Matches in SemOpenAlex for { <https://semopenalex.org/work/W4379928834> ?p ?o ?g. }
Showing items 1 to 69 of
69
with 100 items per page.
- W4379928834 endingPage "64" @default.
- W4379928834 startingPage "49" @default.
- W4379928834 abstract "In this study, a phase field model is established to simulate the microstructure formation during the solidification of dendrites by taking the Al-Cu-Mg ternary alloy as an example, and machine learning and deep learning methods are combined with the Kim-Kim-Suzuki (KKS) phase field model to predict the quasi-phase equilibrium. The paper first uses the least squares method to obtain the required data and then applies eight machine learning methods and five deep learning methods to train the quasi-phase equilibrium prediction models. After obtaining different models, this paper compares the reliability of the established models by using the test data and uses two evaluation criteria to analyze the performance of these models. This work find that the performance of the established deep learning models is generally better than that of the machine learning models, and the Multilayer Perceptron (MLP) based quasi-phase equilibrium prediction model achieves the best performance. Meanwhile the Convolutional Neural Network (CNN) based model also achieves competitive results. The experimental results show that the model proposed in this paper can predict the quasi-phase equilibrium of the KKS phase-field model accurately, which proves that it is feasible to combine machine learning and deep learning methods with phase-field model simulation." @default.
- W4379928834 created "2023-06-09" @default.
- W4379928834 creator A5003555435 @default.
- W4379928834 creator A5023770297 @default.
- W4379928834 creator A5070740938 @default.
- W4379928834 creator A5076904290 @default.
- W4379928834 creator A5088947033 @default.
- W4379928834 date "2023-01-01" @default.
- W4379928834 modified "2023-10-15" @default.
- W4379928834 title "Quasi-Phase Equilibrium Prediction of Multi-Element Alloys Based on Machine Learning and Deep Learning" @default.
- W4379928834 doi "https://doi.org/10.32604/cmc.2023.036729" @default.
- W4379928834 hasPublicationYear "2023" @default.
- W4379928834 type Work @default.
- W4379928834 citedByCount "0" @default.
- W4379928834 crossrefType "journal-article" @default.
- W4379928834 hasAuthorship W4379928834A5003555435 @default.
- W4379928834 hasAuthorship W4379928834A5023770297 @default.
- W4379928834 hasAuthorship W4379928834A5070740938 @default.
- W4379928834 hasAuthorship W4379928834A5076904290 @default.
- W4379928834 hasAuthorship W4379928834A5088947033 @default.
- W4379928834 hasBestOaLocation W43799288341 @default.
- W4379928834 hasConcept C108583219 @default.
- W4379928834 hasConcept C11413529 @default.
- W4379928834 hasConcept C119857082 @default.
- W4379928834 hasConcept C154945302 @default.
- W4379928834 hasConcept C178790620 @default.
- W4379928834 hasConcept C179717631 @default.
- W4379928834 hasConcept C185592680 @default.
- W4379928834 hasConcept C202444582 @default.
- W4379928834 hasConcept C33923547 @default.
- W4379928834 hasConcept C41008148 @default.
- W4379928834 hasConcept C44280652 @default.
- W4379928834 hasConcept C50644808 @default.
- W4379928834 hasConcept C81363708 @default.
- W4379928834 hasConcept C9652623 @default.
- W4379928834 hasConceptScore W4379928834C108583219 @default.
- W4379928834 hasConceptScore W4379928834C11413529 @default.
- W4379928834 hasConceptScore W4379928834C119857082 @default.
- W4379928834 hasConceptScore W4379928834C154945302 @default.
- W4379928834 hasConceptScore W4379928834C178790620 @default.
- W4379928834 hasConceptScore W4379928834C179717631 @default.
- W4379928834 hasConceptScore W4379928834C185592680 @default.
- W4379928834 hasConceptScore W4379928834C202444582 @default.
- W4379928834 hasConceptScore W4379928834C33923547 @default.
- W4379928834 hasConceptScore W4379928834C41008148 @default.
- W4379928834 hasConceptScore W4379928834C44280652 @default.
- W4379928834 hasConceptScore W4379928834C50644808 @default.
- W4379928834 hasConceptScore W4379928834C81363708 @default.
- W4379928834 hasConceptScore W4379928834C9652623 @default.
- W4379928834 hasIssue "1" @default.
- W4379928834 hasLocation W43799288341 @default.
- W4379928834 hasOpenAccess W4379928834 @default.
- W4379928834 hasPrimaryLocation W43799288341 @default.
- W4379928834 hasRelatedWork W2337926734 @default.
- W4379928834 hasRelatedWork W2763109982 @default.
- W4379928834 hasRelatedWork W2945765785 @default.
- W4379928834 hasRelatedWork W3211546796 @default.
- W4379928834 hasRelatedWork W4231994957 @default.
- W4379928834 hasRelatedWork W4312831135 @default.
- W4379928834 hasRelatedWork W4320802194 @default.
- W4379928834 hasRelatedWork W4366224123 @default.
- W4379928834 hasRelatedWork W4381487685 @default.
- W4379928834 hasRelatedWork W4381832759 @default.
- W4379928834 hasVolume "76" @default.
- W4379928834 isParatext "false" @default.
- W4379928834 isRetracted "false" @default.
- W4379928834 workType "article" @default.