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...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.展开更多
基金funded by a Grant-in-Aid for Transformative Research Areas 21H05194“Material Creation in Super Field”and 22H01807 from the Japanese Grants-in-Aid for Scientific Research.
文摘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.
