Article Article
Multi-Scale Mixed Modality Microstructure Assessment for Titanium (M4AT) Data

The capability of a material depends on multiscale physical properties. In many cases, state-of-the-art material characterization methods for micro-to-mesoscale features require extensive preparation or destructive analysis. These shortcomings limit their use for quality control of component-scale parts, as extensive preparation or destructive analysis are prohibitively expensive or impossible for real-time assessment. One example is the detection and characterization of critical microtexture regions in titanium, where the state-of-the-art sensing method is both damaging and constrained to a laboratory environment. New sensing approaches that achieve the capability of laboratory-based characterization methods without destructive assessment offer promise for manufacturing, inspection, and assembly. A potential solution is to develop novel data fusion algorithms to compliment existing nondestructive evaluation techniques.

DOI: 10.32548/RS.2022.040

References

(1) Cappola, J., Stinville, J. C., Charpagne, M. A., Callahan, P. G., Echlin, M. P., Pollock, T. M., Pilchak, A., & Kasemer, M. (2021). On the localization of plastic strain in microtextured regions of Ti-6AL-4v. Acta Materialia, 204, 116492.

(2) Kocks, U. F., Tomé, C. N., & Wenk, H. R. (1998). Texture and anisotropy: preferred orientations in polycrystals and their effect on materials properties. Cambridge university press.

(3) Silk, M. G. (1981). Relationships between metallurgical texture and ultrasonic propagation. Metal Science, 15(11-12), 559-565.

(4) Shyne, J.C., N. Grayeli and G.S. Kino. (1981). Microstructural charactersation and reliability strategies. Proceedings of the Symposium on NDE, 0,  133-140.

(5) Spies, M.. (1989). Nondestructive Determination of Materials' Texture by Ultrasonic Techniques. Thesis, University of Saarlandus, Saarbrucken, Germany.

(6) Spies, M., & Salama, K. (1989). Texture of metal-matrix composites by ultrasonic velocity measurements. Research in Nondestructive Evaluation, 1(2), 99-109.

(7) Palanichamy, P., & Vasudevan, M. (2003). Ultrasonic testing of annealing behavior and texture and determination of texture coefficients in stainless steel. Materials evaluation, 61(9), 1020-1025.

(8) Liu, G., Laabs, F., Rehbein, D., Buck, O., & Thompson, R. B. (1998). Ultrasonic Monitoring of Recrystallization Textures in Aluminum. In Review of Progress in Quantitative Nondestructive Evaluation (pp. 1899-1906). Springer, Boston, MA.

(9) Frederick, S. F., & SF, F. (1975). Texture assurance in titanium using surface wave velocity. Materials evaluation, 33(9), 213-216.

(10) Schwartz, A. J., Kumar, M., Adams, B. L., & Field, D. P. (Eds.). (2009). Electron backscatter diffraction in materials science, (Vol. 2, pp. 35-52). New York: Springer.

(11) Wang, C., Fan, M., Cao, B., Ye, B., & Li, W. (2018). Novel noncontact eddy current measurement of electrical conductivity. IEEE Sensors Journal, 18(22), 9352-9359.

(12) Cherry, M. R., Hutson, A., Aldrin, J. C., & Shank, J. (2018). Eddy current analysis of cracks grown from surface defects and non-metallic particles. In AIP Conference Proceedings (Vol. 1949, No. 1, p. 140007). AIP Publishing LLC.

(13) Aldrin, J. C., Sabbagh, H. A., Annis, C., Shell, E. B., Knopp, J., & Lindgren, E. A. (2015, March). Assessing inversion performance and uncertainty in eddy current crack characterization applications. In AIP Conference Proceedings (Vol. 1650, No. 1, pp. 1873-1883). American Institute of Physics.

(14) Cherry, M. R., Sathish, S., Mooers, R. D., Pilchak, A. L., & Grandhi, R. (2017). Modeling of the change of impedance of an eddy current probe due to small changes in host conductivity. IEEE Transactions on Magnetics, 53(5), 1-10.

(15) Herrin, J., Cardillo, N., Timm, S., & Rohlfing, T. (2018). Flaw detection capabilities in aerospace with eddy current array technology. In NDE of Aerospace Materials & Structures 2018 (pp. 44-52).

(16) Zheng, Y., Blasch, E., Liu, Z. (2018) Multispectral Image Fusion and Colorization, SPIE Press.

(17) Blasch, E. Tiley, J. S., Sparkman, D., Donegan, S., Cherry M. (2020). Data fusion methods of materials awareness, Proc. SPIE 11423.

(18) Blasch, E. P., Darema, F., Ravela, S., Aved, A. J., (eds.), (2021). Handbook of Dynamic Data Driven Applications Systems, Vol. 1, 2nd ed., Springer.

(19) Horn, D., & Mayo, W. R. (2000). NDE reliability gains from combining eddy-current and ultrasonic testing. NDT & e International, 33(6), 351-362.

(20) Gros, X. E. (Ed.). (2001). Applications of NDT data fusion (p. 277). Boston, USA: Kluwer Academic Publishers.

(21) Liu, Z., Forsyth, D. S., Komorowski, J. P., Hanasaki, K., & Kirubarajan, T. (2007). Survey: State of the art in NDE data fusion techniques. IEEE transactions on Instrumentation and Measurement, 56(6), 2435-2451.

(22) Heideklang, R., & Shokouhi, P. (2013). Application of data fusion in nondestructive testing (NDT). In Proceedings of the 16th International Conference on Information Fusion (pp. 835-841). IEEE.

(23) Katunin, A., Przystałka, P., & Wronkowicz, A. (2015). Evaluation of Impact Damages in Composites Based on Fusion of Ultrasonic and Optical Images with Optimized Parameters. Machine Dynamics Research, 38(3).

(24) Cotič, P., Jagličić, Z., Niederleithinger, E., Stoppel, M., & Bosiljkov, V. (2014). Image fusion for improved detection of near-surface defects in NDT-CE using unsupervised clustering methods. Journal of nondestructive evaluation, 33(3), 384-397.

(25) Dion, J., Kumar, M., & Ramuhalli, P. (2007). Multi-Sensor Data Fusion for High-Resolution Material Characterization. In AIP Conference Proceedings (Vol. 894, No. 1, pp. 1189-1196). AIP.

(26) Kahrobaee, S., Haghighi, M. S., & Akhlaghi, I. A. (2018). Improving nondestructive characterization of dual phase steels using data fusion. Journal of Magnetism and Magnetic Materials, 458, 317-326.

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