State-of-the-Art Review on the Aspects of Martensitic Alloys Studied via Machine Learning
Though the martensitic transformation has been a commonly investigated topic in the field of experimental and computational materials science, the understanding of this mechanism in a variety of alloys is yet far from complete. In this era of Industry 4.0, there have been ongoing trends on employing...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/12/11/1884 |
| _version_ | 1797467221503508480 |
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| author | Upadesh Subedi Sachin Poudel Khem Gyanwali Yuri Amorim Coutinho Grzegorz Matula Anil Kunwar |
| author_facet | Upadesh Subedi Sachin Poudel Khem Gyanwali Yuri Amorim Coutinho Grzegorz Matula Anil Kunwar |
| author_sort | Upadesh Subedi |
| collection | DOAJ |
| description | Though the martensitic transformation has been a commonly investigated topic in the field of experimental and computational materials science, the understanding of this mechanism in a variety of alloys is yet far from complete. In this era of Industry 4.0, there have been ongoing trends on employing machine learning (ML) techniques for the study of the martensitic alloys, and such data-driven approaches are expected to unravel a great amount of information about the process-structure-property behaviour relationship in this class of materials. However, with the availability of a large variety of datasets and with an option to use different ML models, a bulk amount of information has already been generated with regard to martensitic alloys. The discovery and design of shape memory alloys can be accelerated if the multi-principal element functional alloys and martensitic transformation phenomenon are studied extensively using machine learning techniques. Thus, it is necessary to highlight the major categories or aspects of these alloys that have been predicted with ML. The present work performs a state-of-the-art review on the machine learning models developed for the quantification of aspects such as martensitic start temperature (Ms), materials properties, microstructure, mechanisms etc., on the alloys. |
| first_indexed | 2024-03-09T18:50:36Z |
| format | Article |
| id | doaj.art-21a4920fa0104567baf439f846e7b22b |
| institution | Directory Open Access Journal |
| issn | 2075-4701 |
| language | English |
| last_indexed | 2024-03-09T18:50:36Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj.art-21a4920fa0104567baf439f846e7b22b2023-11-24T05:52:40ZengMDPI AGMetals2075-47012022-11-011211188410.3390/met12111884State-of-the-Art Review on the Aspects of Martensitic Alloys Studied via Machine LearningUpadesh Subedi0Sachin Poudel1Khem Gyanwali2Yuri Amorim Coutinho3Grzegorz Matula4Anil Kunwar5Department of Automobile and Mechanical Engineering, Institute of Engineering, Thapathali Campus, Tribhuvan University, Kathmandu 44600, NepalDepartment of Automobile and Mechanical Engineering, Institute of Engineering, Thapathali Campus, Tribhuvan University, Kathmandu 44600, NepalDepartment of Automobile and Mechanical Engineering, Institute of Engineering, Thapathali Campus, Tribhuvan University, Kathmandu 44600, NepalDepartment of Materials Engineering, KU Leuven, Kasteelpark Arenberg 44, B-3001 Leuven, BelgiumFaculty of Mechanical Engineering, Silesian University of Technology, Konarskiego 18A, 44-100 Gliwice, PolandFaculty of Mechanical Engineering, Silesian University of Technology, Konarskiego 18A, 44-100 Gliwice, PolandThough the martensitic transformation has been a commonly investigated topic in the field of experimental and computational materials science, the understanding of this mechanism in a variety of alloys is yet far from complete. In this era of Industry 4.0, there have been ongoing trends on employing machine learning (ML) techniques for the study of the martensitic alloys, and such data-driven approaches are expected to unravel a great amount of information about the process-structure-property behaviour relationship in this class of materials. However, with the availability of a large variety of datasets and with an option to use different ML models, a bulk amount of information has already been generated with regard to martensitic alloys. The discovery and design of shape memory alloys can be accelerated if the multi-principal element functional alloys and martensitic transformation phenomenon are studied extensively using machine learning techniques. Thus, it is necessary to highlight the major categories or aspects of these alloys that have been predicted with ML. The present work performs a state-of-the-art review on the machine learning models developed for the quantification of aspects such as martensitic start temperature (Ms), materials properties, microstructure, mechanisms etc., on the alloys.https://www.mdpi.com/2075-4701/12/11/1884martensite start temperaturemachine learningmartensitic transformationtransformation diagramshape memory alloys |
| spellingShingle | Upadesh Subedi Sachin Poudel Khem Gyanwali Yuri Amorim Coutinho Grzegorz Matula Anil Kunwar State-of-the-Art Review on the Aspects of Martensitic Alloys Studied via Machine Learning Metals martensite start temperature machine learning martensitic transformation transformation diagram shape memory alloys |
| title | State-of-the-Art Review on the Aspects of Martensitic Alloys Studied via Machine Learning |
| title_full | State-of-the-Art Review on the Aspects of Martensitic Alloys Studied via Machine Learning |
| title_fullStr | State-of-the-Art Review on the Aspects of Martensitic Alloys Studied via Machine Learning |
| title_full_unstemmed | State-of-the-Art Review on the Aspects of Martensitic Alloys Studied via Machine Learning |
| title_short | State-of-the-Art Review on the Aspects of Martensitic Alloys Studied via Machine Learning |
| title_sort | state of the art review on the aspects of martensitic alloys studied via machine learning |
| topic | martensite start temperature machine learning martensitic transformation transformation diagram shape memory alloys |
| url | https://www.mdpi.com/2075-4701/12/11/1884 |
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