Journal of Computational & Applied Research in Mechanical Engineering (JCARME)

Document Type : Research Paper

Authors

Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, A.P., India, 522502

https://doi.org/10.22061/jcarme.2020.4871.1596

Abstract

Engineering materials and structures have crack-like defects leading to premature failures. The usage of fracture mechanics to assess the structural integrity requires knowledge on the type, location, shape, size, and orientation of the flaws. Tomographic reconstruction is one of the commonly used nondestructive testing methods for flaw characterization. The cross sectional image of the object being tested is obtained through the application of various reconstruction methods that are categorized as either analytical methods or iterative methods. In this work, an iterative algorithm that works on the principles of genetic algorithms is developed and used for the reconstruction. The results of simulation studies on the tomographic reconstructions using genetic algorithms for the identification of defects in isotropic materials are discussed in the paper. The solution methodology based on the use of genetic algorithms is applied to reconstruct the cross sections of test specimens with different flaw characteristics. Simulated time-of-flight data of ultrasound rays transmitted through the specimen under investigation is used as input to the algorithm. The time-of-flight data is simulated neglecting the bending of ultrasound rays and assuming straight ray paths. Numerical studies performed on several specimens with flaws of known materials but unknown location, size and shape are presented. The number of ultrasonic transmitters and receivers needed for complete scanning of the specimen’s cross section is analyzed and presented. The findings of the parametric analysis and sensitivity analysis in order to choose appropriate range of algorithm parameters for performance quality and robustness of the algorithm are presented. The performance of the present algorithm with noisy projection data is also discussed.

Graphical Abstract

Keywords

dor

20.1001.1.22287922.2021.11.1.18.0

Main Subjects

Nondestructive Testing

References

[1] K.R. Leonard, E.V. Malyarenko, M.K. Hinders, “Ultrasonic Lamb wave tomography”, Inverse Problems, Vol. 18, No. 6, pp. 1795-1808, (2002).

[2] P.P. Delsanto, A. Romano, M. Scalerandi, F. Moldoveanu, “Application of genetic algorithms to ultrasonic tomography”, J. Acoust. Soc. Am., Vol. 104, pp. 1374–1381, (1998).

[3] A. C. Kak and Malcolm Slaney, Principles of Computerized Tomographic Imaging, Society of Industrial and Applied Mathematics, 2001.

[4] K. Bryant, R. Vaga, “Computer tomography from micro-electronics to assembled products”, Advances in Science, Technology and Engineering Systems Journal, Vol. 2, No. 3, pp. 932-936, (2017).

[5] L. De Chiffre, S. Carmignato, J. P. Kruth, R. Schmitt, A. Weckenmann, “Industrial applications of computed tomography”, CIRP Annals, Vol. 63, No. 2, pp. 655-677, (2014).

[6] X. Teng, R.E. Green Jr., “Ultrasonic tomographic imaging of defects in industrial materials”, Review of Progress in Quantitative Nondestructive Evaluation, Eds. D.O. Thompson, D.E. Chimenti, Plenum Press, New York, pp. 889-896, (1993).

[7] P. Munshi, “A review of computerized tomography with application to two-phase flows”, Sadhana, Vol. 15, No. 1, pp. 43-55, (1990).

[8] T. Ponikiewski, J. Katzer, “X-ray computed tomography of fibre reinforced self compacting concrete as a tool of assessing its flexural behavior”, Materials and Structures, Vol. 49, pp. 2131–2140, (2016).

[9] S.J. Svärd, S. Holcombe, S. Grape, “Applicability of a set of tomographic reconstruction algorithms for quantitative SPECT on irradiated nuclear fuel assemblies”, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 783, pp. 128-141, (2015).

[10] J.F. Greenleaf, “Computerized tomography with ultrasound”, Proc. IEEE, Vol. 71, No. 3, pp. 330–337, (1983).

[11] B. Jiang, W. Zhao, W. Wang, “Improved ultrasonic computerized tomography method for STS (steel tube slab) structure based on compressive sampling algorithm”, Appl. Sci. Vol. 7, No. 5, pp. 432, (2017).

[12] J.L. Rose, “A baseline and vision of ultrasonic guided wave inspection potential”, J. Press. Vessel Tech., Vol. 124, pp. 273–282, (2002).

[13] C. Byrne, “Iterative algorithms in tomography”, (2005).

[14] R.M. Lewitt, “Reconstruction algorithms: transform methods”, Proc. IEEE, Vol. 71, No. 3, pp. 390–408, (1983).

[15] Y. Censor, “Finite series-expansion reconstruction methods”, Proc. IEEE, Vol. 71, No. 3, pp. 409–419, (1983).

[16] E.F. Oliveira, S.B. Melo, C.C. Dantas, D.A. A. Vasconcelos, L.F. Cadiz, “Comparison among tomographic reconstruction algorithms with a limited data”, Proc. International Nuclear Atlantic Conference - INAC 2011, ISBN: 978-85-99141-04-5 (2011).

[17] N.N. Kishore, P. Munshi, M.A. Ranamale, V.V. Ramakrishna, W. Arnold , “Tomographic reconstruction of defects in composite plates using genetic algorithms with cluster analysis”, Research in Nondestructive Evaluation, Vol. 22, No. 1, pp. 31-60, (2011).

[18] K. Deb, Optimization for Engineering Design: Algorithms and Examples, Prentice Hall of India, New Delhi, (2004).

[19] R.S. Bichkar, A.K. Ray, “Tomographic reconstruction of circular and elliptical objects using genetic algorithm”, IEEE Signal Processing Letters, Vol. 5, pp. 248–251, (1998).

[20] K. J. Batenburg, “An evolutionary algorithm for discrete tomography”, Discrete Applied Mathematics, Vol. 151, pp. 36 – 54, (2005).

[21] C. Valenti, “A genetic algorithm for discrete tomography reconstruction”, Genetic Programming and Evolvable Machines, Vol. 9, No. 1, pp. 85–96, (2008).

[22] X. Yang, J.R. Ommen, R.F. Mudde, “Comparison of genetic algorithm and algebraic reconstruction for X-ray tomography in bubbling fluidized beds”, Powder Technology, Vol. 253, pp. 626-637, (2014).

[23] T. Weise, “Global Optimization Algorithms-Theory and Application”, Version: 2009-06-26.

Name *

Email Address *

Affiliation *

Comments *

Security Code *

CAPTCHA Image