Pattern recognition with machine learning on optical microscopy images of typical metallurgical microstructures

Dmitry S Bulgarevich,Susumu Tsukamoto,Makoto Watanabe,Masahiko Demura,Tadashi Kasuya

doi:10.1038/s41598-018-20438-6

Dmitry S Bulgarevich, Susumu Tsukamoto + Show 3 more

Open Access

https://doi.org/10.1038/s41598-018-20438-6

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Abstract

For advanced materials characterization, a novel and extremely effective approach of pattern recognition in optical microscopic images of steels is demonstrated. It is based on fast Random Forest statistical algorithm of machine learning for reliable and automated segmentation of typical steel microstructures. Their percentage and location areas excellently agreed between machine learning and manual examination results. The accurate microstructure pattern recognition/segmentation technique in combination with other suitable mathematical methods of image processing and analysis can help to handle the large volumes of image data in a short time for quality control and for the quest of new steels with desirable properties.

Highlights

Depending on cooling rate of steels, the ferrite (F), pearlite (P), bainite (B), and martensite (M) microstructures could be formed due to the displacive and reconstructive transformations of austenite (A) crystal structure, which are accompanied with cementite precipitation at different diffusion rates[1]
If the line length is long enough, it is assumed that ratio of the summed length for particular phase to the total line length is equal to the volume fraction of this phase
The original image on the background is overlaid with segmented colour-coded areas for F, B, and P

Summary

Results and Discussion

The accuracy of microstructure percentage estimation become comparable with manual linear analysis by averaging of segmentation results for image stack (see percentage deviations within the stack) or by using of stitched image with large imaging area The main challenge is at Classifier application stage on datasets, which may differ strongly in image quality In this regard, the development/use of appropriate cross-validation protocols by automated or human inspection with sampling analysis may still be needed. The best way is, first, to analyse the image data in terms of j types with some automated unsupervised-learning algorithm or statistical image analysis, and to choose the appropriate Random Forest Classifier for this j combination from corresponding library These are the interesting and important topics for future research and development.

Combination of above tools with thresholding and various filtering techniques

Concluding Remarks

Author Contributions

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