Share Email Print

Proceedings Paper

Research on intelligent rating method for metallographic structure based on deep learning
Author(s): Zhenying Xu; Junlan Gu; Rong Zou
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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.

Paper Details

Date Published: 13 November 2019
PDF: 7 pages
Proc. SPIE 11343, Ninth International Symposium on Precision Mechanical Measurements, 113431R (13 November 2019); doi: 10.1117/12.2548792
Show Author Affiliations
Zhenying Xu, Jiangsu Univ. (China)
Junlan Gu, Jiangsu Univ. (China)
Rong Zou, Jiangsu Univ. (China)

Published in SPIE Proceedings Vol. 11343:
Ninth International Symposium on Precision Mechanical Measurements
Liandong Yu, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?