Abstract
Welding fault detection in the industry of hot water tanks remains typically conducted visually or with the assistance of None Destructive Examination, such as X-ray, ultrasound, and penetrant testing. However, this leads to high consumption of time and resources. We propose in this paper a two-level method for automatic welding defect detection and localization. The method is based on the classification of the probability density distributions of the voltage signals underlying the generated stochastic process from the welding operation. In the main phase, we apply a passband filter to the raw signals and use the Kernel Density Estimation to measure the distribution of the filtered signal. The probability density distributions are processed as functional data and classified employing a functional non-parametric kernel classifier. In the second phase, the signal of nonconforming welding is split into segments and their probability density distributions are classified in order to extract the precise location of the defect in the whole signal. The proposed method allows to detect and localize welding defects with high accuracy.
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References
Arabaci, H. et Laving, S. (2019). Weld Defect Categorization from Welding Current using Principle Component Analysis. International Journal of Advanced Computer Science and Applications,Vol. 10, No. 6.
Cui, Y., Shi, Y., Zhu, T., & Cui, S. (2020). Welding penetration recognition based on arc sound and electrical signals in K-TIG welding. Measurement, p. 107966.
Ferraty, F., & Vieu, P. (2003). Curves discrimination: a nonparametric functional approach. Computational Statistics & Data Analysis, 44(1–2), 161–173.
Hall, P., Poskitt, D. S., & Presnell, B. (2001). A functional data-Analytic approach to signal discrimination. Technometrics, 43(1), 1–9.
HE, K., & et Li, X. (2019). Time-frequency feature extraction of acoustic emission signals in aluminum alloy mig welding process based on SST and PCA. IEEE Access, 7, 113988–113998.
Huang, Y., Yang, D., Wang, K., et al. (2020). Stability analysis of GMAW based on multi-scale entropy and genetic optimized support vector machine. Measurement, 151, 107282.
Kamalov, F. (2020). Kernel density estimation based sampling for imbalanced class distribution. Information Sciences, 512, 1192–1201.
Kumar, V., Albert, S. K., & Chandrasekhar, N. (2019). Signal processing approach on weld data for evaluation of arc welding electrodes using probability density distributions. Measurement, 133, 23–32.
Madigan, R. (1999). Arc sensing for defects in constant-voltage gas metal arc welding. Weld J, 78, 322S-328S.
Mirapeix, J., Garcia-Allende, P. B., Cobo, A., et al. (2007). Real-time arc-welding defect detection and classification with principal component analysis and artificial neural networks. NDT & e International, 40(4), 315–323.
Narwadkar, A., & Bhosle, S. (2016). Optimization of MIG welding parameters to control the angular distortion in Fe410WA steel. Materials and Manufacturing Processes, 31(16), 2158–2164.
Parzen, E. (1962). On estimation of a probability density function and mode. The Annals of Mathematical Statistics, 33(3), 1065–1076.
Pernambuco, B. S. G., Steffens, C. R., Pereira, J. R., Werhli, A. V., Azzolin, R. Z., & Estrada, E. D. S. D. (2019). Online sound based Arc-welding defect detection using artificial neural networks. In 2019 Latin American robotics symposium (LARS), 2019 Brazilian symposium on robotics (SBR) and 2019 workshop on robotics in education (WRE). IEEE, 2019. pp. 263-268.
Sumesh, A., Nair, B., Rameshkumar, K., et al. (2018). Decision tree based weld defect classification using current and voltage signatures in GMAW process. Materials Today: Proceedings, 5(2), 8354–8363.
Sumesh, A., Rameshkumar, K., Mohandas, K., et al. (2015). Use of machine learning algorithms for weld quality monitoring using acoustic signature. Procedia Computer Science, 50, 316–322.
Thekkuden, D., Santhakumari, A., Sumesh, A., et al. (2018). Instant detection of porosity in gas metal arc welding by using probability density distribution and control chart. The International Journal of Advanced Manufacturing Technology, 95(9–12), 4583–4606.
Weglarczyk, S. (2018). Kernel density estimation and its application. In ITM web of conferences (Vol. 23). EDP Sciences.
Zhang, Z., & Chen, S. (2017). Real-time seam penetration identification in arc welding based on fusion of sound, voltage and spectrum signals. Journal of Intelligent Manufacturing, 28(1), 207–218.
Zhang, Z., Chen, X., Chen, H., et al. (2014). Online welding quality monitoring based on feature extraction of arc voltage signal. The International Journal of Advanced Manufacturing Technology, 70(9–12), 1661–1671.
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Melakhsou, A.A., Batton-Hubert, M. Welding monitoring and defect detection using probability density distribution and functional nonparametric kernel classifier. J Intell Manuf 34, 1469–1481 (2023). https://doi.org/10.1007/s10845-021-01871-3
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DOI: https://doi.org/10.1007/s10845-021-01871-3