Automatic grain size determination in microstructures using image processing

Peregrina-Barreto, Hayde; Terol-Villalobos, Iván R.; Rangel-Magdaleno, José; Herrera-Navarro, Ana M.; Morales-Hernandez, Luis A.; Manriquez-Guerrero, Federico

doi:10.1016/j.measurement.2012.06.012

Cited by 69 publications

(32 citation statements)

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“…The ubiquitous connection between microstructure (mainly, grain-size distribution) of a material and its physical properties has motivated numerous studies on developing robust techniques to analyze microscopy/tomography images. [13][14][15][16][17][18] ASTM outlines the industry standard for grain identification in 2D data, 16 which consists of methods such as matching, planimetric, and intercept methods. These methods, albeit can achieve high accuracy (±0.25 grain size units) and reproducibility, can be severely impaired when the intersection criterion (for distinguishing grains) is poorly chosen or the grain-size distribution is non-uniform.…”

Section: Introductionmentioning

confidence: 99%

“…These methods, albeit can achieve high accuracy (±0.25 grain size units) and reproducibility, can be severely impaired when the intersection criterion (for distinguishing grains) is poorly chosen or the grain-size distribution is non-uniform. 16 In addition, these technique often require tedious manual measurements, and automation is challenging due variability in etching level or contrast differences although electron back scattering diffraction methods have been recently proposed to eliminate subjectivity surrounding existence/location of grain boundaries. 15,19 Automated methods for grain identification in 2D data have been developed over the years.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning enabled autonomous microstructural characterization in 3D samples

et al. 2020

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We introduce an unsupervised machine learning (ML) based technique for the identification and characterization of microstructures in three-dimensional (3D) samples obtained from molecular dynamics simulations, particle tracking data, or experiments. Our technique combines topology classification, image processing, and clustering algorithms, and can handle a wide range of microstructure types including grains in polycrystalline materials, voids in porous systems, and structures from self/directed assembly in soft-matter complex solutions. Our technique does not require a priori microstructure description of the target system and is insensitive to disorder such as extended defects in polycrystals arising from line and plane defects. We demonstrate quantitively that our technique provides unbiased microstructural information such as precise quantification of grains and their size distributions in 3D polycrystalline samples, characterizes features such as voids and porosity in 3D polymeric samples and micellar size distribution in 3D complex fluids. To demonstrate the efficacy of our ML approach, we benchmark it against a diverse set of synthetic data samples representing nanocrystalline metals, polymers and complex fluids as well as experimentally published characterization data. Our technique is computationally efficient and provides a way to quickly identify, track, and quantify complex microstructural features that impact the observed material behavior.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine learning enabled autonomous microstructural characterization in 3D samples

et al. 2020

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“…Os métodos semiautomáticos consistem, basicamente, em aplicar as regras dos procedimentos manuais, previstos na norma ASTM E112, utilizando o auxílio de um computador ou uma mesa digitalizadora [7]. A análise de imagem aplicada a metalografia tem se tornado uma ferramenta importante quando se é exigido à avaliação de um grande número de microestruturas em um curto período de tempo, bem como em processos onde são necessários o máximo de repetibilidade e reprodutibilidade das medidas [8,9]. Além dos métodos semiautomáticos, a norma ASTM E1382 estabelece algumas metodologias para determinar o tamanho médio de grão de modo automático.…”

Section: Introductionunclassified

Comparação Entre Métodos Padronizados E Novas Metodologias Para Estimar O Tamanho De Grãos Em Materiais Metálicos

Santana¹,

Faria²

2017

Anais Do Congresso Anual Da ABM

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ResumoO tamanho médio dos grãos é um parâmetro estrutural frequentemente estimado quando se está avaliando, principalmente, as propriedades mecânicas dos materiais metálicos. A American Society for Testing and Materials (ASTM) estabelece, nas normas ASTM E112 e ASTM E1382, metodologias para estimar o tamanho médio dos cristais de maneira manual e semiautomática/automática, respectivamente. O presente trabalho propõe, além de uma nova metodologia semiautomática, duas metodologias automáticas para estimar o tamanhão médio de grão em materiais metálicos policristalinos monofásicos, assim como um parâmetro para avaliar a morfologia dos grãos. O tamanho de grão G ASTM encontrado utilizando os novos procedimentos testados se apresentou muito próximo aos valores obtidos pelos métodos padronizados, que também foram aplicados e usados como referência para avaliação da representatividade das medidas. Desse modo, os métodos propostos se apresentam como uma alternativa viável para estimar o referido parâmetro estrutural dos materiais metálicos. Palavras-chave: Tamanho médio de grãos; ASTM E112; ASTM E1382. COMPARATION BETWEEN STANDARDIZED METHODS AND NEW METHODOLOGIES FOR AGRAIN SIZE ESTIMATION IN METALLIC MATERIALS AbstractThe average grain size is a structural parameter frequently estimated when the mechanical properties of metallic materials are evaluated. The American Society for Testing and Materials (ASTM) standardizes at ASTM E112 and ASTM 1382 methods for determining the average size of crystals by using manual and semiautomatic/automatic procedures, respectively. This work proposes besides a new semiautomatic methodology, two automatic methods for determining the average grain size in monophasic polycrystalline metallic materials, as well as a parameter to evaluate grains shape. The G ASTM grain size obtained by using the new procedures were very similar to the obtained values by employing standard methods, which were also applied and used as reference to evaluate the measures representativeness. So the proposed methods have been a viable alternative for estimating the referred structural parameter in metallic materials.

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“…Peregrina-Barreto et al [14] report that they had problems with the accurate determination of the microstructures from metallographic images which they were processing. Images were differently illuminated, this causing a lot of noise on the image, low contrasts, badly defined boundaries and so on.…”

Section: Introductionmentioning

confidence: 99%

Intelligent system for prediction of mechanical properties of material based on metallographic images

et al. 2015

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Original scientific paper This article presents developed intelligent system for prediction of mechanical properties of material based on metallographic images. The system is composed of two modules. The first module of the system is an algorithm for features extraction from metallographic images. The first algorithm reads metallographic image, which was obtained by microscope, followed by image features extraction with developed algorithm and in the end algorithm calculates proportions of the material microstructure. In this research we need to determine proportions of graphite, ferrite and ausferrite from metallographic images as accurately as possible. The second module of the developed system is a system for prediction of mechanical properties of material. Prediction of mechanical properties of material was performed by feed-forward artificial neural network. As inputs into artificial neural network calculated proportions of graphite, ferrite and ausferrite were used, as targets for training mechanical properties of material were used. Training of artificial neural network was performed on quite small database, but with parameters changing we succeeded. Artificial neural network learned to such extent that the error was acceptable. With the oriented neural network we successfully predicted mechanical properties for excluded sample. Inteligentni sustav za predviđanje mehaničkih svojstava materijala na osnovu metalografskih slika Izvorni znanstveni članak U radu se predstavlja razvijeni inteligentni sustav za predviđanje mehaničkih svojstava materijala na temelju metalografskih slika. Sustav se sastoji od dva modula. Prvi je modul algoritam za dobivanje karakteristika iz metalografskih slika. Prvi algoritam očitava metalografsku sliku dobivenu mikroskopom, zatim se dobivaju karakterisike razvijenim algoritmom, i na kraju algoritam izračunava omjere mikrostrukture materijala. U ovom istraživanju potrebno je što točnije odrediti omjere grafita, ferita i ausferita iz metalografskih slika. Drugi modul razvijenog sustava je sustav za predviđanje mehaničkih svojstava materijala. Predviđanje mehaničkih svojstava materijala izvršeno je pomoću feed-forward umjetne neuronske mreže. Kao ulazi u umjetnu neuronsku mrežu rabljeni su izračunati omjeri grafita, ferita i ausferita, dok su mehanička svojstva materijala upotrebljena kao ciljevi za uvježbavanje. Uvježbavanje umjetnih neuronskih mreža obavljeno je na prilično maloj bazi podataka, no mijenjajući parametre nama je to uspjelo. Umjetna neuronska mreža je naučila do te mjere da je greška bila prihvatljiva. S orijentiranom neuronskom mrežom uspješno smo predvidjeli mehanička svojstva izuzetog uzorka. KeywordsKljučne riječi: faktor faznog prijanjanja između površina; maksimalna vlačna čvrstoča; mehanička svojstva; metalografska slika; naprezanje tečenja; obrada slike; žilavost loma; umjetna neuronska mreža

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Automatic grain size determination in microstructures using image processing

Cited by 69 publications

References 21 publications

Machine learning enabled autonomous microstructural characterization in 3D samples

Machine learning enabled autonomous microstructural characterization in 3D samples

Comparação Entre Métodos Padronizados E Novas Metodologias Para Estimar O Tamanho De Grãos Em Materiais Metálicos

Intelligent system for prediction of mechanical properties of material based on metallographic images

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