Curve/Probabilistic Fitting of Damage Metrics for Al-7075 Materials Behavior by Using Electromechanical Impedance Method

Year 2021, , 481 - 494, 01.06.2021

Gökhan Haydarlar , Mesut Tekkalmaz , Mehmet Alper Sofuoğlu

https://doi.org/10.2339/politeknik.698644

Cited By: 2

Abstract

The purpose of structural health monitoring is to provide information by making a simultaneous diagnosis of the status of the structure. Despite aging, environmental conditions and unforeseen circumstances, the construction should remain as specified in the design. Changing the environmental conditions causes the sensor and the host structure to change material properties. It is essential to take into account environmental conditions to prevent misdiagnosis. Therefore, the real cause of the change can be determined.
In this study, the behavior of constrained piezoelectric wafer active sensor (PWAS)/Al 7075 was investigated by using electromechanical impedance method (EMI) under changing environmental conditions. Numerical studies on this material in the literature are limited and all the experimental/ numerical results are compensated for the temperature effect and analyzed using curve/probabilistic fitting approach for the first time. The sample used in the experimental work was modeled in ANSYS finite element program. In the experimental and numerical results, it has been observed that as the temperature decreases, the frequency shifts to the right and the amplitude increases. The experimental and simulation results were nearly the same. The temperature effect was compensated using the compensation algorithm for experimental and numerical studies. The results were compared using damage metrics. The experimental results analyzed using a curve/probabilistic fitting approach

Keywords

Structural health monitoring, piezoelectric sensors, electromechanical impedance method, temperature effect, damage metrics, curve fitting

Supporting Institution

Eskişehir Osmangazi Üniverstesi

Project Number

201715A119

Thanks

The authors declare that there is no conflict of interest. This article does not contain any studies with human participants or animals performed by any of the authors. This work was supported by the Scientific Research Projects Commission of Eskişehir Osmangazi University as project number 201715A119.

References

• Aktan, A. E., Helmicki, A. J., Hunt, V. J., 1998, Issues in health monitoring for intelligent infrastructure, Smart Materials and Structures, 7(5), 674-692.
• Amezquita-Sanchez, J., Valtierra-Rodriguez, M., Aldwaik, M., Adeli, H., 2016, Neurocomputing in Civil Infrastructure, Scientia Iranica, 23(6), pp. 2417-2428.
• APC International Ltd. Product Manual- Piezoelectric Ceramic: Principles and Applications, 2006, http://www.americampiezo.com
• Baptista, F. G., Budoya, D. E., de Almeida, V. A., Ulson, J. A., 2014, An experimental study on the effect of temperature on piezoelectric sensors for impedance-based structural health monitoring, Sensors (Basel), 14(1), 1208-1227.
• Bukhari, S., Islam, M., Haziot, A., Beamish, J., 2014, Shear piezoelectric coefficients of PZT, LiNbO3and PMN-PT at cryogenic temperatures, Journal of Physics: Conference Series, 568(3). Choi, K., Chang, F.-K., 1996, Identification of impact force and location using distributed sensors, AIAA Journal, 34(1), 136-142. Doebling, S. W., Farrar, C. R., Prime, M. B., 1998, A summary review of vibration-based damage identification methods, The Shock and Vibration Digest, 30(2), 91-105.
• Fallahian, S., Joghataie, A., Kazemi, M., 2017, Structural damage detection using time domain responses and teaching–learning-based optimization (TLBO) algorithm, Scientia Iranica, (article in press),doi: 10.24200/sci.2017.4238 Freitas, V. F., Santos, I. A., Botero, É., Fraygola, B. M., Garcia, D., Eiras, J. A., 2011, Piezoelectric Characterization of (0.6)BiFeO3-(0.4)PbTiO3 Multiferroic Ceramics, Journal of the American Ceramic Society, 94(3), 754-758.
• Goldberg, D.E., 1989, Genetic algorithms in search, optimization, and machine learning. Reading, Mass: Addison-Wesley Pub. Co.
• Grisso, B. L., Inman, D. J., 2010, Temperature corrected sensor diagnostics for impedance-based SHM, Journal of Sound and Vibration, 329(12), 2323-2336.
• Gupta, V., Sharma, M., Thakur, N., Singh, S. P., 2011, Active vibration control of a smart plate using a piezoelectric sensor–actuator pair at elevated temperatures, Smart Materials and Structures, 20(10).
• Haider, M. F., Giurgiutiu, V., Lin, B., Yu, L., 2017, Irreversibility effects in piezoelectric wafer active sensors after exposure to high temperature, Smart Materials and Structures, 26(9).
• Hooker, M. W., 1998, Properties of PZT-based Piezoelectric Ceramics between 150 and 250 °C. Technical Report, National Aeronautics and Space Administration.
• Imaoka, S, 1999, Ansys tip of the week: Conversion of piezoelectric material data, http://ansys.net/ansys/tips/Week13 TNT Conversion of Piezoelectric Material Data. pdf
• Jyoti, A., 2008, Modelling and analysis of PZT Micro power Generator, PhD Thesis, Auburn University.
• Kabeya, K. I., 1998, Structural Health Monitoring Using Multiple Piezoelectric Sensors and Actuators, Master of Science thesis, VirginiaTech University.
• Lerch, R., 1990, Simulation of piezoelectric devices by two- and three-dimensional finite elements, IEEE Trans Ultrason Ferroelectr Freq Control, 37(3), 233-247.
• Michalewicz, Z., 1996, Genetic algorithms + data structures = evolution programs, 3rd rev. and extended ed. Berlin ; New York: Springer-Verlag.
• Naillon, M., Coursant, R., Besnier, F., 1983, Analysis of piezoelectric structures by a finite-element method, Acta Electronica, 25(4), 341-362.
• PI Ceramic Piezoelectric Materials- Material Data, 1996, https://www.piceramic.com/en/products/piezoceramic-materials/#c15162 Piezo System Inc., 1997, http://www.piezo.com/catalog8.pdf
• Rabelo, D.S., Steffen, V., Neto, R.M.F. and Lacerda, H.B., 2017, Impedance-based structural health monitoring and statistical method for threshold-level determination applied to 2024-T3 aluminum panels under varying temperature, Structural Health Monitoring, 16(4), 365-381.
• Rezvani, K. Maia, M., Sabour, M. 2018, Comparison of Some Methods for Structural Damage Detection, Scientia Iranica, 25(3), pp. 1312-1322.
• Rogers, C.,1990, Intelligent material systems and structures. In U.S.-Japan Workshop on Smart/Intelligent Materials and Systems, Honolulu, HI, 11-33.
• Sepehry, N., Shamshirsaz, M., Bastani, A., 2010, Experimental and theoretical analysis in impedance-based structural health monitoring with varying temperature, Structural Health Monitoring: An International Journal, 10(6), 573-585.
• Shankar, R., 2009, An integrated approach for structural health monitoring, PhD thesis, Indian Institute of Technology Delhi, Department of Civil Engineering, India.
• Türker, Ö., 2009, Designing And Manufacturing of PZT/ Polymer Based Smart Beams Which Compatible Active Vibration Control, Master Thesis, Istanbul Technical University, Istanbul, Turkey.
• Wandowski, T., Malinowski, P. H., Ostachowicz, W. M., 2016, Delamination detection in CFRP panels using EMI method with temperature compensation, Composite Structures, 151, 99-107.
• Xu, G., Xu, B., Xu, C., Luo, Y., 2016, Temperature effects in the analysis of electromechanical impedance by using spectral element method, Multidiscipline Modeling in Materials and Structures, 12(1), 119-132.
• Yang, Y., Lim, Y. Y., Soh, C. K., 2008, Practical issues related to the application of the electromechanical impedance technique in the structural health monitoring of civil structures: I. Experiment, Smart Materials and Structures, 17(3).
• Zou, D., Liu, T., Liang, C., Huang, Y., Zhang, F., Du, C., 2015, An experimental investigation on the health monitoring of concrete structures using piezoelectric transducers at various environmental temperatures, Journal of Intelligent Material Systems and Structures, 26(8), 1028-1034.