| About this Abstract |
| Meeting |
2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
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| Symposium
|
2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
|
| Presentation Title |
Predicting Microstructure Properties Using Transfer Learning |
| Author(s) |
Farzana Tasnim, Joshua Grose, Nathan F Sheu, Remi Dingreville, Michael Cullinan |
| On-Site Speaker (Planned) |
Farzana Tasnim |
| Abstract Scope |
This study explores the application of transfer learning using pre-trained convolutional neural networks (CNNs) to investigate the relationship between process parameters and microstructure evolution in scientific applications, aiming to predict their correlation with the resistivity ratio between the laser-sintered samples and fully-sintered samples. This method is demonstrated using a dataset comprising microstructures fabricated via micro-Selective Laser Sintering (µSLS). The proposed approach utilizes pre-trained CNNs to extract informative features from Scanning Electron Microscope (SEM) images based on their associated process parameters. These extracted features are utilized to train a fully connected neural network to predict the resistivity ratio. The model achieves high accuracy in predicting the resistivity ratio directly from SEM images, even when dealing with noisy and varied datasets. This approach offers computational efficiency via transfer learning, robust noise handling, and the ability to generalize to unseen data. It has potential for scientific fields needing microstructure analysis and process-property understanding. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |