Calculation of phase fraction in steel microstructure images using random forest classifier

Paul, Angshuman; Gangopadhyay, Abhinandan; Chintha, Appa Rao; Mukherjee, Dipti Prasad; Das, Prasun; Kundu, Saurabh

doi:10.1049/iet-ipr.2017.1154

Cited by 18 publications

(9 citation statements)

References 37 publications

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Section: Discussionmentioning

confidence: 99%

“…Many works distinguished bainite from martensite, pearlite and ferrite on either SEM or LOM pictures [1,8,15,17,18,23] or EBSD data [12,22]. Few authors made the distinction between upper bainite and ferrite [8,18]. However, most of these models were only assigning a single class to a whole EBSD map and were not performing a segmentation of the analyzed microstructure.…”

Section: Discussionmentioning

confidence: 99%

“…Estimation of phase transformation products in low-carbon steels (martensite, ferrite, bainite, pearlite) is crucial to understand and predict their mechanical behavior. Several authors have therefore focused on automating this task using machine learning approaches, taking Light Optical Microscopy (LOM) or scanning electron microscope (SEM) secondary electron pictures of etched samples as input [1,4,8,13,15,17,18,23].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Leveraging EBSD data by deep learning for bainite, ferrite and martensite segmentation

Breumier

Ostormujof²,

Gey³

et al. 2022

Materials Characterization

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Leveraging EBSD data by deep learning for bainite, ferrite and martensite segmentation

Breumier

Ostormujof²,

Gey³

et al. 2022

Materials Characterization

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“…In Miyama et al [17] upper and lower bainite are distinguished by calculating morphological parameters of the cementite precipitates. Banerjee et al [18] differentiate ferrite, bainite, and martensite by using intensity values as well as density of substructure particles, whereas Paul et al [19] use regional contour pattern and local entropy to segment ferrite, martensite and bainite in dual-phase-steels. Regarding the suggested classifications schemes for bainite mentioned earlier, there are no reported automated workflows who use the differences cited in the definition of these bainite classes for a classification task.…”

Section: Introductionmentioning

confidence: 99%

Classification of Bainitic Structures Using Textural Parameters and Machine Learning Techniques

Müller

Britz

Ulrich

et al. 2020

Metals

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Bainite is an essential constituent of modern high strength steels. In addition to the still great challenge of characterization, the classification of bainite poses difficulties. Challenges when dealing with bainite are the variety and amount of involved phases, the fineness and complexity of the structures and that there is often no consensus among human experts in labeling and classifying those. Therefore, an objective and reproducible characterization and classification is crucial. To achieve this, it is necessary to analyze the substructure of bainite using scanning electron microscope (SEM). This work will present how textural parameters (Haralick features and local binary pattern) calculated from SEM images, taken from specifically produced benchmark samples with defined structures, can be used to distinguish different bainitic microstructures by using machine learning techniques (support vector machine). For the classification task of distinguishing pearlite, granular, degenerate upper, upper and lower bainite as well as martensite a classification accuracy of 91.80% was achieved, by combining Haralick features and local binary pattern.

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“…However, images where both bainite types are present at once were not analyzed. Banerjee et al [3] distinguished ferrite, bainite and martensite by using intensity values and the density of substructure particles, while Paul et al [4] employed regional contour pattern and local entropy for segmenting ferrite, martensite and bainite in dual-phase steels. Gola et al [5] presented a workflow for two-phase steels, in which after first segmenting the carbon-rich phase (pearlite, bainite or martensite) against the ferritic matrix by thresholding, morphological and textural 1 3 parameters are used to classify pearlite, bainite and martensite.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Lath-Like Structures via Localized Identification of Directionality in a Complex-Phase Steel

Müller

Stanke²,

Sonntag³

et al. 2020

Metallogr. Microstruct. Anal.

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In this work, a segmentation approach based on analyzing local orientations and directions in an image, in order to distinguish lath-like from granular structures, is presented. It is based on common image processing operations. A window of appropriate size slides over the image, and the gradient direction and its magnitude inside this window are determined for each pixel. The histogram of all possible directions yields the main direction and its directionality. These two parameters enable the extraction of window positions which represent lath-like structures, and procedures to join these positions are developed. The usability of this approach is demonstrated by distinguishing lath-like bainite from granular bainite in so-called complex-phase steels, a segmentation task for which automated procedures are not yet reported. The segmentation results are in accordance with the regions recognized by human experts. The approach’s main advantages are its use on small sets of images, the easy access to the segmentation process and therefore a targeted adjustment of parameters to achieve the best possible segmentation result. Thus, it is distinct from segmentation using deep learning which is becoming more and more popular and is a promising solution for complex segmentation tasks, but requires large image sets for training and is difficult to interpret.

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Calculation of phase fraction in steel microstructure images using random forest classifier

Cited by 18 publications

References 37 publications

Leveraging EBSD data by deep learning for bainite, ferrite and martensite segmentation

Leveraging EBSD data by deep learning for bainite, ferrite and martensite segmentation

Classification of Bainitic Structures Using Textural Parameters and Machine Learning Techniques

Segmentation of Lath-Like Structures via Localized Identification of Directionality in a Complex-Phase Steel

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