Inverse Models and Implications for NDE

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Any NDE process may be considered to involve three systems, each having a unique set of parameters that define its characteristics viz. (a) The Input to the material, (b) The material itself, and (c) The output response measured by the NDE system. Traditionally, the input and the material parameters are assumed known and numerous Forward Models have been developed that predict or estimate the output response function. Over the years, forward models are very well established and serve the key purpose, for improved interpretation of the, as well as to optimize the input parameters to obtain the desired, output response. The other two scenarios i.e. if the output response function in the form of measured data is available, to obtain one of system parameters, i.e. either the input function or the material properties, while the other one is assumed to be known are classified as Inverse Problems. Due to the availability of computational resources, the inverse problem solutions are becoming increasingly feasible. Typical applications include measurement of material properties such as modulus, viscosity, temperature, hardness and stress profiles, etc. This paper will discuss the different techniques and the kinds of problems that have been successfully addressed in the area of NDE and their implications on the expanding horizons in NDE.

Info:

Periodical:

Key Engineering Materials (Volumes 321-323)

Edited by:

Seung-Seok Lee, Joon Hyun Lee, Ik Keun Park, Sung-Jin Song, Man Yong Choi

Pages:

6-11

DOI:

10.4028/www.scientific.net/KEM.321-323.6

Citation:

K. Balasubramaniam "Inverse Models and Implications for NDE", Key Engineering Materials, Vols. 321-323, pp. 6-11, 2006

Online since:

October 2006

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Price:

$35.00

[1] Roy D N G and Couchman L S, 2002, Inverse Problems and Inverse Scattering of Plane Waves, Academic Press, London.

[2] G.R. Liu, Inverse Methods in Nondestructive Evaluation, CRC Press, (2003).

[3] Tenek L H and Henneke E G H, 1993, Flaw dynamics and vibro-thermographic thermoelastic NDE of advanced composite materials, Thermosense, G S Baird eds, SPIE, Proceedings, 1467 pp.252-263.

DOI: 10.1117/12.46440

[4] Han X, Farvo L D, Ouyang Z and Thomas R L, 2000, Recent Developments in Thermosonic Crack Detection, Annual Rev. Progress in Quantitative Nondestructive Evaluation, D O Thompson and D E Chimenti, America Institute of Physics, 21, pp.552-557.

[5] Naveen V Nair, Krishnan Balasubramaniam and Sarit K Das, A 2-D Inverse Heat Conduction Formulation For Determination Of Heat Source Characteristics From Thermal Images, Review of Quantitative Nondestructive Evaluation, American Institute of Physics, Vol. 23, ed. by D. O. Thompson and D. E. Chimenti, pp.453-60, (2004).

DOI: 10.1063/1.1711657

[6] Kim S K and W I Lee 2002, Solution of Inverse Heat Conduction Equation using Maximum Entropy Method, Intl. J. Heat and Mass Transfer, 45, pp.381-391.

DOI: 10.1016/s0017-9310(01)00155-7

[7] Muniz W B, Ramos F M and Decompose Velho H F, 2000, Entropy and Tikhonov Based Regularization Techniques Applied to the Backwards Heat Equation, Computers and Mathematics with Applications, 40, pp.1071-1084.

DOI: 10.1016/s0898-1221(00)85017-8

[8] Roy D N G and Couchman L S, 2002, Inverse Problems and Inverse Scattering of Plane Waves, Academic Press, London.

[9] Balasubramaniam, K., and N. S. Rao, Inversion of Composite Material Elastic Constants from Ultrasonic Bulk Wave Phase Velocity Data using Genetic Algorithms, Composites Part B: Engineering, Vol. 29(B), pp.171-180. (1998).

DOI: 10.1016/s1359-8368(97)00007-3

[10] Wheeler, E.S., R. King, and K. Balasubramaniam, An Artificial Neural Network as a Tool for the Inversion of Ultrasonic Dispersion Data for Material Characterization, Review of Progress in Quantitative Nondestructive Evaluation, Vol. 18B, pp.1265-1272, (1999).

DOI: 10.1007/978-1-4615-4791-4_162

[11] S.S.S. Reddy, Krishnan Balasubramaniam, C.V. Krishnamurthy, and M. Shankar, Ultrasonic goniometry immersion techniques for the measurement of elastic moduli,., Composite Structures, 67, 3-17, (2005).

DOI: 10.1016/j.compstruct.2004.01.008

[12] Balasubramaniam, K., Inversion of ply lay-up sequence for multi-layered fiber reinforced composite plates using Genetic Algorithm, Nondestr. Test. Eval., Vol. 15, pp.311-331. (1999).

DOI: 10.1080/10589759908952877

[13] Prabhu Rajagopala, Krishnan Balasubramaniam, Shankar Maddu, C.V. Krishnamurthy, A new approach to inversion of surface wave dispersion relation for determination of depth distribution of non-uniform stresses in elastic materials, International Journal of Solids and Structures, 42, 789-803, (2004).

DOI: 10.1016/j.ijsolstr.2004.06.066

[14] Prabhu Rajagopalan, Krishnan Balasubramaniam, Krishnamurthy C V, and M. Shankar, A Novel Inversion Algorithm for the Determination of Stress Gradients using Ultrasonic Raleigh Wave Dispersion, Review of Quantitative Nondestructive Evaluation, American Institute of Physics, Vol. 23, ed. by D. O. Thompson and D. E. Chimenti, pp.1200-07, (2004).

[15] Veeraraghavan Sundararaghavan, Krishnan Balasubramaniam, and Ramesh Babu A multifrequency eddy current inversion method for characterizing water jet peened aluminum alloys, Review of Quantitative Nondestructive Evaluation, American Institute of Physics, Vol. 23, ed. by D. O. Thompson and D. E. Chimenti, pp.651-58, (2004).

DOI: 10.1016/j.ndteint.2005.01.009

[16] Sundararaghavan, V., Balasubramaniam, K., Babu, N.R., Rajesh, N., A multi-frequency eddy current inversion method for characterizing conductivity gradients on water jet peened components, NDT and E International, 38 (7), Pages 541-547 (2005).

DOI: 10.1016/j.ndteint.2005.01.009

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