Applying Non-Destructive Testing and Machine Learning to Control of Quality of Ceramic Tiles
Resumo
One of the requirements of the Industry 4.0 is the concern about the stability of its product development. Applying emerging technologies to improve product quality control is a significant step in achieving balance in the production process. The acoustic emission is a highly efficient technique used in industry to test products to detect structural failures. This type of method is well known as non-destructive testing. We assert that quality control of the products can be significantly improved by combining the acoustic emission with machine learning techniques. To achieve our claim, we built a dataset from ceramic tiles acoustic emission, and we model two different machine learning techniques, such as Support Vector Machine, with five distinct kernel functions, and k-Nearest Neighbors, to classify the quality of ceramic tiles. In this work, we use several ceramic tiles as the objects to be tested. To extract the features of the ceramic tiles sound, we have built a prototype to hit the ceramic tile to record the sounds. As the results shown, we obtained a high precision classification of the control quality of the ceramic tile, ranged from 95% to 99.9%, depending on the parameters set and the algorithm chosen.
Palavras-chave:
Industry 4.0, Machine Learning, Non-Destructive Analysis
Referências
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S. Wang, J. Wan, D. Zhang, D. Li, and C. Zhang, “Towards smart factory for industry 4.0: a self-organized multi-agent system with big data based feedback and coordination,” Computer Networks, vol. 101, pp. 158–168, 2016.
S. Boon-itt, “An empirical model of the relationship between manufac- turing capabilities: Evidence from the thai automotive industry,” NIDA Development Journal, vol. 59, no. 2, pp. 19–45, 2010.
J.-S. Chou, Y. Tai, and L.-J. Chang, “Predicting the development cost of tft-lcd manufacturing equipment with artificial intelligence models,” International Journal of Production Economics, vol. 128, no. 1, pp. 339 – 350, 2010, integrating the Global Supply Chain.
T. Wuest, C. Irgens, and K.-D. Thoben, “An approach to monitoring quality in manufacturing using supervised machine learning on product state data,” Journal of Intelligent Manufacturing, vol. 25, no. 5, pp. 1167–1180, 2014.
J. K. Taylor, “What is quality assurance?” in Quality Assurance for Environmental Measurements. ASTM International, 1985.
L. S. Rosado, T. G. Santos, M. Piedade, P. M. Ramos, and P. Vilaça, “Advanced technique for non-destructive testing of friction stir welding of metals,” Measurement, vol. 43, no. 8, pp. 1021–1030, 2010.
B. Li, Y. Shen, and W. Hu, “The study on defects in aluminum 2219- t6 thick butt friction stir welds with the application of multiple non- destructive testing methods,” Materials & Design, vol. 32, no. 4, pp. 2073–2084, 2011.
R. Zoughi, Microwave non-destructive testing and evaluation principles. Springer Science & Business Media, 2012, vol. 4.
H. W. Kohn, “Non destructive testing,” The Journal of General Education, pp. 176–178, 1972.
R. Halmshaw, Non-destructive testing. Arnold, 1991.
A. Pollock, “Acoustic emission-2: acoustic emission amplitudes,” Non-destructive testing, vol. 6, no. 5, pp. 264–269, 1973.
I. Scott and C. Scala, “A review of non-destructive testing of composite materials,” NDT international, vol. 15, no. 2, pp. 75–86, 1982.
R. Raišutis, E. Jasiu ̄niene ̇, R. Šliteris, and A. Vladišauskas, “The review of non-destructive testing techniques suitable for inspection of the wind turbine blades,” Ultragarsas, vol. 63, no. 2, pp. 26–30, 2008.
C. C. H. Guyott, P. Cawley, and R. Adams, “The non-destructive testing of adhesively bonded structure: a review,” The Journal of Adhesion, vol. 20, no. 2, pp. 129–159, 1986.
P. Cawley and R. Adams, “Defect types and non-destructive testing techniques for composites and bonded joints,” Materials science and technology, vol. 5, no. 5, pp. 413–425, 1989.
V. M. Malhotra and N. J. Carino, Handbook on Nondestructive Testing of Concrete Second Edition. CRC press, 2003.
D. McCann and M. Forde, “Review of ndt methods in the assessment of concrete and masonry structures,” Ndt & E International, vol. 34, no. 2, pp. 71–84, 2001.
M. Kesharaju, R. Nagarajah, T. Zhang, and I. Crouch, “Ultrasonic sensor based defect detection and characterisation of ceramics,” Ultrasonics, vol. 54, no. 1, pp. 312–317, 2014.
M. Kesharaju and R. Nagarajah, “Feature selection for neural network based defect classification of ceramic components using high frequency ultrasound,” Ultrasonics, vol. 62, pp. 271–277, 2015.
M. A. Kewalramani and R. Gupta, “Concrete compressive strength prediction using ultrasonic pulse velocity through artificial neural net- works,” Automation in Construction, vol. 15, no. 3, pp. 374–379, 2006.
M. Bilgehan and P. Turgut, “Artificial neural network approach to pre- dict compressive strength of concrete through ultrasonic pulse velocity,” Research in Nondestructive Evaluation, vol. 21, no. 1, pp. 1–17, 2010.
T. Feng, X. Xiao-Mei, S. Tso, and K. Liu, “Application of evolutionary neural network in impact acoustics based nondestructive inspection of tile-wall,” in Communications, Circuits and Systems, 2005. Proceedings. 2005 International Conference on, vol. 2. IEEE, 2005.
F. Tong, S. Tso, and X. Xu, “Tile-wall bonding integrity inspection based on time-domain features of impact acoustics,” Sensors and actuators A: Physical, vol. 132, no. 2, pp. 557–566, 2006.
F. Tong, S. Tso, and M. Hung, “Impact-acoustics-based health monitoring of tile-wall bonding integrity using principal component analysis,” Journal of sound and vibration, vol. 294, no. 1, pp. 329–340, 2006.
F. Tong, X. Xu, B. Luk, and K. Liu, “Evaluation of tile–wall bonding integrity based on impact acoustics and support vector machine,” Sensors and Actuators A: Physical, vol. 144, no. 1, pp. 97–104, 2008.
B. L. Luk, K. Liu, F. Tong, and K. Man, “Impact-acoustics inspection of tile-wall bonding integrity via wavelet transform and hidden markov models,” Journal of sound and vibration, vol. 329, no. 10, pp. 1954– 1967, 2010.
S. Theodoridis, A. Pikrakis, K. Koutroumbas, and D. Cavouras, Introduction to pattern recognition: a matlab approach. Academic Press, 2010.
S. Theodoridis and K. Koutroumbas, “Chapter 5 - feature selection,” in Pattern Recognition, fourth edition ed., S. Theodoridis and K. Koutroumbas, Eds. Boston: Academic Press, 2009, pp. 261 – 322.
H. Misra, S. Ikbal, H. Bourlard, and H. Hermansky, “Spectral entropy based feature for robust asr,” in Acoustics, Speech, and Signal Processing, 2004. Proceedings.(ICASSP’04). IEEE International Conference on, vol. 1. IEEE, 2004, pp. I–193.
T. Giannakopoulos and S. Petridis, “Unsupervised speaker clustering in a linear discriminant subspace,” in Machine Learning and Applications (ICMLA), 2010 Ninth International Conference on. IEEE, 2010, pp. 1005–1009.
M. Slaney, “Auditory toolbox,” Interval Research Corporation, Tech. Rep, vol. 10, p. 1998, 1998.
T. Giannakopoulos and A. Pikrakis, “Chapter 4 - audio features,” in Introduction to Audio Analysis, T. Giannakopoulos and A. Pikrakis, Eds. Oxford: Academic Press, 2014, pp. 59 – 103.
H.-G. Kim, N. Moreau, and T. Sikora, MPEG-7 audio and beyond: Audio content indexing and retrieval. John Wiley & Sons, 2006.
G. H. Wakefield, “Mathematical representation of joint time-chroma distributions,” in International Symposium on Optical Science, Engineering, and Instrumentation, SPIE, vol. 99, 1999, pp. 18–23.
M. A. Bartsch and G. H. Wakefield, “Audio thumbnailing of popular music using chroma-based representations,” IEEE Transactions on multimedia, vol. 7, no. 1, pp. 96–104, 2005.
——, “To catch a chorus: Using chroma-based representations for audio thumbnailing,” in Applications of Signal Processing to Audio and Acoustics, 2001 IEEE Workshop on the, 2001, pp. 15–18.
M. Müller, F. Kurth, and M. Clausen, “Audio matching via chroma- based statistical features.” in ISMIR, vol. 2005, 2005, p. 6th.
S. Selvaluxmiy, T. Kumara, P. Keerthanan, R. Velmakivan, R. Ragel, and S. Deegalla, “Accelerating k-nn classification algorithm using graphics processing units,” in Information and Automation for Sustainability (ICIAfS), 2016 IEEE International Conference on. IEEE, 2016, pp. 1–6.
W. Fang, K. K. Lau, M. Lu, X. Xiao, C. K. Lam, P. Y. Yang, B. He, Q. Luo, P. V. Sander, and K. Yang, “Parallel data mining on graphics processors,” Hong Kong Univ. Sci. and Technology, Hong Kong, China, Tech. Rep. HKUST-CS08-07, 2008.
L. Demidova, I. Klyueva, Y. Sokolova, N. Stepanov, and N. Tyart, “Intellectual approaches to improvement of the classification decisions quality on the base of the svm classifier,” Procedia Computer Science, vol. 103, pp. 222–230, 2017.
J. Vavrek, E. Vozáriková, M. Pleva, and J. Juhár, “Broadcast news audio classification using svm binary trees,” in Telecommunications and Signal Processing (TSP), 2012 35th International Conference on. IEEE, 2012, pp. 469–473.
L. Demidova and I. Klyueva, “Svm classification: Optimization with the smote algorithm for the class imbalance problem,” in Embedded Computing (MECO), 2017 6th Mediterranean Conference on. 2017, pp. 1–4.
Publicado
06/11/2018
Como Citar
CUNHA, Renan et al.
Applying Non-Destructive Testing and Machine Learning to Control of Quality of Ceramic Tiles. In: SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SISTEMAS COMPUTACIONAIS (SBESC), 8. , 2018, Salvador.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 66-73.
ISSN 2237-5430.
