Abstract
Processing and identification of signals informatively characterizing any technological process, especially the signals of acoustic and electromagnetic emission, is a complex multi-level task. Many studies are devoted to solving it. However, no comprehensive system for analyzing these signals has been represented yet. This study found out that there is a relationship between the parameters of laser welding technological process and registered acoustic emission signals, and that relationship is independent on investigation methods. The signals of acoustic and electromagnetic emission have been chosen as feedback signals of laser welding technological process, because their transmission and analysis require minimal time and computational resources. The paper describes the peculiarities of channels shielding for transmitting the technological data, in particular, the circuits of applying the shields for electromagnetic protection are given and their efficiency is shown.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Borkar, P., Malik, L.G.: Acoustic signal based traffic density state estimation using SVM. Int. J. Image Graph. Sig. Process. (IJIGSP) 5(8), 37–44 (2013). https://doi.org/10.5815/ijigsp.2013.08.05
Jager, M., Humbert, S., Hamprecht, F.A.: Sputter tracking for the automatic monitoring of industrial laser-welding processes. IEEE Trans. Industr. Electron. 55(5), 2177–2184 (2008)
Nagashree, R.N., Aswini, N.: Approaches of buried object detection technology. Int. J. Wirel. Microwave Technol. (IJWMT) 4(2), 31–37 (2014). https://doi.org/10.5815/ijwmt.2014.02.04
Sheela Selvakumari, N.A., Radha, V.: Voice pathology identification: a survey on voice disorder. Int. J. Eng. Manuf. (IJEM) 7(2), 39–49 (2017). https://doi.org/10.5815/ijem.2017.02.04
Bahoura, M.: Pattern recognition methods applied to respiratory sounds classification into normal and wheeze classes. Comput. Biol. Med. 39(9), 824–843 (2009)
Huang, C.J., Yang, Y.J., Yang, D.X., Chen, Y.J.: Frog classification using machine learning techniques. Expert Syst. Appl. 36(2), 3737–3743 (2009)
Mporas, I., Ganchev, T., Kocsis, O., Fakotakis, N., Jahn, O., Riede, K.: Integration of temporal contextual information for robust acoustic recognition of bird species from real-field data. Int. J. Intell. Syst. Appl. (IJISA) 5(7), 9–15 (2013). https://doi.org/10.5815/ijisa.2013.07.02
Loutas, T.H., Sotiriades, G., Kalaitzoglou, I., Kostopoulos, V.: Condition monitoring of a single-stage gearbox with artificially induced gear cracks utilizing on-line vibration and acoustic emission measurements. Appl. Acoust. 70(9), 1148–1159 (2009)
Anami, B.S., Pagi, V.B.: Acoustic signal based fault detection in motorcycles – a comparative study of classifiers. Int. J. Image Graph. Sig. Process. (IJIGSP) 5(1), 8–15 (2013). https://doi.org/10.5815/ijigsp.2013.01.02
Ahadi, M., Bakhtiar, M.S.: Leak detection in water-filled plastic pipes through the application of tuned wavelet transforms to acoustic emission signals. Appl. Acoust. 71(7), 634–639 (2010)
Pearson, T.C., Cetin, A.E., Tewfik, A.H., Haff, R.P.: Feasibility of impact-acoustic emissions for detection of damaged wheat kernels. Digit. Sig. Proc. 17(3), 617–633 (2007)
Zhao, Y., Wang, J., Lu, Q., Jiang, R.: Pattern recognition of eggshell crack using PCA and LDA. Innovative Food Sci. Emerg. Technol. 11(3), 520–525 (2010)
Park, Y.W., Park, H., Rhee, S., Kang, M.: Real time estimation of CO2 laser weld quality for automotive industry. Opt. Laser Technol. 34(2), 135–142 (2002)
Conesa, S., Palanco, S., Laserna, J.J.: Acoustic and optical emission during laser-induced plasma formation. Spectrochim. Acta Part B 59(9), 1395–1401 (2004)
Duley, W.W., Mao, Y.L.: The effect of surface condition on acoustic emission during welding of aluminium with CO2 laser radiation. J. Phys. D Appl. Phys. 27(7), 1379–1383 (1994)
de la Rosa, J.G., Puntonet, C.G., Lloret, I.: An application of the independent component analysis to monitor acoustic emission signals generated by termite activity in wood. Measurement 37(1), 63–76 (2005)
Yella, S., Gupta, N.K., Dougherty, M.S.: Comparison of pattern recognition techniques for the classification of impact acoustic emissions. Transp. Res. Part C Emerg. Technol. 15(6), 345–360 (2007)
Saini, D., Floyd, S.: An investigation of gas metal arc welding sound signature for on-line quality control. Weld. J. 77(4), 172–179 (1998)
Molino, A., Martina, M., Vacca, F., Masera, G., Terreno, A., Pasquettaz, G., D’Angelo, G.: FPGA implementation of time-frequency analysis algorithms for laser welding monitoring. Microprocess. Microsyst. 33(3), 179–190 (2009)
Rao, Y., Sarwade, N., Makkar, R.: Denoising and enhancement of medical images using wavelets in LabVIEW. Int. J. Image Graph. Sig. Process. (IJIGSP) 7(11), 42–47 (2015). https://doi.org/10.5815/ijigsp.2015.11.06
Vinoth Kumar, K., Suresh Kumar, S.: LabVIEW based condition monitoring of induction machines. Int. J. Intell. Syst. Appl. (IJISA) 4(3), 56–62 (2012). https://doi.org/10.5815/ijisa.2012.03.08
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Shelyagin, V. et al. (2019). Monitoring of Laser Welding Process Using Its Acoustic Emission Signal. In: Hu, Z., Petoukhov, S., Dychka, I., He, M. (eds) Advances in Computer Science for Engineering and Education. ICCSEEA 2018. Advances in Intelligent Systems and Computing, vol 754. Springer, Cham. https://doi.org/10.1007/978-3-319-91008-6_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-91008-6_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91007-9
Online ISBN: 978-3-319-91008-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)