Automatic Microstructural Characterization and Classification Using Higher-Order Spectra on Ultrasound Signals

详细信息    查看全文

• 作者：Masoud Vejdannik ; Ali Sadr
• 关键词：Bispectrum ; Classification and regression tree ; k ; Nearest neighbor ; Linear discriminant analysis ; Microstructural characterization ; Non ; destructive inspection ; Random forest ; Ultrasound signals
• 刊名：Journal of Nondestructive Evaluation
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：35
• 期：1
• 全文大小：3,371 KB
• 参考文献：1.Boser, O.: The behavior of inconel 625 in a silver environment. Mater. Sci. Eng. 41(1), 59–64 (1979)CrossRef
  2.Kohl, H., Peng, K.: Thermal stability of the superalloys Inconel 625 and Nimonic 86. J. Nucl. Mater. 101(3), 243–250 (1981)CrossRef
  3.Thomas, C., Tait, P.: The performance of Alloy 625 in long-term intermediate temperature applications. Int. J. Press. Vessel. Pip. 59(1–3), 41–49 (1994)CrossRef
  4.Cieslak, M.: The welding and solidification metallurgy of alloy 625. Weld. J. 70(2), 49–56 (1991)
  5.Cieslak, M., Headley, T., Romig, A.: The welding metallurgy of HASTELLOY alloys C-4, C-22, and C-276. Metall. Trans. A 17(11), 2035–2047 (1986)CrossRef
  6.Yang, J., Zheng, Q., Sun, X., Guan, H., Hu, Z.: Formation of \(\mu \) phase during thermal exposure and its effect on the properties of K465 superalloy. Scr. Mater. 55(4), 331–334 (2006)CrossRef
  7.Dupont, J., Banovic, S., Marder, A.: Microstructural evolution and weldability of dissimilar welds between a super austenitic stainless steel and nickel-based alloys. Weld. J. 82(6), 125–156 (2003)
  8.de Albuquerque, V., de Macedo Silva, E., Pereira Leite, J., de Moura, E., de Araújo Freitas, V., Tavares, J.: Spinodal decomposition mechanism study on the duplex stainless steel UNS S31803 using ultrasonic speed measurements. Mater. Des. 31(4), 2147–2150 (2010)CrossRef
  9.de Albuquerque, V., Melo, T., de Oliveira, D., Gomes, R., Tavares, J.: Evaluation of grain refiners influence on the mechanical properties in a CuAlBe shape memory alloy by ultrasonic and mechanical tensile testing. Mater. Des. 31(7), 3275–3281 (2010)CrossRef
  10.de Macedo Silva, E., de Albuquerque, V., Leite, J., Varela, A., Moura, E., Tavares, J.: Phase transformations evaluation on a UNS S31803 duplex stainless steel based on nondestructive testing. Mater. Sci. Eng. A 516(1—-2), 126–130 (2009)CrossRef
  11.de Araújo Freitas, V., Normando, P., de Albuquerque, V., de Macedo Silva, E., Silva, A., Tavares, J.: Nondestructive characterization and evaluation of embrittlement kinetics and elastic constants of duplex stainless steel SAF 2205 for different aging times at 425\(^{\circ }\) C and 475\(^{\circ }\) C. J. Nondestruct. Eval. 30(3), 130–136 (2011)CrossRef
  12.Normando, P., Moura, E., Souza, J., Tavares, S., Padovese, L.: Ultrasound, eddy current and magnetic Barkhausen noise as tools for sigma phase detection on a UNS S31803 duplex stainless steel. Mater. Sci. Eng. A 527(12), 2886–2891 (2010)CrossRef
  13.Vieira, A., de Moura, E., Gonçalves, L., Rebello, J.: Characterization of welding defects by fractal analysis of ultrasonic signals. Chaos Solitons Fractals 38(3), 748–754 (2008)CrossRef
  14.de Moura, E., Normando, P., Gonçalves, L., Kruger, S.: Characterization of cast iron microstructure through fluctuation and fractal analyses of ultrasonic backscattered signals combined with classification techniques. J. Nondestruct. Eval. 31(1), 90–98 (2011)CrossRef
  15.Vieira, A., de Moura, E., Gonçalves, L.: Fluctuation analyses for pattern classification in nondestructive materials inspection. EURASIP J. Adv. Signal Process. 2010(1), 262869 (2010)CrossRef
  16.Freitas, V., Albuquerque, V., Silva, E., Silva, A., Tavares, J.: Nondestructive characterization of microstructures and determination of elastic properties in plain carbon steel using ultrasonic measurements. Mater. Sci. Eng. A 527(16–17), 4431–4437 (2010)CrossRef
  17.de Albuquerque, V., Filho, P., Cavalcante, T., Tavares, J.: New computational solution to quantify synthetic material porosity from optical microscopic images. J. Microsc. 240(1), 50–59 (2010)MathSciNet CrossRef
  18.Albuquerque, V., Tavares, J., Cortez, P.: Quantification of the microstructures of hypoeutectic white cast iron using mathematical morphology and an artificial neural network. IJMMP 5(1), 52 (2010)CrossRef
  19.de Albuquerque, V., de Alexandria, A., Cortez, P., Tavares, J.: Evaluation of multilayer perceptron and self-organizing map neural network topologies applied on microstructure segmentation from metallographic images. NDT & E Int. 42(7), 644–651 (2009)CrossRef
  20.de Albuquerque, V., Cortez, P., de Alexandria, A., Tavares, J.: A new solution for automatic microstructures analysis from images based on a backpropagation artificial neural network. Nondestruct. Test. Eval. 23(4), 273–283 (2008)CrossRef
  21.Papa, J., Nakamura, R., de Albuquerque, V., Falcão, A., Tavares, J.: Computer techniques towards the automatic characterization of graphite particles in metallographic images of industrial materials. Expert Syst. Appl. 40(2), 590–597 (2013)CrossRef
  22.Nunes, T., de Albuquerque, V., Papa, J., Silva, C., Normando, P., Moura, E., Tavares, J.: Automatic microstructural characterization and classification using artificial intelligence techniques on ultrasound signals. Expert Syst. Appl. 40(8), 3096–3105 (2013)CrossRef
  23.de Albuquerque, V., Silva, C., Normando, P., Moura, E., Tavares, J.: Thermal aging effects on the microstructure of Nb-bearing nickel based superalloy weld overlays using ultrasound techniques. Mater. Des. 36, 337–347 (2012)
  24.Shankar, V., Bhanu, K., Sankara, R., Mannan, S.: Microstructure and mechanical properties of Inconel 625 superalloy. J. Nucl. Mater. 288(2–3), 222–232 (2001)CrossRef
  25.Schölkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (2002)
  26.Nikias, C., Mendel, J.: Signal processing with higher-order spectra. IEEE Signal Process. Mag. 10(3), 10–37 (1993)CrossRef
  27.Mendel, J.: Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications. Proc. IEEE 79(3), 278–305 (1991)CrossRef
  28.Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley, New York (2001)
  29.Bishop, C.: Neural Networks for Pattern Recognition. Clarendon Press, Oxford (1995)
  30.Strobl, C., Malley, J., Tutz, G.: An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychol. Methods 14(4), 323–348 (2009)
  31.Fukunaga, K.: Introduction to Statistical Pattern Recognition. Academic Press, Boston (1990)
  32.Bishop, C.: Pattern Recognition and Machine Learning. Springer, New York (2006)
  33.Cox, D.: Principles of Statistical Inference. Cambridge University Press, Cambridge (2006)CrossRef
  34.Freedman, D.: Statistical Models. Cambridge University Press, Cambridge (2005)
  35.Chen, J., Shi, Y., Shi, S.: Noise analysis of digital ultrasonic nondestructive evaluation system. Int. J. Press. Vessel. Pip. 76(9), 619–630 (1999)CrossRef
  36.Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)MathSciNet CrossRef
  37.de Albuquerque, V., Barbosa, C., Silva, C., Moura, E., Filho, P., Papa, J., Tavares, J.: Ultrasonic sensor signals and optimum path forest classifier for the microstructural characterization of thermally-aged inconel 625 alloy. Sensors 15(6), 12474–12497 (2015)CrossRef
  38.Vejdannik, M., Sadr, A.: Application of linear discriminant analysis to ultrasound signals for automatic microstructural characterization and classification. J. Signal Process. Syst. (2015). doi:10.​1007/​s11265-015-1029-x
  
• 作者单位：Masoud Vejdannik (1)
  Ali Sadr (1)
  
  1. School of Electrical Engineering, Iran University of Science & Technology (IUST), Narmak, 16844, Tehran, Iran
  
• 刊物类别：Engineering
• 刊物主题：Structural Mechanics
  Characterization and Evaluation Materials
  Vibration, Dynamical Systems and Control
  Mechanics
  
• 出版者：Springer Netherlands
• ISSN：1573-4862

文摘

During the gas tungsten arc welding of nickel based superalloys, the secondary phases such as Laves and carbides are formed in final stage of solidification. But, other phases such as \(\gamma {''}\) and \(\delta \) phases can precipitate in the microstructure, during aging at high temperatures. Nevertheless, choosing the appropriate conditions of welding can minimize the formation of the Nb-rich Laves phases and thus reduce the susceptibility to solidification cracking. This study aims at the automatic microstructurally characterizing the kinetics of phase transformations on an Nb-base alloy, thermally aged at 650 and 950 \(^{\circ }\)C for 10, 100 and 200 h, through backscattered ultrasound signals at frequency of 4 MHz. For this, an automated processing system was designed using the spectrum representation of higher order statistics. The ultrasound signals are inherently non-linear and thus the conventional linear time and frequency domain methods can not reveal the complexity of these signals clearly. Bispectrum (the spectral representation of third order correlation) is a non-linear method which is highly robust to noise. In the proposed system, the bispectrum coefficients are subjected to linear discriminant analysis (LDA) technique to reduce the statistical redundancy and reveal discriminating features. These dimensionality reduced features are fed to the classification and regression tree, random forest and k-nearest neighbor (k-NN) classifiers to automatic microstructural characterization. Bispectrum coupled with LDA and k-NN yielded the highest average accuracy of 95.0 and 78.0 %, respectively for thermal aging at 650 and 950 \(^{\circ }\)C. Thus, the proposed processing system provides high reliability to be used for microstructure characterization through ultrasound signals. Keywords Bispectrum Classification and regression tree k-Nearest neighbor Linear discriminant analysis Microstructural characterization Non-destructive inspection Random forest Ultrasound signals