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Full-Text Articles in Mechanical Engineering
Processing Time, Temperature, And Initial Chemical Composition Prediction From Materials Microstructure By Deep Network For Multiple Inputs And Fused Data, Amir Abbas Kazemzadeh Farizhandi, Mahmood Mamivand
Processing Time, Temperature, And Initial Chemical Composition Prediction From Materials Microstructure By Deep Network For Multiple Inputs And Fused Data, Amir Abbas Kazemzadeh Farizhandi, Mahmood Mamivand
Mechanical and Biomedical Engineering Faculty Publications and Presentations
Prediction of the chemical composition and processing history from microstructure morphology can help in material inverse design. In this work, we propose a fused-data deep learning framework that can predict the processing history of a microstructure. We used the Fe-Cr-Co alloys as a model material. The developed framework is able to predict the heat treatment time, temperature, and initial chemical compositions by reading the morphology of Fe distribution and its concentration. The results show that the trained deep neural network has the highest accuracy for chemistry and then time and temperature. We identified two scenarios for inaccurate predictions; 1) There …
Deep Learning Approach For Chemistry And Processing History Prediction From Materials Microstructure, Amir Abbas Kazemzadeh Farizhandi, Omar Betancourt, Mahmood Mamivand
Deep Learning Approach For Chemistry And Processing History Prediction From Materials Microstructure, Amir Abbas Kazemzadeh Farizhandi, Omar Betancourt, Mahmood Mamivand
Mechanical and Biomedical Engineering Faculty Publications and Presentations
Finding the chemical composition and processing history from a microstructure morphology for heterogeneous materials is desired in many applications. While the simulation methods based on physical concepts such as the phase-field method can predict the spatio-temporal evolution of the materials’ microstructure, they are not efficient techniques for predicting processing and chemistry if a specific morphology is desired. In this study, we propose a framework based on a deep learning approach that enables us to predict the chemistry and processing history just by reading the morphological distribution of one element. As a case study, we used a dataset from spinodal decomposition …