Characterization of micrographs and fractographs of Cu-strengthened HSLA steel using image texture analysis

Dutta, Samik; Barat, Kaustav; Das, Arijit; Das, Swapan Kumar; Shukla, A. K.; Roy, Himadri

doi:10.1016/j.measurement.2013.08.030

Cited by 21 publications

(8 citation statements)

References 19 publications

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“…Correlation is a measure of how correlated a pixel is to its neighbor over the whole image, i.e., the joint probability occurrence of the specified pixel pairs [29]. Energy measures the uniformity of the gray level distribution in an image [30]. Few entries in the GLCM that have high probability, lead to a high energy value.…”

Section: Classification Resultsmentioning

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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Section: Classification Resultsmentioning

confidence: 99%

Classification of Bainitic Structures Using Textural Parameters and Machine Learning Techniques

Müller

Britz

Ulrich

et al. 2020

Metals

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“…Filho et al 29 compared different kinds of features extractions and classifiers to classify microstructural states of nongrained electrical steel using photomicrographies and concludes that the GLCM feature extracting was the best of the ones considered. In Dutta et al, 30 a GLCM is used for automatic characterization of microstructures and fracture surfaces of Cu-strengthened HSLA-100 steel. Dutta et al 31 analyzed tensile fractographs of AISI 304LN austenitic stainless steel with GLCM for automatic characterization of fracture surfaces.…”

Section: Introductionmentioning

confidence: 99%

An enhanced method for the identification of ferritic morphologies in welded fusion zones based on gray-level co-occurrence matrix: A computational intelligence approach

Souza

Silva

Caldeira

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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This work focuses on the identification of five of the most common ferritic morphologies present in welded fusion zones of low carbon steel through images acquired by photomicrographies. With this regards, we discuss the importance of the gray-level co-occurrence matrix to extract the features to be used as the input of the computational intelligence techniques. We use artificial neural networks and support vector machines to identify the proportions of each morphology and present the error identification rate for each technique. The results show that the use of gray-level co-occurrence extraction allows a less intense computational model with statistical validity and the support vector machine as a computational intelligence technique allows smaller variability when compared to the artificial neural networks.

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“…The methods and techniques developed for particle size and particle size distribution measurement are numerous, such as: laser diffraction (LD), aerodynamic time-of-flight, electrical zone sensing, dynamic light scattering, photon correlation spectroscopy and optical image analysis (IA) [6][7][8][9]. Among the numerous available techniques, LD is the most frequently used technique for the characterisation of small particles [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…Also, in cases of particle aggregation, it considers aggregates as large spherical particles. Electron microscopy techniques are widely used to provide information on the size, morphology and elemental composition of particles [5,8,11]. The limitation of microscopic techniques is that obtaining statistically significant data can be extremely time consuming.…”

Section: Introductionmentioning

confidence: 99%

Size and shape particle analysis by applying image analysis and laser diffraction – Inhalable dust in a dental laboratory

et al. 2015

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Characterization of micrographs and fractographs of Cu-strengthened HSLA steel using image texture analysis

Cited by 21 publications

References 19 publications

Classification of Bainitic Structures Using Textural Parameters and Machine Learning Techniques

Classification of Bainitic Structures Using Textural Parameters and Machine Learning Techniques

An enhanced method for the identification of ferritic morphologies in welded fusion zones based on gray-level co-occurrence matrix: A computational intelligence approach

Size and shape particle analysis by applying image analysis and laser diffraction – Inhalable dust in a dental laboratory

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