title: Automatic Detection of Pearlite Spheroidization Grade of Steel Using Optical Metallography.
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.
authors.affiliation: 1School Energy and Mechanical Engineering,Shanghai University of Electric Power,Shanghai 200090,China.
authors.forename: Naichao
authors.initials: N
authors.lastname: Chen
authors.affiliation: 1School Energy and Mechanical Engineering,Shanghai University of Electric Power,Shanghai 200090,China.
authors.forename: Yingchao
authors.initials: Y
authors.lastname: Chen
authors.affiliation: 1School Energy and Mechanical Engineering,Shanghai University of Electric Power,Shanghai 200090,China.
authors.forename: Jun
authors.initials: J
authors.lastname: Ai
authors.affiliation: 1School Energy and Mechanical Engineering,Shanghai University of Electric Power,Shanghai 200090,China.
authors.forename: Jianxin
authors.initials: J
authors.lastname: Ren
authors.affiliation: 1School Energy and Mechanical Engineering,Shanghai University of Electric Power,Shanghai 200090,China.
authors.forename: Rui
authors.initials: R
authors.lastname: Zhu
authors.affiliation: 1School Energy and Mechanical Engineering,Shanghai University of Electric Power,Shanghai 200090,China.
authors.forename: Xingchi
authors.initials: X
authors.lastname: Ma
authors.affiliation: 3Shanghai Special Equipment Inspection and Research Institute,Shanghai 200333,China.
authors.forename: Jun
authors.initials: J
authors.lastname: Han
authors.affiliation: 3Shanghai Special Equipment Inspection and Research Institute,Shanghai 200333,China.
authors.forename: Qingqian
authors.initials: Q
authors.lastname: Ma
mesh_terms: D000537:Aluminum Oxide / Q000032:analysis*; D016571:Neural Networks, Computer; D061848:Optical Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated; D012822:Silicon Dioxide / Q000032:analysis*; D013232:Steel
chemical_list: C003076:Perlite; D013232:Steel; D012822:Silicon Dioxide; D000537:Aluminum Oxide
keywords: artificial neural networks; grade; image mining; optical metallography; pearlite spheroidization