Skip to main content
Log in

Automatic microstructural characterization and classification using probabilistic neural network on ultrasound signals

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

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 \(\gamma ^{{\prime \prime }}\) and \(\delta \) phases can precipitate in the microstructure, during aging at high temperatures. However, it is possible to minimize the formation of the Nb-rich Laves phases and therefore reduce the possibility of solidification cracking by adopting the appropriate welding conditions. This paper 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. 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. Consequently, an automated processing system is designed using the higher-order statistics techniques, such as 3rd-order cumulant and bispectrum. These techniques are non-linear methods which are highly robust to noise. For this, the coefficients of 3rd-order cumulant and bispectrum of ultrasound signals are subjected to the independent component analysis (ICA) technique to reduce the statistical redundancy and reveal discriminating features. These dimensionality reduced features are fed to the probabilistic neural network (PNN) to automatic microstructural classification. The training process of PNN depends only on the selection of the smoothing parameters of pattern neurons. In this article, we propose the application of the bees algorithm to the automatic adaptation of smoothing parameters. The ICA components of cumulant coefficients coupled with the optimized PNN yielded the highest average accuracy of 97.0 and 83.5 %, 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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  • Albuquerque, V., Tavares, J., & Cortez, P. (2010). Quantification of the microstructures of hypoeutectic white cast iron using mathematical morphology and an artificial neural network. IJMMP, 5(1), 52.

    Article  Google Scholar 

  • Boser, O. (1979). The behavior of inconel 625 in a silver environment. Materials Science and Engineering, 41(1), 59–64.

    Article  Google Scholar 

  • Chen, J., Shi, Y., & Shi, S. (1999). Noise analysis of digital ultrasonic nondestructive evaluation system. International Journal of Pressure Vessels and Piping, 76(9), 619–630.

    Article  Google Scholar 

  • Chtioui, Y. (1998). Conjugate gradient and approximate Newton methods for an optimal probabilistic neural network for food color classification. Optical Engineering, 37(11), 3015.

    Article  Google Scholar 

  • Cieslak, M. (1991). The welding and solidification metallurgy of alloy 625. Welding Journal, 70(2), 49–56.

    Google Scholar 

  • Cieslak, M., Headley, T., & Romig, A. (1986). The welding metallurgy of HASTELLOY alloys C-4, C-22, and C-276. Metallurgical Transactions A, 17(11), 2035–2047.

    Article  Google Scholar 

  • Cox, D. (2006). Principles of statistical inference. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • de Albuquerque, V., de Alexandria, A., Cortez, P., & Tavares, J. (2009). Evaluation of multilayer perceptron and self-organizing map neural network topologies applied on microstructure segmentation from metallographic images. NDT and E International, 42(7), 644–651.

    Article  Google Scholar 

  • de Albuquerque, V., Barbosa, C., Silva, C., Moura, E., Filho, P., Papa, J., et al. (2015). Ultrasonic sensor signals and optimum path forest classifier for the microstructural characterization of thermally-aged Inconel 625 alloy. Sensors, 15(6), 12474–12497.

    Article  Google Scholar 

  • de Albuquerque, V., Cortez, P., de Alexandria, A., & Tavares, J. (2008). A new solution for automatic microstructures analysis from images based on a backpropagation artificial neural network. Nondestructive Testing and Evaluation, 23(4), 273–283.

    Article  Google Scholar 

  • de Albuquerque, V., Filho, P., Cavalcante, T., & Tavares, J. (2010). New computational solution to quantify synthetic material porosity from optical microscopic images. Journal of Microscopy, 240(1), 50–59.

    Article  Google Scholar 

  • de Albuquerque, V., de Macedo Silva, E., Leite, J. P., de Moura, E., de Araújo Freitas, V., & Tavares, J. (2010). Spinodal decomposition mechanism study on the duplex stainless steel UNS S31803 using ultrasonic speed measurements. Materials and Design, 31(4), 2147–2150.

    Article  Google Scholar 

  • de Albuquerque, V., Melo, T., de Oliveira, D., Gomes, R., & Tavares, J. (2010). Evaluation of grain refiners influence on the mechanical properties in a CuAlBe shape memory alloy by ultrasonic and mechanical tensile testing. Materials and Design, 31(7), 3275–3281.

    Article  Google Scholar 

  • de Albuquerque, V., Silva, C., Normando, P., Moura, E., & Tavares, J. (2012). Thermal aging effects on the microstructure of Nb-bearing nickel based superalloy weld overlays using ultrasound techniques. Materials and Design, 36, 337–347.

    Article  Google Scholar 

  • de Araújo Freitas, V., Normando, P., de Albuquerque, V., de Macedo Silva, E., Silva, A., & Tavares, J. (2011). Nondestructive characterization and evaluation of embrittlement kinetics and elastic constants of duplex stainless steel SAF 2205 for different aging times at \(425^{\circ }\text{ C }\) and \(475^{\circ }\text{ C }\). Journal of Nondestructive Evaluation, 30(3), 130–136.

    Article  Google Scholar 

  • de Macedo Silva, E., de Albuquerque, V., Leite, J., & Varela, A. (2009). Phase transformations evaluation on a UNS S31803 duplex stainless steel based on nondestructive testing. Materials Science and Engineering: A, 516(1–2), 126–130.

    Article  Google Scholar 

  • de Moura, E., Normando, P., Gonçalves, L., & Kruger, S. (2011). Characterization of cast iron microstructure through fluctuation and fractal analyses of ultrasonic backscattered signals combined with classification techniques. Journal of Nondestructive Evaluation, 31(1), 90–98.

    Article  Google Scholar 

  • Duda, R., Hart, P., & Stork, D. (2001). Pattern classification. New York: Wiley.

    Google Scholar 

  • Dupont, J., Banovic, S., & Marder, A. (2003). Microstructural evolution and weldability of dissimilar welds between a super austenitic stainless steel and nickel-based alloys. Welding Journal, 82(6), 125–156.

    Google Scholar 

  • Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874.

    Article  Google Scholar 

  • Freedman, D. (2005). Statistical models. Cambridge: Cambridge University Press.

    Google Scholar 

  • Freitas, V., Albuquerque, V., Silva, E., Silva, A., & Tavares, J. (2010). Nondestructive characterization of microstructures and determination of elastic properties in plain carbon steel using ultrasonic measurements. Materials Science and Engineering: A, 527(16–17), 4431–4437.

    Article  Google Scholar 

  • Gorunescu, F., Gorunescu, M., El-Darzi, E., & Gorunescu, S. (2005) An evolutionary computational approach to probabilistic neural network with application to hepatic cancer diagnosis. In IEEE Symposium on Computer-Based Medical Systems (pp. 461–466).

  • Hyvarinen, A., Karhunen, J., & Oja, E. (2001). Independent component analysis. New York: Wiley.

    Book  Google Scholar 

  • Jaynes, E., & Bretthorst, G. (2003). Probability theory. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Kohl, H., & Peng, K. (1981). Thermal stability of the superalloys Inconel 625 and Nimonic 86. Journal of Nuclear Materials, 101(3), 243–250.

    Article  Google Scholar 

  • Liu, X., Ghorpade, A., Tu, Y., & Zhang, W. (2012). A novel approach to probability distribution aggregation. Information Sciences, 188, 269–275.

    Article  Google Scholar 

  • Mao, K., Tan, K., & Ser, W. (2000). Probabilistic neural-network structure determination for pattern classification. IEEE Transactions on Neural Networks, 11(4), 1009–1016.

    Article  Google Scholar 

  • Mendel, J. (1991). Tutorial on higher-order statistics (spectra) in signal processing and system theory: Theoretical results and some applications. Proceedings of the IEEE, 79(3), 278–305.

    Article  Google Scholar 

  • Modi, S., Lin, Y., Cheng, L., Yang, G., Liu, L., & Zhang, W. (2011). A socially inspired framework for human state inference using expert opinion integration. IEEE/ASME Transactions on Mechatronics, 16(5), 874–878.

    Article  Google Scholar 

  • Nikias, C., & Mendel, J. (1993). Signal processing with higher-order spectra. IEEE Signal Processing Magazine, 10(3), 10–37.

    Article  Google Scholar 

  • Normando, P., Moura, E., Souza, J., Tavares, S., & Padovese, L. (2010). Ultrasound, eddy current and magnetic Barkhausen noise as tools for sigma phase detection on a UNS S31803 duplex stainless steel. Materials Science and Engineering: A, 527(12), 2886–2891.

    Article  Google Scholar 

  • Nunes, T., de Albuquerque, V., Papa, J., Silva, C., Normando, P., Moura, E., et al. (2013). Automatic microstructural characterization and classification using artificial intelligence techniques on ultrasound signals. Expert Systems with Applications, 40(8), 3096–3105.

    Article  Google Scholar 

  • Papa, J., Nakamura, R., de Albuquerque, V., Falcão, A., & Tavares, J. (2013). Computer techniques towards the automatic characterization of graphite particles in metallographic images of industrial materials. Expert Systems with Applications, 40(2), 590–597.

    Article  Google Scholar 

  • Pham, D.T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., & Zaidi M. (2006). The bees algorithm, a novel tool for complex optimisation problems. In Proceedings IPROMS Conference (pp. 454–456).

    Chapter  Google Scholar 

  • Pham, D. T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., & Zaidi, M. (2005). The bees algorithm. Technical Note. Manufacturing Engineering Centre, Cardiff University, UK.

  • Schölkopf, B., & Smola, A. (2002). Learning with kernels. Cambridge, MA: MIT Press.

    Google Scholar 

  • Shankar, V., Rao, K. Bhanu Sankara, & Mannan, S. (2001). Microstructure and mechanical properties of Inconel 625 superalloy. Journal of Nuclear Materials, 288(2–3), 222–232.

    Article  Google Scholar 

  • Specht, D. (1991). A general regression neural network. IEEE Transactions on Neural Networks, 2(6), 568–576.

    Article  Google Scholar 

  • Thomas, C., & Tait, P. (1994). The performance of alloy 625 in long-term intermediate temperature applications. International Journal of Pressure Vessels and Piping, 59(1–3), 41–49.

    Article  Google Scholar 

  • Vejdannik, M., & Sadr, A. (2016a). Application of linear discriminant analysis to ultrasound signals for automatic microstructural characterization and classification. Journal of Signal Processing Systems, 83(3), 411–421.

    Article  Google Scholar 

  • Vejdannik, M., & Sadr, A. (2016b). Automatic microstructural characterization and classification using higher-order spectra on ultrasound signals. Journal of Nondestructive Evaluation, 35(1), 1.

  • Vejdannik, M., & Sadr, A. (2016c). Automatic microstructural characterization and classification using dual tree complex wavelet-based features and bees algorithm. Neural Computing and Applications. doi:10.1007/s00521-016-2188-9.

    Article  Google Scholar 

  • Vieira, A., de Moura, E., & Gonçalves, L. (2010). Fluctuation analyses for pattern classification in nondestructive materials inspection. EURASIP Journal on Advances in Signal Processing, 2010(1), 262869.

    Article  Google Scholar 

  • Vieira, A., de Moura, E., Gonçalves, L., & Rebello, J. (2008). Characterization of welding defects by fractal analysis of ultrasonic signals. Chaos, Solitons and Fractals, 38(3), 748–754.

    Article  Google Scholar 

  • Yang, J., Zheng, Q., Sun, X., Guan, H., & Hu, Z. (2006). Formation of \(\mu \) phase during thermal exposure and its effect on the properties of K465 superalloy. Scripta Materialia, 55(4), 331–334.

    Article  Google Scholar 

  • Zhong, M., Coggeshall, D., Ghaneie, E., Pope, T., Rivera, M., Georgiopoulos, M., et al. (2007). Gap-based estimation: Choosing the smoothing parameters for probabilistic and general regression neural networks. Neural Computation, 19(10), 2840–2864.

    Article  Google Scholar 

Download references

Acknowledgments

The first author thanks from Victor Hugo C. de Albuquerque and is also grateful for his help for providing the experimental dataset.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Masoud Vejdannik.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vejdannik, M., Sadr, A. Automatic microstructural characterization and classification using probabilistic neural network on ultrasound signals. J Intell Manuf 29, 1923–1940 (2018). https://doi.org/10.1007/s10845-016-1225-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-016-1225-y

Keywords

Navigation