DEFECT CHARACTERIZATION OF METALLIC MATERIALS USING SELF-PARAMETERIZED DENSITY-BASED CLUSTERING AND COMPUTATIONAL INTELLIGENCE TECHNIQUES
Keywords:
material defect characterization, ultrasonic non-destructive testing, density-based clustering, artificial intelligence
Abstract
Rapid growth of manufacturing industries is propelled by transformative technologies such as machine intelligence, autonomous computing and non-destructive testing (NDT). During the manufacturing of wrought products, there is no guarantee that the final product is 100-% flawless. Thus, all final products are subjected to quality checking to identify and eliminate defective products. In industries, most of internal defects are identified using NDT techniques, which fail to precisely characterize the defects. In this paper a novel algorithm, called Self-Parameterized Density-Based Clustering (SPDBC), is proposed for defect characterization. The proposed clustering method uses spatial parameters to identify the size and position of defects by filtering out the noise and other data that correspond to the non-defect area. Using these filtered data, computational intelligence techniques are employed to predict the defect type. SPDBC achieved Jaccard indices of 97.02 % and 98.78 % for identifying the defect size and position, respectively. Gradient boosting regression trees (GBRT) achieved a maximum accuracy of 97.44 % in predicting the defect type. As a result, the proposed approach can assist NDT experts in various sectors to differentiate between problem severities faster and replace defective parts before any major breakdown occurs.
References
[1] A Sifa, A S Baskoro, S Sugeng, B Badruzzaman and T Endramawan, Identification of the Thickness of Nugget on Worksheet Spot Welding Using Non Destructive Test (NDT) – Effect of Pressure, Proc. of the 2nd Inter. Conf. Ser. Mater. Sci. Eng., 2018, 306 012009.
[2] S. Sambath, P. Nagaraj, N. Selvakumar, S. Arunachalam, and , T Page, Automatic detection of defects in ultrasonic testing using artificial neural network, Int. J. Microstruct. Mater. Prop., 5 (2010) 6, 561–574, doi: 10.1504/IJMMP.2010.038155.
[3] D. Selvathi, I. H. Nithilla, and N. Akshaya, Image Processing Techniques for Defect Detection in Metals using Thermal Images, Proc. of the 3rd Inter. Conf. ICOEI, 2019, 939–944.
[4] A. El Kouche and H. S. Hassanein, Ultrasonic non-destructive testing (NDT) using wireless sensor networks, Procedia Comput. Sci., 10 (2012) 2, 136–143, doi: 10.1016/j.procs.2012.06.021.
[5] M. Jolly, Review of Non-destructive Testing (NDT) Techniques and their Applicability to Thick Walled Composites, Procedia CIRP, 38 (2015) 1, 129–136, 2015, doi: 10.1016/j.procir.2015.07.043.
[6] Y. Jia, L. Tang, P. Ming, and Y. Xie, “Ultrasound-excited thermography for detecting microcracks in concrete materials,” NDT E Int.,101 (2019) 2, 62–71, doi: 10.1016/j.ndteint.2018.10.006.
[7] A Savin, Influence of the defect characteristic in aluminum alloy 7075 in non-destructive electromagnetic testing, Proc. of the 6th Inter. Conf. Ser. Mater. Sci. Eng., 2022, 1262 012029.
[8] E. Jasiūnienė, L. Mažeika, V. Samaitis, V. Cicėnas, and D. Mattsson, Ultrasonic non-destructive testing of complex titanium/carbon fibre composite joints, Ultrasonics, 95 (2019) 1, 13–21, doi: 10.1016/j.ultras.2019.02.009.
[9] H Towsyfyan, A Biguri, R Boardman, and TBlumensath, Successes and challenges in non-destructive testing of aircraft composite structures, Chinese J. Aeronaut., 33, (2020) 3,771–791, doi: https://doi.org/10.1016/j.cja.2019.09.017.
[10] R. Leiderman and A. M. B. Braga, Scattering of guided waves by defective adhesive bonds in multilayer anisotropic plates, Wave Motion,74 (2017) 3, 93–104, doi: 10.1016/j.wavemoti.2017.05.007.
[11] T. D’Orazio, M. Leo, A. Distante, C. Guaragnella, V. Pianese, and G. Cavaccini, Automatic ultrasonic inspection for internal defect detection in composite materials, NDT E Int., 41 (2008) 2, 145–154, doi: 10.1016/j.ndteint.2007.08.001.
[12] M. E. Buchanan, Methods of data collection, AORN J., 33 (1981) 1, 137–149, doi: 10.1016/S0001-2092(07)69400-9.
[13] J. Peters, Computational Intelligence: Principles, Techniques and Applications, Comput. J., 50 (2007) 6, 758-765, doi: 10.1093/comjnl/bxm073.
[14] L. Sepulvene, Performance Evaluation of Machine Learning Techniques for Fault Diagnosis in Vehicle Fleet Tracking Modules, Comput. J., 65 (2021) 8, 2073–2086, doi: 10.1093/comjnl/bxab047.
[15] H Shi, P Wang, X Yang and H Yu, An Improved Mean Imputation Clustering Algorithm for Incomplete Data, Neural Process. Lett., 54 (2020) 5, 203-212, doi: 10.1007/s11063-020-10298-5.
[16] DQ Zhang and SC Chen, Clustering Incomplete Data Using Kernel-Based Fuzzy C-means Algorithm, Neural Process. Lett., 18 (2003) 3,155–162, doi: 10.1023/B:NEPL.0000011135.19145.1b.
[17] T M Meksen, B Boudraa and M Boudraa, Defects clustering using kohonen networks during ultrasonic inspection, IAENG Int. J. Comput. Sci., 36 (2009) 3, 1-4.
[18] S Arivazhagan, J Jasline Tracia and N Selvakumar, Mater. Res. Express, 6 (2019) 9, 6 096539, DOI 10.1088/2053-1591/ab2d83.
[19] I Ghafoor, P W TSE, N Munir, and A J C Trappey, Non-contact detection of railhead defects and their classification by using convolutional neural network, Optik (Stuttg)., 253 (2022) 2, 168607,doi: 10.1016/J.IJLEO.2022.168607.
[20] V A Golodov and AA Maltseva, Approach to weld segmentation and defect classification in radiographic images of pipe welds, NDT E Int., 127 (2022) 3, 102597, doi: 10.1016/J.NDTEINT.2021.102597.
[21] Y Ahmad Al-Maharma, P Sandeep Patil and B Markert, Effects of porosity on the mechanical properties of additively manufactured components: a critical review, Mater. Res. Express, 7 (2020) 12, 7 122001, DOI 10.1088/2053-1591/abcc5d
[22] https://onlinelibrary.wiley.com/doi/10.1002/0471719145.ch11
[23] S Sambath, P Nagaraj and N Selvakumar, Automatic Defect Classification in Ultrasonic NDT Using Artificial Intelligence, J. Nondestruct. Eval., 30 (2011) 1, 20–28, doi: 10.1007/s10921-010-0086-0.
[24] P Radha, N Selvakumar, J Raja Sekar, and J V Johnsonselva, Density-Based Dynamically Self-Parameterized Clustering for Material Inspection, Comput. J., 66 (2021) 2, 416–428, doi: 10.1093/comjnl/bxab169.
[25] N S Altman, An introduction to kernel and nearest-neighbor nonparametric regression, Am. Stat.,46 (1992) 3,175–185, doi: 10.1080/00031305.1992.10475879.
[26] H F Chen, In silico log p prediction for a large data set with support vector machines, radial basis neural networks and multiple linear regression, Chem. Biol. Drug Des., 74 (2009) 2, 142–147, doi: 10.1111/j.1747-0285.2009.00840.x.
[27] J R Quinlan, Induction of decision trees, Mach. Learn., 1 (1986) 1, 81–106, doi: 10.1007/bf00116251.
[28] https://towardsdatascience.com/a-mathematical-explanation-of-naive-bayes-in-5-minutes-44adebcdb5f8
[29] T K Ho, Random decision forests, Proc. Int. Conf. Doc. Anal. Recognition, ICDAR, 5 (1995) 1, 278–282, doi: 10.1109/ICDAR.1995.598994.
[30] Y Freund and R E Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, J. Comput. Syst. Sci., 55 (1997) 1, 119–139, doi: 10.1006/jcss.1997.1504.
[31] http://arxiv.org/abs/1609.04747
[32] E Grossi and M Buscema, Introduction to artificial neural networks, Eur. J. Gastroenterol. Hepatol., 19 (2007) 12, 1046–1054,doi: 10.1097/MEG.0b013e3282f198a0.
[33] B Boehmke and B Greenwell, Hands-On Machine Learning with R, 1st ed., Taylor & Francis, New York, 1991, 484.
[34] https://www.analyticsvidhya.com/blog/2020/04/confusion-matrix-machine-learning/
[35] https://en.wikipedia.org/w/index.php?title=Jaccard_index&oldid=1100399116
[36] X Jin and J Han, K-Means Clustering, A Matrix Algebra Approach to Artificial Intelligence, 1st ed., Springer, US, 563.
[37] https://en.wikipedia.org/w/index.php?title=Hierarchical_clustering&oldid=1096531593
[38] B J Frey and D Dueck, Clustering by passing messages between data points, Science., 315 (2007) 5814, 972–976, doi: 10.1126/science.1136800.
[39] J R Sekar, N Selvakumar, P Radha and J V Johnsonselva, Supervised and Unsuper-vised learning for characterizing the industrial material defects, International Journal of Business Intelligence and Data Mining, 21 (2022) 3, 233 – 246, doi:10.1504/IJBIDM.2022.10039148.
[40] Liu Y, Zhou Y, Wen S and Tang C, A Strategy on Selecting Performance Metrics for Classifier Evaluation, International Journal of Mobile Computing and Multimedia Communications, 6 (2014) 1, 20 - 35, doi:10.4018/IJMCMC.2014100102.
[2] S. Sambath, P. Nagaraj, N. Selvakumar, S. Arunachalam, and , T Page, Automatic detection of defects in ultrasonic testing using artificial neural network, Int. J. Microstruct. Mater. Prop., 5 (2010) 6, 561–574, doi: 10.1504/IJMMP.2010.038155.
[3] D. Selvathi, I. H. Nithilla, and N. Akshaya, Image Processing Techniques for Defect Detection in Metals using Thermal Images, Proc. of the 3rd Inter. Conf. ICOEI, 2019, 939–944.
[4] A. El Kouche and H. S. Hassanein, Ultrasonic non-destructive testing (NDT) using wireless sensor networks, Procedia Comput. Sci., 10 (2012) 2, 136–143, doi: 10.1016/j.procs.2012.06.021.
[5] M. Jolly, Review of Non-destructive Testing (NDT) Techniques and their Applicability to Thick Walled Composites, Procedia CIRP, 38 (2015) 1, 129–136, 2015, doi: 10.1016/j.procir.2015.07.043.
[6] Y. Jia, L. Tang, P. Ming, and Y. Xie, “Ultrasound-excited thermography for detecting microcracks in concrete materials,” NDT E Int.,101 (2019) 2, 62–71, doi: 10.1016/j.ndteint.2018.10.006.
[7] A Savin, Influence of the defect characteristic in aluminum alloy 7075 in non-destructive electromagnetic testing, Proc. of the 6th Inter. Conf. Ser. Mater. Sci. Eng., 2022, 1262 012029.
[8] E. Jasiūnienė, L. Mažeika, V. Samaitis, V. Cicėnas, and D. Mattsson, Ultrasonic non-destructive testing of complex titanium/carbon fibre composite joints, Ultrasonics, 95 (2019) 1, 13–21, doi: 10.1016/j.ultras.2019.02.009.
[9] H Towsyfyan, A Biguri, R Boardman, and TBlumensath, Successes and challenges in non-destructive testing of aircraft composite structures, Chinese J. Aeronaut., 33, (2020) 3,771–791, doi: https://doi.org/10.1016/j.cja.2019.09.017.
[10] R. Leiderman and A. M. B. Braga, Scattering of guided waves by defective adhesive bonds in multilayer anisotropic plates, Wave Motion,74 (2017) 3, 93–104, doi: 10.1016/j.wavemoti.2017.05.007.
[11] T. D’Orazio, M. Leo, A. Distante, C. Guaragnella, V. Pianese, and G. Cavaccini, Automatic ultrasonic inspection for internal defect detection in composite materials, NDT E Int., 41 (2008) 2, 145–154, doi: 10.1016/j.ndteint.2007.08.001.
[12] M. E. Buchanan, Methods of data collection, AORN J., 33 (1981) 1, 137–149, doi: 10.1016/S0001-2092(07)69400-9.
[13] J. Peters, Computational Intelligence: Principles, Techniques and Applications, Comput. J., 50 (2007) 6, 758-765, doi: 10.1093/comjnl/bxm073.
[14] L. Sepulvene, Performance Evaluation of Machine Learning Techniques for Fault Diagnosis in Vehicle Fleet Tracking Modules, Comput. J., 65 (2021) 8, 2073–2086, doi: 10.1093/comjnl/bxab047.
[15] H Shi, P Wang, X Yang and H Yu, An Improved Mean Imputation Clustering Algorithm for Incomplete Data, Neural Process. Lett., 54 (2020) 5, 203-212, doi: 10.1007/s11063-020-10298-5.
[16] DQ Zhang and SC Chen, Clustering Incomplete Data Using Kernel-Based Fuzzy C-means Algorithm, Neural Process. Lett., 18 (2003) 3,155–162, doi: 10.1023/B:NEPL.0000011135.19145.1b.
[17] T M Meksen, B Boudraa and M Boudraa, Defects clustering using kohonen networks during ultrasonic inspection, IAENG Int. J. Comput. Sci., 36 (2009) 3, 1-4.
[18] S Arivazhagan, J Jasline Tracia and N Selvakumar, Mater. Res. Express, 6 (2019) 9, 6 096539, DOI 10.1088/2053-1591/ab2d83.
[19] I Ghafoor, P W TSE, N Munir, and A J C Trappey, Non-contact detection of railhead defects and their classification by using convolutional neural network, Optik (Stuttg)., 253 (2022) 2, 168607,doi: 10.1016/J.IJLEO.2022.168607.
[20] V A Golodov and AA Maltseva, Approach to weld segmentation and defect classification in radiographic images of pipe welds, NDT E Int., 127 (2022) 3, 102597, doi: 10.1016/J.NDTEINT.2021.102597.
[21] Y Ahmad Al-Maharma, P Sandeep Patil and B Markert, Effects of porosity on the mechanical properties of additively manufactured components: a critical review, Mater. Res. Express, 7 (2020) 12, 7 122001, DOI 10.1088/2053-1591/abcc5d
[22] https://onlinelibrary.wiley.com/doi/10.1002/0471719145.ch11
[23] S Sambath, P Nagaraj and N Selvakumar, Automatic Defect Classification in Ultrasonic NDT Using Artificial Intelligence, J. Nondestruct. Eval., 30 (2011) 1, 20–28, doi: 10.1007/s10921-010-0086-0.
[24] P Radha, N Selvakumar, J Raja Sekar, and J V Johnsonselva, Density-Based Dynamically Self-Parameterized Clustering for Material Inspection, Comput. J., 66 (2021) 2, 416–428, doi: 10.1093/comjnl/bxab169.
[25] N S Altman, An introduction to kernel and nearest-neighbor nonparametric regression, Am. Stat.,46 (1992) 3,175–185, doi: 10.1080/00031305.1992.10475879.
[26] H F Chen, In silico log p prediction for a large data set with support vector machines, radial basis neural networks and multiple linear regression, Chem. Biol. Drug Des., 74 (2009) 2, 142–147, doi: 10.1111/j.1747-0285.2009.00840.x.
[27] J R Quinlan, Induction of decision trees, Mach. Learn., 1 (1986) 1, 81–106, doi: 10.1007/bf00116251.
[28] https://towardsdatascience.com/a-mathematical-explanation-of-naive-bayes-in-5-minutes-44adebcdb5f8
[29] T K Ho, Random decision forests, Proc. Int. Conf. Doc. Anal. Recognition, ICDAR, 5 (1995) 1, 278–282, doi: 10.1109/ICDAR.1995.598994.
[30] Y Freund and R E Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, J. Comput. Syst. Sci., 55 (1997) 1, 119–139, doi: 10.1006/jcss.1997.1504.
[31] http://arxiv.org/abs/1609.04747
[32] E Grossi and M Buscema, Introduction to artificial neural networks, Eur. J. Gastroenterol. Hepatol., 19 (2007) 12, 1046–1054,doi: 10.1097/MEG.0b013e3282f198a0.
[33] B Boehmke and B Greenwell, Hands-On Machine Learning with R, 1st ed., Taylor & Francis, New York, 1991, 484.
[34] https://www.analyticsvidhya.com/blog/2020/04/confusion-matrix-machine-learning/
[35] https://en.wikipedia.org/w/index.php?title=Jaccard_index&oldid=1100399116
[36] X Jin and J Han, K-Means Clustering, A Matrix Algebra Approach to Artificial Intelligence, 1st ed., Springer, US, 563.
[37] https://en.wikipedia.org/w/index.php?title=Hierarchical_clustering&oldid=1096531593
[38] B J Frey and D Dueck, Clustering by passing messages between data points, Science., 315 (2007) 5814, 972–976, doi: 10.1126/science.1136800.
[39] J R Sekar, N Selvakumar, P Radha and J V Johnsonselva, Supervised and Unsuper-vised learning for characterizing the industrial material defects, International Journal of Business Intelligence and Data Mining, 21 (2022) 3, 233 – 246, doi:10.1504/IJBIDM.2022.10039148.
[40] Liu Y, Zhou Y, Wen S and Tang C, A Strategy on Selecting Performance Metrics for Classifier Evaluation, International Journal of Mobile Computing and Multimedia Communications, 6 (2014) 1, 20 - 35, doi:10.4018/IJMCMC.2014100102.
Published
2024-12-03
How to Cite
1.
J. V.J, J.RS. DEFECT CHARACTERIZATION OF METALLIC MATERIALS USING SELF-PARAMETERIZED DENSITY-BASED CLUSTERING AND COMPUTATIONAL INTELLIGENCE TECHNIQUES. MatTech [Internet]. 2024Dec.3 [cited 2026Oct.11];58(6):719–728. Available from: https://mater-tehnol.si/index.php/MatTech/article/view/982
Section
Articles
