Detection of thinning of homogeneous material using active thermography and classification trees

• Autorzy: Dudzik Sebastian , Dudek Grzegorz

Identyfikatory

DOI

10.24425/mms.2021.135994

• Języki publikacji: EN

Abstrakty

EN

Active thermography is an efficient tool for defect detection and characterization as it does not change the properties of tested materials. The detection and characterization process involves heating a sample and then analysing the thermal response. In this paper, a long heating pulse was used on samples with a low thermal diffusivity and artificially created holes of various depths. As a result of the experiments, heating and cooling curves were obtained. These curves, which describe local characteristics of the material, are recognized using a classification tree and divided into categories depending on the material thickness (hole depths). Two advantages of the proposed use of classification trees are: an in-built mechanism for feature selection and a strong reduction in the dimensions of the pattern. Based on the experimental study, it can be concluded that classification trees are a useful tool for the thinning detection of homogeneous material.

Słowa kluczowe

EN

active thermography; classification tree; defect detection and characterization; material thinning detection

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2021
• Tom: Vol. 28, nr 1
• Strony: 89--105
• Opis fizyczny: Bibliogr. 18 poz., rys., wykr.,, wzory

Twórcy

autor

Dudzik Sebastian

• Czestochowa University of Technology, Faculty of Electrical Engineering, Al. Armii Krajowej 17, 42-200 Częstochowa, Poland

autor

Dudek Grzegorz

• Czestochowa University of Technology, Faculty of Electrical Engineering, Al. Armii Krajowej 17, 42-200 Częstochowa, Poland

Bibliografia

• [1] Maldague, X. P. (2001). Theory and practice of infrared technology for non-destructive testing, John Wiley & Sons.
• [2] Minkina, W. & Dudzik, S. (2009). Infrared thermography, John Wiley & Sons, Ltd, Chichester.
• [3] Ciampa, F., Mahmoodi, P., Pinto, F. & Meo, M. (2018). Review: Recent Advances in Active Infrared Thermography for Non-Destructive Testing of Aerospace Components. Sensors, 18(2), 609. https://doi.org/10.3390/s18020609
• [4] Montanini, R. (2010). Quantitative determination of subsurface defects in a reference specimen made of Plexiglas by means of lock-in and pulse phase infrared thermography. Infrared Physics & Technology, 53(5), 363-371. https://doi.org/10.1016/j.infrared.2010.07.002
• [5] Marani, R., Palumbo, D., Galietti, U., Stella, E., & D’Orazio, T. (2016). Automatic detection of subsurface defects in composite materials using thermography and unsupervised machine learning. 2016 IEEE 8th International Conference on Intelligent Systems (IS), Bulgaria, 516-521. https://doi.org/10.1109/IS.2016.7737471
• [6] Dudzik, S. (2012). Application of the naive Bayes classifier to defect characterization using active thermography. Journal of Nondestructive Evaluation, 31, 383-392. https://doi.org/10.1007/s10921-012-0149-5
• [7] Popow, V., & Gurka, M. (2019). Possibilities and limitations of passive and active thermography methods for investigation of composite materials using NDT simulations. Smart Structures and NDE for Energy Systems and Industry 4.0 (Vol. 10973, p. 109730K). https://doi.org/10.1117/12.2518226
• [8] Bishop, C. M. (2006). Pattern recognition and machine learning. Springer-Verlag New York.
• [9] Carslaw, H. S. & Jaeger, J. C. (1959). Conduction of heat in solids (2nd ed.). Oxford University Press.
• [10] Saeed, N., Omar, M. A. & Abdulrahman, Y. (2018). A neural network approach for quantifying defects depth, for nondestructive testing thermograms. Infrared Physics & Technology, 94, 55-64. https://doi.org/10.1016/j.infrared.2018.08.022
• [11] Duan, L., Yao, M., Wang, J., Bai, T., & Zhang, L. (2016). Segmented infrared image analysis for rotating machinery fault diagnosis. Infrared Physics & Technology, 77, 267-276. https://doi.org/10.1016/j.infrared.2016.06.011
• [12] Lu, P., Gao, B., Feng, Q., Yang, Y., Woo, W. L., & Tian, G. Y. (2017). Ensemble variational Bayes tensor factorization for super resolution of CFRP debond detection. Infrared Physics & Technology, 85, 335-346. https://doi.org/10.1016/j.infrared.2017.07.012
• [13] Dudzik, S. (2010). Approximation of thermal background applied to defect detection using the methods of active thermography. Metrology and Measurement Systems, 17(4), 621-636. https://doi.org/10.2478/v10178-010-0051-3
• [14] Rokach, L., & Maimon, O. Z. (2008). Data mining with decision trees: theory and applications. World Scientific Pub Co Inc. https://doi.org/10.1142/9789812771728_0001
• [15] Hastie, T. J, Tibshirani, R. J, & Friedman, J. H. (2009).The Elements of Statistical Learning: Data Mining Inference and Prediction (Second Edition). Springer. https://doi.org/10.1007/978-0-387-84858-7
• [16] Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1, 81-106. https://doi.org/10.1007/BF00116251
• [17] Breiman, L., Friedman, J. H., Olshen, R. A. and Stone, C. J. (1984). Classification and regression trees, Wadsworth, Inc.
• [18] Dudek, G. & Dudzik, S. (2017). Classification tree for material defect detection using active thermography. Proceedings of 38th International Conference on Information Systems Architecture and Technology ISAT, Poland, 118-127. https://doi.org/10.1007/978-3-319-67220-5_11

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).