using four novel efficient to evaluate feature descriptors. The second purely machine learning driven approach utilizes convolutional neural networks, where we introduce a novel mod- ule, designed to capture characteristics of different length scales within the image. The new module features a new normalization block, which aids in calibrating the differently obtained feature char- acteristics. Further improvements, universally applicable to artificial neural networks, are found with a novel hyperparameter insensitive learning rate schedule, which adapts to the training progress of the model. A further improvement is given by a pre-trained feature bypass which utilizes global low level features, to serve as baseline prediction such that the model is able to dedicate its atten- tion to high level features. The proposed schemes have been applied to different literature models, yielding significant improvements in any investigated convolutional neural network. The improve- ments found by the two overarching contributions, i.e., derived through feature development with a human in the loop, and via convolutional neural networks, are critically assessed in a thermal and mechanical setting. It is further expanded to variable material parameters while allowing for variable microstructural elements, yielding drastically reduced prediction errors across-the-board.

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Section: Microstructure Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Microstructure Homogenization - Human vs Machine

Lißner,

Fritzen

2024

Preprint

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“…The use of coarser voxels will lead to inaccurate representation of the smallest features (i.e., phase regions) in the RVE, while the use of finer voxels will increase the computational cost. Prior work (Latypov et al, 2019;Marshall and Kalidindi, 2021) has utilized successfully RVEs of resolution 27 X 27 X 27 in modelling the homogenized plastic response of composite microstructures. The resolution of the RVEs in this work was increased to 31 X 31 X 31 to allow for a slightly improved representation of the RVEs used to capture the salient microstructure-property linkages for the present application.…”

Section: Convolutional Neural Network Model For Microstructure-proper...mentioning

confidence: 99%

Development of a Robust CNN Model for Capturing Microstructure-Property Linkages and Building Property Closures Supporting Material Design

Mann

Kalidindi

2022

Front. Mater.

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Recent works have demonstrated the viability of convolutional neural networks (CNN) for capturing the highly non-linear microstructure-property linkages in high contrast composite material systems. In this work, we develop a new CNN architecture that utilizes a drastically reduced number of trainable parameters for building these linkages, compared to the benchmarks in current literature. This is accomplished by creating CNN architectures that completely avoid the use of fully connected layers, while using the 2-point spatial correlations of the microstructure as the input to the CNN. In addition to increased robustness (because of the much smaller number of trainable parameters), the CNN models developed in this work facilitate the construction of property closures at very low computational cost. This is because it allows for easy exploration of the space of valid 2-point spatial correlations, which is known to be a convex hull. Consequently, one can generate new sets of valid 2-point spatial correlations from previously available valid sets of 2-point spatial correlations, simply as convex combinations. This work demonstrates the significant benefits of utilizing 2-point spatial correlations as the input to the CNN, in place of the voxelated discrete microstructures used in current benchmarks.

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“…Indeed, much progress has been made in organizing and disseminating materials data (The Minerals, Metals & Materials Society TMS, 2017), and physics-based simulation toolsets (The Minerals, Metals & Materials Society TMS, 2015). There has also been a strong injection of data sciences and AI/ML into materials research, especially in aspects related to data ingestion (e.g., experimental laboratory automation) (Kalidindi et al, 2019), curation (e.g., ontologies) (Morgado et al, 2020;Voigt and Kalidindi, 2021), feature engineering (Kalidindi, 2020;Xiang et al, 2021), and automated generation of surrogate models (Generale and Kalidindi, 2021;Marshall and Kalidindi, 2021). These recent advances in materials research have set the stage for the extension and application of the emerging concept of digital twins described earlier to include the multiscale details of the material.…”

Section: Introductionmentioning

confidence: 99%

Digital Twins for Materials

et al. 2022

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Digital twins are emerging as powerful tools for supporting innovation as well as optimizing the in-service performance of a broad range of complex physical machines, devices, and components. A digital twin is generally designed to provide accurate in-silico representation of the form (i.e., appearance) and the functional response of a specified (unique) physical twin. This paper offers a new perspective on how the emerging concept of digital twins could be applied to accelerate materials innovation efforts. Specifically, it is argued that the material itself can be considered as a highly complex multiscale physical system whose form (i.e., details of the material structure over a hierarchy of material length) and function (i.e., response to external stimuli typically characterized through suitably defined material properties) can be captured suitably in a digital twin. Accordingly, the digital twin can represent the evolution of structure, process, and performance of the material over time, with regard to both process history and in-service environment. This paper establishes the foundational concepts and frameworks needed to formulate and continuously update both the form and function of the digital twin of a selected material physical twin. The form of the proposed material digital twin can be captured effectively using the broadly applicable framework of n-point spatial correlations, while its function at the different length scales can be captured using homogenization and localization process-structure-property surrogate models calibrated to collections of available experimental and physics-based simulation data.

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Autonomous Development of a Machine-Learning Model for the Plastic Response of Two-Phase Composites from Micromechanical Finite Element Models

Cited by 18 publications

References 50 publications

Microstructure Homogenization - Human vs Machine

Microstructure Homogenization - Human vs Machine

Development of a Robust CNN Model for Capturing Microstructure-Property Linkages and Building Property Closures Supporting Material Design

Digital Twins for Materials

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