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Advanced Steel Microstructure Classification by Deep Learning Methods

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons197294

Azimi,  Seyed Majid
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44451

Fritz,  Mario
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

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Fulltext (public)

arXiv:1706.06480.pdf
(Preprint), 2MB

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Citation

Azimi, S. M., Britz, D., Engstler, M., Fritz, M., & Mücklich, F. (2017). Advanced Steel Microstructure Classification by Deep Learning Methods. Retrieved from http://arxiv.org/abs/1706.06480.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002D-8B65-6
Abstract
The inner structure of a material is called microstructure. It stores the genesis of a material and determines all its physical and chemical properties. While microstructural characterization is widely spread and well known, the microstructural classification is mostly done manually by human experts, which opens doors for huge uncertainties. Since the microstructure could be a combination of different phases with complex substructures its automatic classification is very challenging and just a little work in this field has been carried out. Prior related works apply mostly designed and engineered features by experts and classify microstructure separately from feature extraction step. Recently Deep Learning methods have shown surprisingly good performance in vision applications by learning the features from data together with the classification step. In this work, we propose a deep learning method for microstructure classification in the examples of certain microstructural constituents of low carbon steel. This novel method employs pixel-wise segmentation via Fully Convolutional Neural Networks (FCNN) accompanied by max-voting scheme. Our system achieves 93.94% classification accuracy, drastically outperforming the state-of-the-art method of 48.89% accuracy, indicating the effectiveness of pixel-wise approaches. Beyond the success presented in this paper, this line of research offers a more robust and first of all objective way for the difficult task of steel quality appreciation.