Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-28T05:22:42.009Z Has data issue: false hasContentIssue false
  • English
  • Français

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
Get access

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bhadeshia, H. (1999). Neural networks in materials science. ISIJ Int 39, 966–979.CrossRefGoogle Scholar
Brooks, C.R. (2000). Microstructural observations of spheroidization from a lamellar structure in iron meteorites. Mater Charact 45, 71–80.CrossRefGoogle Scholar
Buessler, J.L., Smagghe, P. & Urban, J.P. (2014). Image receptive fields for artificial neural networks. Neurocomputing 144, 258–270.CrossRefGoogle Scholar
Das, A., Sivaprasad, S., Tarafder, M., Das, S.K. & Tarafder, S. (2013). Estimation of damage in high strength steels. Appl Soft Comput 13, 1033–1041.CrossRefGoogle Scholar
DL/T674-2001 C.E.P.I.S. (2001). The Gradational Standard of Spherular Pearlite for Carbon Steel No. 20 used in Fossil Power Plant. Beijing: China Electric Power Press.Google Scholar
DL/T773-2001 C.E.P.I.S. (2001). Spheroidization Evaluation Standard of 12Cr1MoV Steel used in Power Plan. Beijing: China Electric Power Press.Google Scholar
DL/T787-2001 C.E.P.I.S. (2001). The Gradational Standard of Spherular Pearlite for 15CrMo Steel used in Fossil Power Plant. Beijing: China Electric Power Press.Google Scholar
Fayyad, U., Piatetsky-Shapiro, G. & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI Mag 17, 37–54.Google Scholar
Fu, Y., Yu, H. & Tao, P. (2014). On-line spheroidization process of medium-carbon low-alloyed cold heading steel. Int J Min Met Mater 21, 26–35.CrossRefGoogle Scholar
Gorkunov, E.S., Savrai, R.A., Makarov, A.V., Zadvorkin, S.M. & Malygina, I.Y. (2011). Magnetic inspection of fatigue degradation of a high-carbon pearlitic steel. Russ J Nondestruct Test 47, 803–809.CrossRefGoogle Scholar
Haralick, R.M., Shanmugam, K. & Dinstein, I.H. (1973). Textural features for image classification. IEEE Trans Syst Man Cybern Syst SMC–3, 610–621.CrossRefGoogle Scholar
Haralick, R.M. & Shapiro, L.G. (1992). Computer and Robot (vol. 1). Boston: Addison-Wesley.Google Scholar
Hornik, K., Stinchcombe, M. & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Netw 2, 359–366.CrossRefGoogle Scholar
Kim, E.S. & Kim, I.S. (2000). Effect of spheroidization on the near-threshold fatigue crack growth in ferrite-pearlite steel. J Mater Sci Lett 19, 367–369.CrossRefGoogle Scholar
Liu, C.J., Dung, L.Y. & Jiang, X.D. (2012). Characterizing the spheroidization grade and strength of 15CrMo steel through determining fractal dimension. Chin J Mech Eng 25, 826–831.CrossRefGoogle Scholar
Mandal, S., Sivaprasad, P.V., Venugopal, S. & Murthy, K.P.N. (2009). Artificial neural network modeling to evaluate and predict the deformation behavior of stainless steel type AISI 304L during hot torsion. Appl Soft Comput 9, 237–244.CrossRefGoogle Scholar
Moakhar, R.S., Mehdipour, M., Ghorbani, M., Mohebali, M. & Koohbor, B. (2013). Investigations of the failure in boilers economizer tubes used in power plants. J Mater Eng Perform 22, 2691–2697.CrossRefGoogle Scholar
Nutal, N., Gommes, C.J., Blacher, S., Pouteau, P., Pirard, J.P., Boschini, F., Traina, K. & Cloots, R. (2010). Image analysis of pearlite spheroidization based on the morphological characterization of cementite particles. Image Anal Stereol 29, 91–98.CrossRefGoogle Scholar
Ohashi, T., Roslan, L., Takahashi, K., Shimokawa, T., Tanaka, M. & Higashida, K. (2013). A multiscale approach for the deformation mechanism in pearlite microstructure: Numerical evaluation of elasto-plastic deformation in fine lamellar structures. Mater Sci Eng A 588, 214–220.CrossRefGoogle Scholar
Poonnoy, P., Yodkeaw, P., Sriwai, A., Umongkol, P. & Intamoon, S. (2014). Classification of boiled shrimp’s shape using image analysis and artificial neural network model. J Food Process Eng 37, 257–263.CrossRefGoogle Scholar
Rumelhart, D.E., Hinton, G.E. & Williams, R.J. (1986). Learning representations by back-propagating errors. Nature 323, 533–536.CrossRefGoogle Scholar
Samtas, G. (2014). Measurement and evaluation of surface roughness based on optic system using image processing and artificial neural network. Int J Adv Manuf Tech 73, 353–364.CrossRefGoogle Scholar
Schütze, M., Schorr, M., Renusch, D.P., Donchev, A. & Vossen, J.P.T. (2004). The role of alloy composition, environment and stresses for the oxidation resistance of modern 9% Cr steels for fossil power stations. Mater Res 7, 111–123.CrossRefGoogle Scholar
Skowronek, T., Ratuszek, W., Chrusciel, K., Czarski, A., Satora, K. & Wiencek, K. (2004). Spheroidization of cementite in pearlite. Arch Met Mater 49, 961–971.Google Scholar
Srinivasulu, S. & Jain, A. (2006). A comparative analysis of training methods for artificial neural network rainfall–runoff models. Appl Soft Comput 6, 295–306.CrossRefGoogle Scholar
Tamura, H., Mori, S. & Yamawaki, T. (1978). Textural features corresponding to visual perception. IEEE Trans Syst Man Cybern Syst SMC–8, 460–473.CrossRefGoogle Scholar
Toribio, J., Gonzalez, B. & Matos, J.C. (2011). Influence of the microstructure of eutectoid steel on the cyclic crack propagation: Pearlite and spheroidite. Int J Fract 171, 209–215.CrossRefGoogle Scholar
Wang, Y.T., Adachi, Y., Nakajima, K. & Sugimoto, Y. (2010). Quantitative three-dimensional characterization of pearlite spheroidization. Acta Mater 58, 4849–4858.CrossRefGoogle Scholar
Wang, Y.T., Adachi, Y., Nakajima, K. & Sugimoto, Y. (2012). Topology and differential geometry-based three-dimensional characterization of pearlite spheroidization. ISIJ Int 52, 697–703.CrossRefGoogle Scholar
Yao, S.C., Dong, M.R., Lu, J.D., Li, J. & Dong, X. (2013). Correlation between grade of pearlite spheroidization and laser induced spectra. Laser Phys 23, 125702.CrossRefGoogle Scholar