Reduced-order models for microstructure-sensitive effective thermal conductivity of woven ceramic matrix composites with residual porosity

Kalidindi, Surya R.

doi:10.1016/j.compstruct.2021.114399

Cited by 25 publications

(8 citation statements)

References 57 publications

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“…The many benefits that could come from the use of 2-point spatial correlations as the input instead of the discrete microstructure have already been discussed earlier. It is emphasized here that the features identified by the 2-point spatial correlations are expected to serve as universal features for all effective anisotropic material properties of interest (Garmestani et al, 1998;Cecen et al, 2014;Gupta et al, 2015;Kalidindi, 2015;Paulson et al, 2017;Yabansu et al, 2020;Generale and Kalidindi, 2021). Therefore, it should be possible to create microstructure-property surrogates capable of concurrently predicting multiple effective anisotropic material properties.…”

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

confidence: 96%

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

confidence: 96%

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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“…Broadly referred as Materials Knowledge Systems (MKS), this framework takes advantage of the computational efficiency of voxelated representations and Fast Fourier Transform (FFT) algorithms to implement the theoretical framework of n-point spatial correlations. The feasibility and benefits of this approach have been demonstrated on a wide variety of material classes and material structures at different length scales [from the atomic (Gomberg et al, 2017;Kaundinya et al, 2021) to dislocation length scales (Robertson and Kalidindi, 2021a) to microscale (Generale and Kalidindi, 2021)].…”

Section: Materials Structure Representation and Quantificationmentioning

confidence: 99%

“…Although, one can define higher-order features (e.g., 3-point features), one often finds a sufficiently large number of features in the 2-point feature set, as it includes all permutations of (h, h′) over a very large domain of r (this domain includes all distinct set of all vectors of interest that can be placed in Ω). The adequacy of the set of 2-point features in capturing the salient features of the material structure (including the set of features identified in conventional practices in materials science and engineering) has been established for a broad range of material classes (Latypov et al, 2019;Generale and Kalidindi, 2021) as well as the different structure length scales (Fullwood et al, 2010;Robertson and Kalidindi, 2021a;Kaundinya et al, 2021) encountered in them.…”

Section: Materials Structure Representation and Quantificationmentioning

confidence: 99%

“…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%

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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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“…The inherent relationship existing among the structure and property of materials can be defined as structure-property (SP) linkages. 1 The investigation of SP linkages propels advancements in material and structural innovation, as well as design optimization. However, the rapid development of traditional SP linkages is limited by extremely high time and economic costs, which seriously delays the innovation in related fields.…”

Section: Introductionmentioning

confidence: 99%

A materials informatics framework based on reduced‐order models for extracting structure–property linkages of additively manufactured continuous fiber‐reinforced polymer composites

Zhang,

Shi,

et al. 2024

Polymer Composites

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The innovative combination of additive manufacturing (AM) and continuous fiber‐reinforced polymer composites (CFRPCs) confers products with the dual advantages of integrated manufacturing and designability of properties, but lack an efficient and reliable method for property prediction. This study presents a materials informatics framework using reduced‐order models and machine learning (ML) to extract the structure–property (SP) linkages between microstructures and macroscopic elastic properties of AM‐CFRPCs. The initial step involves generating microstructural 2D cross‐sections and representative volume elements (RVEs) with random fiber and pore distributions based on the minimum potential method. Then, finite element (FE) calculations are performed on RVEs to obtain nine macroscopic elastic properties. Following that, the quantification and dimensionality reduction of the 2D cross‐sectional dataset are conducted separately using two‐point spatial correlations and principal component analysis (PCA). Finally, a Bayesian optimized composite kernel support vector regression (CK‐SVR) algorithm is used to effectively establish complex mapping relationships between the reduced‐order representations of the microstructures and the mechanical properties. Despite the reduced‐order dataset containing only 3–6 variables, the framework generates an interpretable model exhibiting excellent accuracy with all predicted R2 values surpassing 0.91. Therefore, this framework presents a prospective solution for expediting the design and optimization of AM‐CFRPCs.Highlights A materials informatics scheme is proposed to predict the 9 elastic properties of AM‐CFRPCs. Microstructures are quantified and dimensionally reduced by two‐point statistics and PCA. SP linkages are established between 2D cross‐sections and 3D macromechanical properties. Modified CK‐SVR exhibits higher prediction accuracy compared to conventional models.

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Reduced-order models for microstructure-sensitive effective thermal conductivity of woven ceramic matrix composites with residual porosity

Cited by 25 publications

References 57 publications

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

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

Digital Twins for Materials

A materials informatics framework based on reduced‐order models for extracting structure–property linkages of additively manufactured continuous fiber‐reinforced polymer composites

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