Texture-Based Metallurgical Phase Identification in Structural Steels: A Supervised Machine Learning Approach
Automatic identification of metallurgical phases based on thresholding methods in microstructural images may not be possible when the pixel intensities associated with the metallurgical phases overlap and, hence, are indistinguishable. To circumvent this problem, additional visual information about...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-05-01
|
| Series: | Metals |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4701/9/5/546 |
| _version_ | 1819163701319565312 |
|---|---|
| author | Dayakar L. Naik Hizb Ullah Sajid Ravi Kiran |
| author_facet | Dayakar L. Naik Hizb Ullah Sajid Ravi Kiran |
| author_sort | Dayakar L. Naik |
| collection | DOAJ |
| description | Automatic identification of metallurgical phases based on thresholding methods in microstructural images may not be possible when the pixel intensities associated with the metallurgical phases overlap and, hence, are indistinguishable. To circumvent this problem, additional visual information about the metallurgical phases, referred to as textural features, are considered in this study. Mathematically, textural features are the second order statistics of an image domain and can be distinct for each metallurgical phase. Textural features are evaluated from the gray level co-occurrence matrix (GLCM) of each metallurgical phase (ferrite, pearlite, and martensite) present in heat-treated ASTM A36 steels in this study. The dataset of textural features and pixel intensities generated for the metallurgical phases is used to train supervised machine learning classifiers, which are subsequently employed to predict the metallurgical phases in the microstructure. Naïve Bayes (NB), k-nearest neighbor (K-NN), linear discriminant analysis (LDA), and decision tree (DT) classifiers are the four classifiers employed in this study. The performances of all four classifiers were assessed prior to their deployment, and the classification accuracy was found to be >97%. The proposed technique has two unique advantages: (1) unlike pixel intensity-based methods, the proposed method does not misclassify the grain boundaries as a metallurgical phase, and (2) the proposed method does not require the end-user to input the number of phases present in the microstructure. |
| first_indexed | 2024-12-22T17:48:19Z |
| format | Article |
| id | doaj.art-88a89b050ee64c6e946d3d9de191eb72 |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-12-22T17:48:19Z |
| publishDate | 2019-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-88a89b050ee64c6e946d3d9de191eb722022-12-21T18:18:14ZengMDPI AGMetals2075-47012019-05-019554610.3390/met9050546met9050546Texture-Based Metallurgical Phase Identification in Structural Steels: A Supervised Machine Learning ApproachDayakar L. Naik0Hizb Ullah Sajid1Ravi Kiran2Department of Civil & Environmental Engineering, North Dakota State University, ND 58105, USADepartment of Civil & Environmental Engineering, North Dakota State University, ND 58105, USADepartment of Civil & Environmental Engineering, North Dakota State University, ND 58105, USAAutomatic identification of metallurgical phases based on thresholding methods in microstructural images may not be possible when the pixel intensities associated with the metallurgical phases overlap and, hence, are indistinguishable. To circumvent this problem, additional visual information about the metallurgical phases, referred to as textural features, are considered in this study. Mathematically, textural features are the second order statistics of an image domain and can be distinct for each metallurgical phase. Textural features are evaluated from the gray level co-occurrence matrix (GLCM) of each metallurgical phase (ferrite, pearlite, and martensite) present in heat-treated ASTM A36 steels in this study. The dataset of textural features and pixel intensities generated for the metallurgical phases is used to train supervised machine learning classifiers, which are subsequently employed to predict the metallurgical phases in the microstructure. Naïve Bayes (NB), k-nearest neighbor (K-NN), linear discriminant analysis (LDA), and decision tree (DT) classifiers are the four classifiers employed in this study. The performances of all four classifiers were assessed prior to their deployment, and the classification accuracy was found to be >97%. The proposed technique has two unique advantages: (1) unlike pixel intensity-based methods, the proposed method does not misclassify the grain boundaries as a metallurgical phase, and (2) the proposed method does not require the end-user to input the number of phases present in the microstructure.https://www.mdpi.com/2075-4701/9/5/546Gray level co-occurrence matrix (GLCM), ASTM A36steel microstructuretextural featuresmachine learning classifiers |
| spellingShingle | Dayakar L. Naik Hizb Ullah Sajid Ravi Kiran Texture-Based Metallurgical Phase Identification in Structural Steels: A Supervised Machine Learning Approach Metals Gray level co-occurrence matrix (GLCM), ASTM A36 steel microstructure textural features machine learning classifiers |
| title | Texture-Based Metallurgical Phase Identification in Structural Steels: A Supervised Machine Learning Approach |
| title_full | Texture-Based Metallurgical Phase Identification in Structural Steels: A Supervised Machine Learning Approach |
| title_fullStr | Texture-Based Metallurgical Phase Identification in Structural Steels: A Supervised Machine Learning Approach |
| title_full_unstemmed | Texture-Based Metallurgical Phase Identification in Structural Steels: A Supervised Machine Learning Approach |
| title_short | Texture-Based Metallurgical Phase Identification in Structural Steels: A Supervised Machine Learning Approach |
| title_sort | texture based metallurgical phase identification in structural steels a supervised machine learning approach |
| topic | Gray level co-occurrence matrix (GLCM), ASTM A36 steel microstructure textural features machine learning classifiers |
| url | https://www.mdpi.com/2075-4701/9/5/546 |
| work_keys_str_mv | AT dayakarlnaik texturebasedmetallurgicalphaseidentificationinstructuralsteelsasupervisedmachinelearningapproach AT hizbullahsajid texturebasedmetallurgicalphaseidentificationinstructuralsteelsasupervisedmachinelearningapproach AT ravikiran texturebasedmetallurgicalphaseidentificationinstructuralsteelsasupervisedmachinelearningapproach |