Objective microstructure classification by support vector machine (SVM) using a combination of morphological parameters and textural features for low carbon steels

Gola, Jessica; Webel, Johannes; Britz, Dominik; Guitar, Agustina; Staudt, Thorsten; Winter, M.; Mücklich, Frank

doi:10.1016/j.commatsci.2019.01.006

Cited by 76 publications

(49 citation statements)

References 26 publications

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“…The only drawback of their approach is that Deep Learning is inherently difficult to interpret. Gola et al [12] worked with the same data set, but used morphological and textural parameters in a combination with a support vector machine for classification. This approach can be better linked to the metallurgical processes and material properties.…”

Section: Discussionmentioning

confidence: 99%

“…For classification, the workflow presented by Gola et al [12,27], using a support vector machine (SVM) was adopted. An SVM classifies data by finding the best hyperplane that separates the data points of one class from the data points of another class.…”

Section: Classification Process Using Support Vector Machinementioning

confidence: 99%

“…Considering the aforementioned challenges when dealing with bainite, it is not surprising that not many approaches for an automated classification of steel microstructures, including bainite subclasses can be found in the literature. Gola et al [12] used a combination of morphological and textural parameters with a support vector machine (SVM) to classify the carbon-rich second phase of two phase steels into pearlite, bainite, and martensite. However, bainite subclasses were not considered, as all structures that were neither pearlite nor martensite were put into one bainite class.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Classification Process Using Support Vector Machinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Classification of Bainitic Structures Using Textural Parameters and Machine Learning Techniques

Müller

Britz

Ulrich

et al. 2020

Metals

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“…Research in automated microstructure recognition has mainly focused on distinguishing between groups of materials with significantly different compositions and processing conditions and consequently clear visual differences. When examining the literature one quickly finds several recent examples, all of which report close to perfect performance 5,7,12,13 . However, the question remains whether it is possible for a machine learning model to learn to distinguish between materials with only minor differences in composition and processing.…”

Section: Introductionmentioning

confidence: 99%

Compact representations of microstructure images using triplet networks

et al. 2020

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The microstructure of a material, typically characterized through a set of microscopy images of two-dimensional cross-sections, is a valuable source of information about the material and its properties. Every pixel of the image is a degree of freedom causing the dimensionality of the information space to be extremely high. This makes it difficult to recognize and extract all relevant information from the images. Human experts circumvent this by manually creating a lower-dimensional representation of the microstructure. However, the question of how a microstructure image can be best represented remains open. From the field of deep learning, we present triplet networks as a method to build highly compact representations of the microstructure, condensing the relevant information into a much smaller number of dimensions. We demonstrate that these representations can be created even with a limited amount of example images, and that they are able to distinguish between visually very similar microstructures. We discuss the interpretability and generalization of the representations. Having compact microstructure representations, it becomes easier to establish processing–structure–property links that are key to rational materials design.

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“…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. The applicability of textural parameters to distinguish different microstructures was also shown by Webel et al [6] who distinguished pearlite, lower bainite and martensite with Haralick textural features or by Arivazhagan et al [7] where local ternary patterns were used to differentiate low-, medium-and high-carbon steels.…”

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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Objective microstructure classification by support vector machine (SVM) using a combination of morphological parameters and textural features for low carbon steels

Cited by 76 publications

References 26 publications

Classification of Bainitic Structures Using Textural Parameters and Machine Learning Techniques

Classification of Bainitic Structures Using Textural Parameters and Machine Learning Techniques

Compact representations of microstructure images using triplet networks

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

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