Research on intelligent rating method for metallographic structure based on deep learning

Zhenying Xu; Junlan Gu; Rong Zou

doi:10.1117/12.2548792

13 November 2019 Research on intelligent rating method for metallographic structure based on deep learning

Zhenying Xu, Junlan Gu, Rong Zou

Proceedings Volume 11343, Ninth International Symposium on Precision Mechanical Measurements; 113431R (2019) https://doi.org/10.1117/12.2548792
Event: International Symposium on Precision Mechanical Measurements 2019, 2019, Chongqing, China

Abstract

With the development of modern society, people have higher requirements for the properties of metal materials. However, according to the traditional performance testing method, the prepared samples will be placed under the high-power metallographic microscope for artificial observation and analysis. It has low efficiency and is greatly affected by subjective human factors. In order to finish the task of material recognition and classification of metallographic images, this paper established database and used the deep learning method to research the process and method of convolution neural network, hierarchical learning, transfer learning and so on. The two classification algorithms based on convolution neural network and hierarchical transfer learning have achieved good results for material recognition and grading of metallographic images, respectively and the highest accuracy rate of classification is 98.89%, which provide a good way of thinking and foundation for subsequent metallographic image analysis and detection.

Citation Download Citation

Zhenying Xu, Junlan Gu, and Rong Zou "Research on intelligent rating method for metallographic structure based on deep learning", Proc. SPIE 11343, Ninth International Symposium on Precision Mechanical Measurements, 113431R (13 November 2019); https://doi.org/10.1117/12.2548792

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