Prediction of metal temperature by microstructural features in creep exposed austenitic stainless steel with sparse modeling
This study proposes a framework to estimate the metal temperature from an optical micrograph of metals by using a machine learning approach. Specifically, 38 image statistical parameters such as area, contour, and circularity are calculated for the precipitate region determined through optical micro...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2021-01-01
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| Series: | Science and Technology of Advanced Materials: Methods |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/27660400.2021.1997556 |
| _version_ | 1797685789511909376 |
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| author | Akihiro Endo Kota Sawada Kenji Nagata Hideki Yoshikawa Hayaru Shouno |
| author_facet | Akihiro Endo Kota Sawada Kenji Nagata Hideki Yoshikawa Hayaru Shouno |
| author_sort | Akihiro Endo |
| collection | DOAJ |
| description | This study proposes a framework to estimate the metal temperature from an optical micrograph of metals by using a machine learning approach. Specifically, 38 image statistical parameters such as area, contour, and circularity are calculated for the precipitate region determined through optical microscopy. Sparse modeling is then conducted to build a statistical model to estimate the Larson-Miller parameter (LMP), which is generally used in the evaluation of creep strength. This allows for the prediction of the metal temperature from the optical micrographs. The prediction performance of the proposed method is analyzed by applying it to KA-SUS304J1HTB (18Cr-9Ni-3Cu-Nb-N steel), reported in the NIMS Creep Data Sheets No. 56A and No. M-11. Consequently, temperature prediction is successfully achieved for unknown data with an error within ± 10°C. |
| first_indexed | 2024-03-12T00:56:28Z |
| format | Article |
| id | doaj.art-ae5cc286942749ecb5966a5787355ecb |
| institution | Directory Open Access Journal |
| issn | 2766-0400 |
| language | English |
| last_indexed | 2024-03-12T00:56:28Z |
| publishDate | 2021-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Science and Technology of Advanced Materials: Methods |
| spelling | doaj.art-ae5cc286942749ecb5966a5787355ecb2023-09-14T13:24:39ZengTaylor & Francis GroupScience and Technology of Advanced Materials: Methods2766-04002021-01-011122523310.1080/27660400.2021.19975561997556Prediction of metal temperature by microstructural features in creep exposed austenitic stainless steel with sparse modelingAkihiro Endo0Kota Sawada1Kenji Nagata2Hideki Yoshikawa3Hayaru Shouno4Graduate School of Informatics and Engineering, The University of Electro-CommunicationsNational Institute for Materials ScienceNational Institute for Materials ScienceNational Institute for Materials ScienceGraduate School of Informatics and Engineering, The University of Electro-CommunicationsThis study proposes a framework to estimate the metal temperature from an optical micrograph of metals by using a machine learning approach. Specifically, 38 image statistical parameters such as area, contour, and circularity are calculated for the precipitate region determined through optical microscopy. Sparse modeling is then conducted to build a statistical model to estimate the Larson-Miller parameter (LMP), which is generally used in the evaluation of creep strength. This allows for the prediction of the metal temperature from the optical micrographs. The prediction performance of the proposed method is analyzed by applying it to KA-SUS304J1HTB (18Cr-9Ni-3Cu-Nb-N steel), reported in the NIMS Creep Data Sheets No. 56A and No. M-11. Consequently, temperature prediction is successfully achieved for unknown data with an error within ± 10°C.http://dx.doi.org/10.1080/27660400.2021.1997556stainless steelcreepmicrostructural featureslarson-miller parameterregression analysissparse modeling |
| spellingShingle | Akihiro Endo Kota Sawada Kenji Nagata Hideki Yoshikawa Hayaru Shouno Prediction of metal temperature by microstructural features in creep exposed austenitic stainless steel with sparse modeling Science and Technology of Advanced Materials: Methods stainless steel creep microstructural features larson-miller parameter regression analysis sparse modeling |
| title | Prediction of metal temperature by microstructural features in creep exposed austenitic stainless steel with sparse modeling |
| title_full | Prediction of metal temperature by microstructural features in creep exposed austenitic stainless steel with sparse modeling |
| title_fullStr | Prediction of metal temperature by microstructural features in creep exposed austenitic stainless steel with sparse modeling |
| title_full_unstemmed | Prediction of metal temperature by microstructural features in creep exposed austenitic stainless steel with sparse modeling |
| title_short | Prediction of metal temperature by microstructural features in creep exposed austenitic stainless steel with sparse modeling |
| title_sort | prediction of metal temperature by microstructural features in creep exposed austenitic stainless steel with sparse modeling |
| topic | stainless steel creep microstructural features larson-miller parameter regression analysis sparse modeling |
| url | http://dx.doi.org/10.1080/27660400.2021.1997556 |
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