Abstract
The phase field model for multicomponent alloys are usually coupled with the thermodynamic database. But it will take much time to solve the quasi phase equilibrium equations. In order to reduce such time and keep the enough accuracy, we develop a new model to predict the quasi phase equilibrium based on the machine learning method. As an example, the quasi phase equilibrium during the isothermal solidification of Al-Cu-Mg alloy is studied in detail. A neural network model with 3 inputs, 4 outputs and a hidden layer of 150 nodes is constructed. The “training data” are prepared by solving the quasi phase equilibrium equations with least square method. The neural network model is trained by different amount of data set, which can fully cover the ranges of all the variables. The accuracy and performance of the neural network model are discussed in detail. Its high accuracy and fast speed demonstrated that this will be a convenient method to acquire the quasi phase equilibrium data in phase field model for multicomponent alloys.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.