A review on inverse analysis models in steel material design
Abstract This paper reviews various inverse analysis models used in steel material design, with a focus on integrating process, microstructure, and properties through advanced machine learning techniques. The study underscores the importance of establishing comprehensive models that effectively link...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley-VCH
2024-12-01
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| Series: | Materials Genome Engineering Advances |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/mgea.71 |
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| _version_ | 1841543271249084416 |
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| author | Yoshitaka Adachi Ta‐Te Chen Fei Sun Daichi Maruyama Kengo Sawai Yoshihito Fukatsu Zhi‐Lei Wang |
| author_facet | Yoshitaka Adachi Ta‐Te Chen Fei Sun Daichi Maruyama Kengo Sawai Yoshihito Fukatsu Zhi‐Lei Wang |
| author_sort | Yoshitaka Adachi |
| collection | DOAJ |
| description | Abstract This paper reviews various inverse analysis models used in steel material design, with a focus on integrating process, microstructure, and properties through advanced machine learning techniques. The study underscores the importance of establishing comprehensive models that effectively link these elements for enhanced materials engineering. Key models discussed include the convolutional neural network–artificial neural network‐coupled model, which employs convolutional neural networks for feature extraction; the Bayesian‐optimized generative adversarial network–conditional generative adversarial network model, which generates diverse virtual microstructures; the multi‐objective optimization model, which concentrates on process–property relationships; and the microstructure–process parallelization model, which correlates microstructural features with process conditions. Each model is assessed for its strengths and limitations, influencing its practical applicability in material design. The paper concludes by advocating for continued improvements in model accuracy and versatility, with the ultimate goal of enhancing steel properties and expanding the scope of data‐driven material development. |
| format | Article |
| id | doaj-art-5a516239b02947a4ad3c95ebe9bcd30b |
| institution | Kabale University |
| issn | 2940-9489 2940-9497 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Materials Genome Engineering Advances |
| spelling | doaj-art-5a516239b02947a4ad3c95ebe9bcd30b2025-01-13T15:15:31ZengWiley-VCHMaterials Genome Engineering Advances2940-94892940-94972024-12-0124n/an/a10.1002/mgea.71A review on inverse analysis models in steel material designYoshitaka Adachi0Ta‐Te Chen1Fei Sun2Daichi Maruyama3Kengo Sawai4Yoshihito Fukatsu5Zhi‐Lei Wang6Nagoya University Nagoya JapanNagoya University Nagoya JapanNagoya University Nagoya JapanNagoya University Nagoya JapanNagoya University Nagoya JapanNagoya University Nagoya JapanUniversity of Science and Technology Beijing Beijing ChinaAbstract This paper reviews various inverse analysis models used in steel material design, with a focus on integrating process, microstructure, and properties through advanced machine learning techniques. The study underscores the importance of establishing comprehensive models that effectively link these elements for enhanced materials engineering. Key models discussed include the convolutional neural network–artificial neural network‐coupled model, which employs convolutional neural networks for feature extraction; the Bayesian‐optimized generative adversarial network–conditional generative adversarial network model, which generates diverse virtual microstructures; the multi‐objective optimization model, which concentrates on process–property relationships; and the microstructure–process parallelization model, which correlates microstructural features with process conditions. Each model is assessed for its strengths and limitations, influencing its practical applicability in material design. The paper concludes by advocating for continued improvements in model accuracy and versatility, with the ultimate goal of enhancing steel properties and expanding the scope of data‐driven material development.https://doi.org/10.1002/mgea.71GANimage regressioninverse analysismultiple‐objective optimizationsteel |
| spellingShingle | Yoshitaka Adachi Ta‐Te Chen Fei Sun Daichi Maruyama Kengo Sawai Yoshihito Fukatsu Zhi‐Lei Wang A review on inverse analysis models in steel material design Materials Genome Engineering Advances GAN image regression inverse analysis multiple‐objective optimization steel |
| title | A review on inverse analysis models in steel material design |
| title_full | A review on inverse analysis models in steel material design |
| title_fullStr | A review on inverse analysis models in steel material design |
| title_full_unstemmed | A review on inverse analysis models in steel material design |
| title_short | A review on inverse analysis models in steel material design |
| title_sort | review on inverse analysis models in steel material design |
| topic | GAN image regression inverse analysis multiple‐objective optimization steel |
| url | https://doi.org/10.1002/mgea.71 |
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