Machine Learning-Aided Parametrically Homogenized Crystal Plasticity Model (PHCPM) for Single Crystal Ni-Based Superalloys

Weber, George; Pinz, Maxwell; Ghosh, Somnath

doi:10.1007/s11837-020-04344-9

Cited by 27 publications

(8 citation statements)

References 34 publications

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“…Classification algorithm C4.5 [125] Analysis of the causes of Coffee defects by decision Tree [126] Naive Bayes [127] Classification of metal binders [128] SVM [129] Material monitoring and defect diagnosis [130] Prediction of rock brittleness [131] KNN [132] Prediction of process parameters of reinforced metal casting [133] Analysis of welding modeling of different materials [134] Adaboost [135] Temperature compensation of Silicon Piezoresistive pressure Sensor [136] Cart [137] Differential diagnosis of mucosanase [138] Clustering algorithm K-Means [139] Structural texture similarity recognition of materials [140] Establishment of parametric homogenized crystal plasticity model of single crystal Ni-base superalloy [141] EM [142] Estimation of dose distribution from positron emitter distribution combined with filtering [143] Correlation distribution Apriori [144] Identify the frequency trajectory of material transportation [145] Connection analysis PageRank [146] Measurement of hyperelastic materials [147] Remote protein homology detection [148] open-source material packages and machine learning frameworks could be effectively connected by the cloud-based interconnected applications. At present, in the research of machine learning in materials science, the materials-related open-source toolkits and programming language frameworks have been well designed by programming tools, which can provide great convenience for non-professional programming researchers, such as materials researchers.…”

Section: Algorithm Type Algorithm Model Examples In Materials Sciencementioning

confidence: 99%

Innovative Materials Science via Machine Learning

Gao

Min

Fang

et al. 2021

Adv Funct Materials

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Nowadays, the research on materials science is rapidly entering a phase of data-driven age. Machine learning, one of the most powerful data-driven methods, have been being applied to materials discovery and performances prediction with undoubtedly tremendous application foreground. Herein, the challenges and current progress of machine learning are summarized in materials science, the design strategies are classified and highlighted, and possible perspectives are proposed for the future development. It is hoped this review can provide important scientific guidance for innovating materials science and technology via machine learning in the future.

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Section: Algorithm Type Algorithm Model Examples In Materials Sciencementioning

confidence: 99%

Innovative Materials Science via Machine Learning

Gao

Min

Fang

et al. 2021

Adv Funct Materials

View full text Add to dashboard Cite

show abstract

“…Major advances inmaterials informatics, from the introduction of integrated computational materials engineering Allison et al (2006) to the high-throughput Materials Project database Jain et al (2013), have provided a new way for materials scientists to explore structure/properties/processing relationships. In particular, these advances have motivated the development of advanced materials characterization tools Park et al (2017) and facilitated multiscale modelling efforts aimed at advanced materials design Weber et al (2020); Weber et al (2022) and failure prediction Talebi et al (2014). At the forefront of this field is the pressing need for automated frameworks that can quickly and accurately process material information to advance the materials discovery process.…”

Section: Introductionmentioning

confidence: 99%

Automated, high-accuracy classification of textured microstructures using a convolutional neural network

Khurjekar

Conry²,

Kesler³

et al. 2023

Front. Mater.

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Crystallographic texture is an important descriptor of material properties but requires time-intensive electron backscatter diffraction (EBSD) for identifying grain orientations. While some metrics such as grain size or grain aspect ratio can distinguish textured microstructures from untextured microstructures after significant grain growth, such morphological differences are not always visually observable. This paper explores the use of deep learning to classify experimentally measured textured microstructures without knowledge of crystallographic orientation. A deep convolutional neural network is used to extract high-order morphological features from binary images to distinguish textured microstructures from untextured microstructures. The convolutional neural network results are compared with a statistical Kolmogorov–Smirnov tests with traditional morphological metrics for describing microstructures. Results show that the convolutional neural network achieves a significantly improved classification accuracy, particularly at early stages of grain growth, highlighting the capability of deep learning to identify the subtle morphological patterns resulting from texture. The results demonstrate the potential of a convolutional neural network as a tool for reliable and automated microstructure classification with minimal preprocessing.

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“…Tran and Wildey [14] applied data-consistent inversion method to infer a distribution of microstructure features from a distribution of yield stress, where the push-forward density map via a heteroscedastic Gaussian process approximation is consistent with the imposed yield stress density. Kotha et al [15,16,17,18] developed uncertainty-quantified, parametrically homogenized constitutive models to capture uncertainty in microstructure-dependent stress-strain curve, as well as stochastic yield surface, which has been broadly applied for modeling multi-scale fatigue crack nucleation in Ti alloys [19,20] and for single-crystal Ni-based superalloys with support vector regression as an underlying machine learning model [21]. Sedighiani et al [22,23] applied genetic algorithm and polynomial approximation to various constitutive models, including phenomenological and dislocation-density-based models.…”

Section: Introductionmentioning

confidence: 99%

Multi-fidelity microstructure-induced uncertainty quantification by advanced Monte Carlo methods

Tran¹,

Robbe²,

Lim³

2023

Preprint

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Quantifying uncertainty associated with the microstructure variation of a material can be a computationally daunting task, especially when dealing with advanced constitutive models and fine mesh resolutions in the crystal plasticity finite element method (CPFEM). Numerous studies have been conducted regarding the sensitivity of material properties and performance to the mesh resolution and choice of constitutive model. However, a unified approach that accounts for various fidelity parameters, such as mesh resolutions, integration time-steps, and constitutive models simultaneously is currently lacking. This paper proposes a novel uncertainty quantification (UQ) approach for computing the properties and performance of homogenized materials using CPFEM, that exploits a hierarchy of approximations with different levels of fidelity. In particular, we illustrate how multi-level sampling methods, such as multi-level Monte Carlo (MLMC) and multi-index Monte Carlo (MIMC), can be applied to assess the impact of variations in the microstructure of polycrystalline materials on the predictions of homogenized materials properties. We show that by adaptively exploiting the fidelity hierarchy, we can significantly reduce the number of microstructures required to reach a certain prescribed accuracy. Finally, we show how our approach can be extended to a multi-fidelity framework, where we allow the underlying constitutive model to be chosen from either a phenomenological plasticity model or a dislocation-density-based model.

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Machine Learning-Aided Parametrically Homogenized Crystal Plasticity Model (PHCPM) for Single Crystal Ni-Based Superalloys

Cited by 27 publications

References 34 publications

Innovative Materials Science via Machine Learning

Innovative Materials Science via Machine Learning

Automated, high-accuracy classification of textured microstructures using a convolutional neural network

Multi-fidelity microstructure-induced uncertainty quantification by advanced Monte Carlo methods

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