About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Presentation Title |
Automated Microstructure Property Multi-Classification of Ni-Based Superalloys Using Deep Learning |
Author(s) |
Irina Roslyakova, Uchechukwu Nwachukwu , Abdulmonem Obaied, Oliver Horst, David Bürger, Muhammad Adil Ali, Ingo Steinbach |
On-Site Speaker (Planned) |
Irina Roslyakova |
Abstract Scope |
This study presents a solution for automated multi-classification of SEM/TEM images of Ni-based superalloys with respect to creep strain values, heat treatment conditions and chemical compositions using deep learning methods by training convolutional neural network (CNN) in two steps. First, phase-field simulations that displayed similar results to experiments were utilized to build a model with pre-trained CNN architectures (e.g. AlexNet, ResNet34). Then, the optimized hyper-parameters were refined by re-training the CNN with experimental SEM-images of Ni-based superalloys. This fine-tuning process was applied to compensate for the lack of “big data” of available experimental images while training the model. The proposed model was tested on micrographs from other existing publications and showed a promising performance in identifying and predicting strain levels, heat treatment conditions and chemical compositions of SEM/TEM micrographs. Moreover, the refined model is proved to be independent of image scale size. |
Proceedings Inclusion? |
Undecided |