Materials informatics approach to understand aluminum alloys
The relations between the mechanical properties, heat treatment, and compositions of elements in aluminum alloys are extracted by a materials informatics technique. In our strategy, a machine learning model is first trained by a prepared database to predict the properties of materials. The dependenc...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2020-01-01
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| Series: | Science and Technology of Advanced Materials |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/14686996.2020.1791676 |
| _version_ | 1818940292477222912 |
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| author | Ryo Tamura Makoto Watanabe Hiroaki Mamiya Kota Washio Masao Yano Katsunori Danno Akira Kato Tetsuya Shoji |
| author_facet | Ryo Tamura Makoto Watanabe Hiroaki Mamiya Kota Washio Masao Yano Katsunori Danno Akira Kato Tetsuya Shoji |
| author_sort | Ryo Tamura |
| collection | DOAJ |
| description | The relations between the mechanical properties, heat treatment, and compositions of elements in aluminum alloys are extracted by a materials informatics technique. In our strategy, a machine learning model is first trained by a prepared database to predict the properties of materials. The dependence of the predicted properties on explanatory variables, that is, the type of heat treatment and element composition, is searched using a Markov chain Monte Carlo method. From the dependencies, a factor to obtain the desired properties is investigated. Using targets of 5000, 6000, and 7000 series aluminum alloys, we extracted relations that are difficult to find via simple correlation analysis. Our method is also used to design an experimental plan to optimize the materials properties while promoting the understanding of target materials. |
| first_indexed | 2024-12-20T06:37:19Z |
| format | Article |
| id | doaj.art-b6d3fcf3770f4b578a4a8c70f6d1dc3f |
| institution | Directory Open Access Journal |
| issn | 1468-6996 1878-5514 |
| language | English |
| last_indexed | 2024-12-20T06:37:19Z |
| publishDate | 2020-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Science and Technology of Advanced Materials |
| spelling | doaj.art-b6d3fcf3770f4b578a4a8c70f6d1dc3f2022-12-21T19:49:58ZengTaylor & Francis GroupScience and Technology of Advanced Materials1468-69961878-55142020-01-0121154055110.1080/14686996.2020.17916761791676Materials informatics approach to understand aluminum alloysRyo Tamura0Makoto Watanabe1Hiroaki Mamiya2Kota Washio3Masao Yano4Katsunori Danno5Akira Kato6Tetsuya Shoji7National Institute for Materials ScienceNational Institute for Materials ScienceNational Institute for Materials ScienceToyota Motor CorporationToyota Motor CorporationToyota Motor CorporationToyota Motor CorporationToyota Motor CorporationThe relations between the mechanical properties, heat treatment, and compositions of elements in aluminum alloys are extracted by a materials informatics technique. In our strategy, a machine learning model is first trained by a prepared database to predict the properties of materials. The dependence of the predicted properties on explanatory variables, that is, the type of heat treatment and element composition, is searched using a Markov chain Monte Carlo method. From the dependencies, a factor to obtain the desired properties is investigated. Using targets of 5000, 6000, and 7000 series aluminum alloys, we extracted relations that are difficult to find via simple correlation analysis. Our method is also used to design an experimental plan to optimize the materials properties while promoting the understanding of target materials.http://dx.doi.org/10.1080/14686996.2020.1791676materials informaticsaluminum alloysmarkov chain monte carlo |
| spellingShingle | Ryo Tamura Makoto Watanabe Hiroaki Mamiya Kota Washio Masao Yano Katsunori Danno Akira Kato Tetsuya Shoji Materials informatics approach to understand aluminum alloys Science and Technology of Advanced Materials materials informatics aluminum alloys markov chain monte carlo |
| title | Materials informatics approach to understand aluminum alloys |
| title_full | Materials informatics approach to understand aluminum alloys |
| title_fullStr | Materials informatics approach to understand aluminum alloys |
| title_full_unstemmed | Materials informatics approach to understand aluminum alloys |
| title_short | Materials informatics approach to understand aluminum alloys |
| title_sort | materials informatics approach to understand aluminum alloys |
| topic | materials informatics aluminum alloys markov chain monte carlo |
| url | http://dx.doi.org/10.1080/14686996.2020.1791676 |
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