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 |
| Published: |
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 |