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Automatic Detection of Pearlite Spheroidization Grade of Steel Using Optical Metallography

Published online by Cambridge University Press:  12 January 2016

Naichao Chen ,
Yingchao Chen ,
Jun Ai ,
Jianxin Ren ,
Rui Zhu ,
Xingchi Ma ,
Jun Han  and
Qingqian Ma
Naichao Chen*
Affiliation:
School Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 200090, China Shanghai Key Laboratory of Materials Protection and Advanced Materials in Electric Power, Shanghai 200090, China
Yingchao Chen
Affiliation:
School Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Jun Ai
Affiliation:
School Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Jianxin Ren
Affiliation:
School Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Rui Zhu
Affiliation:
School Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Xingchi Ma
Affiliation:
School Energy and Mechanical Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Jun Han
Affiliation:
Shanghai Special Equipment Inspection and Research Institute, Shanghai 200333, China
Qingqian Ma
Affiliation:
Shanghai Special Equipment Inspection and Research Institute, Shanghai 200333, China
*
*Corresponding author.yeiji_chen@126.com
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Abstract

To eliminate the effect of subjective factors during manually determining the pearlite spheroidization grade of steel by analysis of optical metallography images, a novel method combining image mining and artificial neural networks (ANN) is proposed. The four co-occurrence matrices of angular second moment, contrast, correlation, and entropy are adopted to objectively characterize the images. ANN is employed to establish a mathematical model between the four co-occurrence matrices and the corresponding spheroidization grade. Three materials used in coal-fired power plants (ASTM A315-B steel, ASTM A335-P12 steel, and ASTM A355-P11 steel) were selected as the samples to test the validity of our proposed method. The results indicate that the accuracies of the calculated spheroidization grades reach 99.05, 95.46, and 93.63%, respectively. Hence, our newly proposed method is adequate for automatically detecting the pearlite spheroidization grade of steel using optical metallography.

Keywords

pearlite spheroidizationgradeoptical metallographyimage miningartificial neural networks
Type
Techniques, Software, and Equipment
Information
Microscopy and Microanalysis , Volume 22 , Issue 1 , February 2016 , pp. 208 - 218
Copyright
© Microscopy Society of America 2016 

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