A methodology of steel microstructure recognition using SEM images by machine learning based on textural analysis

Tsutsui, Kazumasa; Terasaki, Hidenori; Uto, Kyohei; Maemura, Tatsuya; Hiramatsu, Shogo; Hayashi, Kotaro; Moriguchi, Koji; Morito, Shigekazu

doi:10.1016/j.mtcomm.2020.101514

Cited by 24 publications

(12 citation statements)

References 28 publications

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“…Future work will include correlative characterization combining EBSD, SEM, and LM, as described in Müller et al [25]. Examples of using EBSD for ML-based microstructure classification can be found in [22,44]. In this correlative approach, EBSD is an ideally complementary information source to LM and SEM, as it is based on measuring crystallographic orientations and does not have the subjective component of how the microstructure visually appears to the human expert eye in the microscope.…”

Section: Discussionmentioning

confidence: 99%

“…Müller et al [21] employed textural parameters combined with ML to classify pearlite, martensite, and four bainite subclasses in specifically produced reference samples. Textural parameters and ML were also used by Tsutsui et al [22] for classifying samples with bainite and martensite. Non-ML based approaches for bainite classifications include Zajac et al [15,23], who utilized misorientation angle distribution from EBSD measurements to differentiate granular, upper, and lower bainite.…”

Section: Introductionmentioning

confidence: 99%

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Microstructural Classification of Bainitic Subclasses in Low-Carbon Multi-Phase Steels Using Machine Learning Techniques

Müller

Britz

Staudt

et al. 2021

Metals

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With its excellent property combinations and ability to specifically adjust tailor-made microstructures, steel is still the world’s most important engineering and construction material. To fulfill ever-increasing demands and tighter tolerances in today’s steel industry, steel research remains indispensable. The continuous material development leads to more and more complex microstructures, which is especially true for steel designs that include bainitic structures. This poses new challenges for the classification and quantification of these microstructures. Machine learning (ML) based microstructure classification offers exciting potentials in this context. This paper is concerned with the automated, objective, and reproducible classification of the carbon-rich second phase objects in multi-phase steels by using machine learning techniques. For successful applications of ML-based classifications, a holistic approach combining computer science expertise and material science domain knowledge is necessary. Seven microstructure classes are considered: pearlite, martensite, and the bainitic subclasses degenerate pearlite, debris of cementite, incomplete transformation product, and upper and lower bainite, which can all be present simultaneously in one micrograph. Based on SEM images, textural features (Haralick parameters and local binary pattern) and morphological parameters are calculated and classified with a support vector machine. Of all second phase objects, 82.9% are classified correctly. Regarding the total area of these objects, 89.2% are classified correctly. The reported classification can be the basis for an improved, sophisticated microstructure quantification, enabling process–microstructure–property correlations to be established and thereby forming the backbone of further, microstructure-centered material development.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Microstructural Classification of Bainitic Subclasses in Low-Carbon Multi-Phase Steels Using Machine Learning Techniques

Müller

Britz

Staudt

et al. 2021

Metals

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“…The use of these descriptors is usually called “perceptual characterisation” and is useful for a coarse classification of textures [ 98 ]. A quantitative description of a texture requires the use of advanced computational methods, for instance a multiresolution decomposition using Gabor wavelets [ 98 ], artificial neural networks [ 99 ], as well as a textural analysis based on a gray-level co-occurrence matrix (GLCM) [ 100 ].…”

Section: Morphometric Analysis Of Model Eukaryotic Cells: B35 Neuroblastoma Cellsmentioning

confidence: 99%

“…Since SEM enables to capture fine shapes of biological objects, this technique has been routinely used to collect images suitable for the analysis of textural descriptors [ 99 , 100 , 101 , 102 ]. Still, it should be borne in mind that the processing method could affect the texture of a sample.…”

Section: Morphometric Analysis Of Model Eukaryotic Cells: B35 Neuroblastoma Cellsmentioning

confidence: 99%

Guidelines for a Morphometric Analysis of Prokaryotic and Eukaryotic Cells by Scanning Electron Microscopy

Czerwińska-Główka

Krukiewicz

2021

Cells

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The invention of a scanning electron microscopy (SEM) pushed the imaging methods and allowed for the observation of cell details with a high resolution. Currently, SEM appears as an extremely useful tool to analyse the morphology of biological samples. The aim of this paper is to provide a set of guidelines for using SEM to analyse morphology of prokaryotic and eukaryotic cells, taking as model cases Escherichia coli bacteria and B-35 rat neuroblastoma cells. Herein, we discuss the necessity of a careful sample preparation and provide an optimised protocol that allows to observe the details of cell ultrastructure (≥ 50 nm) with a minimum processing effort. Highlighting the versatility of morphometric descriptors, we present the most informative parameters and couple them with molecular processes. In this way, we indicate the wide range of information that can be collected through SEM imaging of biological materials that makes SEM a convenient screening method to detect cell pathology.

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“…6) The traditional experiment-based microstructural characterization is mostly performed by stereological measurements, which highly depend on prior metallurgical expertise and often lead to significant individual errors. With the launch of artificial intelligence in materials science, machine-learning-based approaches have been increasingly applied to microstructure identification, 7) recognition, 8,9) detection, 10) etc., which efficiently overcome the limitations of the experiment-based characterization. Nevertheless, most of such machine-learning-based microstructural analyses lack an explicit quantitative strategy of the identified microstructural features, and thus have a difficulty in establishing a data-driven microstructure-property linkage.…”

Section: Introductionmentioning

confidence: 99%

Characterization and Optimization of Pearlite Microstructure Using Persistent Homology and Bayesian Optimization

et al. 2022

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Pearlite spheroidization is substantially a spatial-geometric evolution of cementite. In this study, a persistent homology analysis was employed to characterize the topological features of cementite component of pearlite steel, through which the lamellar and spherical pearlite microstructures were successfully distinguished. Because the mechanical performance of pearlite steel is highly sensitive to the cementite configuration, an inverse conversion of persistent-homology digital data to an image for some properties of interest was proposed by using Bayesian optimization. The proposed microstructural optimization approach paves a way to interpret persistent-homology information in metallurgy and presents the feasibility of data-driven persistent-homology-based property predictions and microstructural optimization.

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A methodology of steel microstructure recognition using SEM images by machine learning based on textural analysis

Cited by 24 publications

References 28 publications

Microstructural Classification of Bainitic Subclasses in Low-Carbon Multi-Phase Steels Using Machine Learning Techniques

Microstructural Classification of Bainitic Subclasses in Low-Carbon Multi-Phase Steels Using Machine Learning Techniques

Guidelines for a Morphometric Analysis of Prokaryotic and Eukaryotic Cells by Scanning Electron Microscopy

Characterization and Optimization of Pearlite Microstructure Using Persistent Homology and Bayesian Optimization

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