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Meeting Materials Science & Technology 2020
Symposium AI for Big Data Problems in Imaging, Modeling and Synthesis
Presentation Title A Hybrid EBSD Indexing Method Powered by Convolutional Neural Network (CNN) and Dictionary Indexing (DI)
Author(s) Zihao Ding, Marc De Graef
On-Site Speaker (Planned) Zihao Ding
Abstract Scope Based on Dictionary Indexing (DI) and convolutional neural network (CNN) methods our group developed, we propose a novel hybrid EBSD indexing technique, combining advantages of both approaches. Different from an end-to-end regression CNN, the neural net here provides an efficient classification of orientation interval given an EBSD pattern, while DI precisely determines the final orientation. The classification part is trained by simulated EBSD patterns from the EMsoft-based forward model and reduces the workload of DI; thus, the indexing rate of the whole system is greatly improved. The noise resistance and indexing accuracy of DI are preserved in the hybrid method. Through tests on experimental data, we show that machine learning methods can be applied to accelerate conventional EBSD indexing without a loss of robustness.


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