Implementation of vision system for defects classification method using international standard welding imperfection

Defects are ones of the problems that should be minimized by any manufacturing company. Defects occurrence will decrease the performance of a production system. Defects typically occur in many kinds of product especially in manufacturing field. This research proposes an automated inspection of weldi...

Full description

Saved in:

Bibliographic Details
Main Author:	Awang, Nurfadzylah
Format:	Thesis
Language:	English English
Published:	2018
Subjects:	T Technology (General) TS Manufactures
Online Access:	http://eprints.utem.edu.my/id/eprint/23333/1/Implementation%20Of%20Vision%20System%20For%20Defects%20Classification%20Method%20Using%20International%20Standard%20Welding%20Imperfection.pdf http://eprints.utem.edu.my/id/eprint/23333/2/Implementation%20of%20vision%20system%20for%20defects%20classification%20method%20using%20international%20standard%20welding%20imperfection.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utem-ep.23333
record_format	uketd_dc
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Md Fauadi, Muhammad Hafidz Fazli
topic	T Technology (General) TS Manufactures
spellingShingle	T Technology (General) TS Manufactures Awang, Nurfadzylah Implementation of vision system for defects classification method using international standard welding imperfection
description	Defects are ones of the problems that should be minimized by any manufacturing company. Defects occurrence will decrease the performance of a production system. Defects typically occur in many kinds of product especially in manufacturing field. This research proposes an automated inspection of welding defects particularly the surface defects that are normally inspected manually. In order to inspect the welding product, Metal Inert Gas (MIG) welding has been chosen to be tested using three types of gases which are Carbon Dioxide (CO2), Argon (Ar) and gas mixture (combination of CO2 and Ar). The selection of the three types of gases is because the three types are the most widely used gases in the industry. Currently,many of the companies in Malaysia are implementing manual visual inspection conducted by operators. As welding is a highly repetitive process, the operators will have high hazard exposure.In order to improve the working condition, vision system is proposed. This research proposes a classification of the defects method that involves with image enhancement, image filtering and image segmentation. The classification method is involve with decision tree. A proposed of decision tree is lead to the image determination. In image filtering method, an improved method is applied to get a better picture of the image. The peak signal to noise ratio (PSNR) and mean square error (MSE) is calculated. The result obtained shows that the system is capable to detect welding defects successfully. According to the result, percentage error rate for both situation of the noise shows that the result is less than 30%.In addition, it shows that the most of the result is same with the expertise. Thus, the system is success for the classification of welding defect.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Awang, Nurfadzylah
author_facet	Awang, Nurfadzylah
author_sort	Awang, Nurfadzylah
title	Implementation of vision system for defects classification method using international standard welding imperfection
title_short	Implementation of vision system for defects classification method using international standard welding imperfection
title_full	Implementation of vision system for defects classification method using international standard welding imperfection
title_fullStr	Implementation of vision system for defects classification method using international standard welding imperfection
title_full_unstemmed	Implementation of vision system for defects classification method using international standard welding imperfection
title_sort	implementation of vision system for defects classification method using international standard welding imperfection
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty Of Manufacturing Engineering
publishDate	2018
url	http://eprints.utem.edu.my/id/eprint/23333/1/Implementation%20Of%20Vision%20System%20For%20Defects%20Classification%20Method%20Using%20International%20Standard%20Welding%20Imperfection.pdf http://eprints.utem.edu.my/id/eprint/23333/2/Implementation%20of%20vision%20system%20for%20defects%20classification%20method%20using%20international%20standard%20welding%20imperfection.pdf
_version_	1747834037239545856
spelling	my-utem-ep.233332022-06-13T09:43:15Z Implementation of vision system for defects classification method using international standard welding imperfection 2018 Awang, Nurfadzylah T Technology (General) TS Manufactures Defects are ones of the problems that should be minimized by any manufacturing company. Defects occurrence will decrease the performance of a production system. Defects typically occur in many kinds of product especially in manufacturing field. This research proposes an automated inspection of welding defects particularly the surface defects that are normally inspected manually. In order to inspect the welding product, Metal Inert Gas (MIG) welding has been chosen to be tested using three types of gases which are Carbon Dioxide (CO2), Argon (Ar) and gas mixture (combination of CO2 and Ar). The selection of the three types of gases is because the three types are the most widely used gases in the industry. Currently,many of the companies in Malaysia are implementing manual visual inspection conducted by operators. As welding is a highly repetitive process, the operators will have high hazard exposure.In order to improve the working condition, vision system is proposed. This research proposes a classification of the defects method that involves with image enhancement, image filtering and image segmentation. The classification method is involve with decision tree. A proposed of decision tree is lead to the image determination. In image filtering method, an improved method is applied to get a better picture of the image. The peak signal to noise ratio (PSNR) and mean square error (MSE) is calculated. The result obtained shows that the system is capable to detect welding defects successfully. According to the result, percentage error rate for both situation of the noise shows that the result is less than 30%.In addition, it shows that the most of the result is same with the expertise. Thus, the system is success for the classification of welding defect. 2018 Thesis http://eprints.utem.edu.my/id/eprint/23333/ http://eprints.utem.edu.my/id/eprint/23333/1/Implementation%20Of%20Vision%20System%20For%20Defects%20Classification%20Method%20Using%20International%20Standard%20Welding%20Imperfection.pdf text en public http://eprints.utem.edu.my/id/eprint/23333/2/Implementation%20of%20vision%20system%20for%20defects%20classification%20method%20using%20international%20standard%20welding%20imperfection.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=112290 mphil masters Universiti Teknikal Malaysia Melaka Faculty Of Manufacturing Engineering Md Fauadi, Muhammad Hafidz Fazli 1. Acharya, U.R., Fujita, H., Sudarshan, V. K., Mookiah, M. R. K., Koh, J. E., Tan, J. H., Ng, K. H., 2016. An Integrated Index for Identification of Fatty Liver Disease Using Radon Transform and Discrete Cosine Transform Features in Ultrasound Images. Information Fusion Journal, 31, pp. 43-53. 2. Ahmed, F., and Das, S., 2014. Removal of High Density Salt-and-Pepper Noise in Images with an Iterative Adaptive Fuzzy Filter Using Alpha-Trimmed Mean. Proceedings of the 2013 IEEE Transactions on Fuzzy Systems, 22(5), pp. 1352-1358. 3. Ahmed, M. N., and Farag, A., 1997. Two-stage Neural Network for Volume Segmentation of Medical Images. Proceedings of International Conference on Neural Networks, 3, pp. 1143-1151. 4. Allodi, G., and Coisson, R., 2011. Coupled Waves in One –dimensional Quasi-periodic Structures, A Scilab Toolbox Project. Proceedings of the 2011 IEEE Conference on Opensource Software for Scientific Computation, pp. 22-26 5. Basak, R. K., Mukhopadhyay, B., Chatterjee, S., Goswami, S., Zaman, A., Ray, R., Nag, S., 2016. Hindu Arabic Character Recognition Using Mathematical Morphology. Proceedings of the 2016 7th IEEE Conference on Annual Ubiquitous Computing , Electronics and Mobile Communication, pp. 0-4.130 6. Becari, W., Ruiz, L., Evaristo, B. G. P., and Ramirez-Fernandez, F. J., 2016. Comparative Analysis of Classification Algorithms on Tactile Sensors. Proceedings of the 2016 IEEE International Symposium on Consumer Electronics, pp. 1-2. 7. Bordeianu, C. C., Felea, D., Besliu, C., Jipa, A., and Grossu, I.V., 2009. A New Version of Scilab Software Package for the Study of Dynamical Systems. Computer Physics Communication Journal, 180 (11), pp. 2398-2399. 8. Chen, Y., Chen, D. R., Yang, X., and Li, Y., 2010. Fast Two-Dimensional Otsu’s Thresholding Method Based on Integral Image. Proceedings of the 2010 on International Conference on Multimedia Technology, pp. 1-4. 9. Chen, Y., Huang, W., Zhang, D., and Chen, W., 2016.An Open Source Matlab Code Package for Improved Rank-Reduction 3D Seismic Data Denoising and Reconstruction. Computers and Geosciences Journal, 95, pp. 59-66. 10. Collette, R., King, J., Keiser, D., Miller, B., Madden, J., and Schulthess, J., 2016. Fission Gas Bubble Identification Using MATLAB’S Image Processing Toolbox. Materials Characterization Journal, 118, pp 284-293. 11. Daamouche, A., Boudebza, I.E., Touil, M., 2015. Classification of Hyperspectral Data Using Grey Model. Proceedings of the 2015 4th IEEE Conference on Electrical Engineering, Boumerdes, Algeria, 13-15 December 2015. IEEE Xplore Digital Library Publisher.131 12. Evans, J.R., 2014. Introduction to Six Sigma and Process Improvement, South Western : Thomson. 13. George, E. C., Barnett, R. J., Kristinn Andersen, James, F. A., and Alvin, M. S., 1995. Automated Visual Inspection and Interpretation System for Weld Quality Evaluation. Proceedings of the 1995 IEEE, pp. 1809-1816. 14. Gibson, B. T., Lammlein, D. H., Prater, T. J., Longhurst, W. R., Cox, C. D., Ballun, M. C., Strauss, A. M., 2014. Friction Stir Welding: Process, Automation, and Control. Journal of Manufacturing Processes, 16 (1), pp. 56-73. 15. Hongjun, C., Junyuan, L., Xiaohua, Z., and Zongquan, D., 2005. Application of Visual Servoing to an X-ray Based Welding Inspection Robot. Proceedings on the 2005 in International Conference on Control and Automation, pp. 977-982. 16. Hou, G., and Chen, A., 2010. A Mathematica Package for Solving Pseudo-Division Algorithm of Multivariate Polynomial Matrix and Its Application. Proceedings of the 2010 IEEE International Conference on Computational Intelligence and Software Engineering, pp. 1-4. 17. Hoyos, E., Lopez, D., and Alvarez, H., 2016. A Phenomenologically Based Material Flow Model for Friction Stir Welding. Journal of Materials and Design, 111, pp. 321-330132 International Standards, 1998. ISO 8785- Geometrical Product Specification (GPS)-Surface Imperfections- Terms, definitions, and parameters. 18. International Standard, 2007. EN ISO 6520-1: 2007-Welding and Allied ProcessesClassification of Geometric Imperfections in Metallic Materials. 19. Ivanovic, A., and Neilson, R. D., 2013. Non-destructive Testing of Rock Bolts for Estimating Total Bolt Length. International Journal of Rock Mechanics and Mining Sciences, 64, pp. 36-43. 20. Iyshwerya, K., Janani, B., Krithika, S., and Manikandan, T., 2013. Defect Detection Algorithm for High Speed Inspection in Machine Vision. Proceedings in International Conference on Smart Structures and Systems, pp. 103-107. 21. John, N., Surya, R., Ashwini, R., Sachin Kumar, S., and Soman, K. P., 2015. A Low Cost Implementation of Multi-label Classification Algorithm Using Mathematica on Raspberry 22. Pi. Journal of Procedia Computer Science, 46, pp. 306-313. 23. Johnson, J. P., 2010. Phyton for Collective Intelligence and Collaborative Filtering, pp. 1- 10 24. Jung, Y., Kim, J., Kumar, A., Feng, D. D., and Fulham, M., 2016. Efficient Visibility-driven Medical Image Visualisation via Adaptive Binned Visibility Histogram. Journal of Computerized Medical Imaging and Graphics, 51, pp. 40-49.133 25. Kim, S. M., Lee, Y. C., and Lee, S. C., 2006. Vision Based Automatic Inspection System for Nuts Welded on the Support Hinge. Proceedings on the 2006 of International Joint Conference, pp. 1508-1512. 26. Lokhande, N. M., 2015. A Matlab Based GUI for Multiframe Super Resolution Reconstruction of Different Gray Scale Images With and WithoutAdaptive Filter. Proceedings on International Conference on Computing Communication, Control and Automation, pp. 668-672. 27. Millman, K. J., and Aivazis, M., 2007. Python for Scientist and Engineers 28. Mulchrone, K.F., McCarthy, D. J., and Meere, P. A., 2013. Mathematica Code for Image Analysis, Semi-automatic Parameter Extraction and Strain Analysis. Journal of Computers and Geosciences, 61, pp. 64-70. 29. Nguyen, H. C., and Lee, B. R., 2014. Laser-Vision-based Quality Inspection System for Small Bead Laser Welding. International Journal of Precision engineering and Manufacturing, 15 (3), pp. 415-423. 30. Novotny, F. 2016. Particle Recognition, a Mathematica GUI Interface for Analysis of Complex Shaped Nanoparticles in Micrograph. Journal of Computer Physics Communications, pp. 1-7. 31. Pandit, D., Zhang, L., Aslam, N., Liu, C., and Chattopadhyay, S., 2016. Improved Abnormality Detection from Raw ECG Signals Using Feature Enhancement. Proceedings134 of the 2016 on 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, pp. 1402-1406. 32. PirahanSiah, F., Abdullah, S. N. H. S and Sahran, S., 2010. Adaptive Image Segmentation Based on Peak Signal-to-Noise Ratio for a License Plate Recognition System. Proceedings of 2010 on International Conference in Computer Applications and Industrial Electronics, pp. 468-472. 33. PirahanSiah, F., Abdullah, S. N. H. S., Shahnorbanun, and Sahran, 2013. Peak Signal-toNoise Ratio Based on Threshold for Image Segmentation. Journal of Theoretical and Applied Information Technology, 57 (2), pp. 158-168. 34. Priyadarshni, and Sohal, J. S., 2016. Improvement of Artificial Neural Network Based Character Recognition System, Using Scilab. Journal of Optik, 127 (22), pp. 10510-10518. 35. Quraishi, I., and Choudhury, J. P., 2012. Image Recognition and Processing Using Artificial Neural Network. Proceedings of the 2012 IEEE 1st International Conference on Recent Advances in Information Technology 36. Rosado, L. S., Santos, T. G., Piedade, M., Ramos, P. M., and Vilaca, P., 2010. Advanced Technique for Non-destructive Testing of Friction Stir Welding of Metals. Journal of the International Measurement Confederation, 43 (8), pp. 1021-1030.135 37. Sahlol, A. T., Zawban, H. M., Hassanien, A. E., Suen, C. Y., and Elfattah, M. A., 2016. Bioinspired BAT Optimization Algorithm for Handwritten Arabic Characters Recognition. Proceedings of the 2016 IEEE International Conference on Evolutionary Computation, pp. 1749-1756. 38. Sardari, S., and Eftekhari, M., 2016. A Fuzzy Decision Tree Approach for Imbalanced Data Classification. Proceedings on the 2016 IEEE 6th International Conference on Computer and Knowledge Engineering, pp. 292-297. 39. Senthil Kumar, G., Natarajan, U., and Ananthan S. S., 2012. Vision Inspection System for the Identification and Classification of Defects in MIG Welding Joints. International Journal of Advanced Manufactruing Technology, 61 (9-12), pp. 923-933. 40. Serope Kalpakjian, S. R. S., 2006. Fusion Welding Processes. In Manufacturing Engineering and Technology, 5th ed, pp. 940-941, Singapore : Pearson Prentice Hall. 41. Shih, F. Y., 2010. Image Processing and Pattern Recognition ( Fundamentals and Techniques), pp. 119-175, IEEE Press & Wiley & Sons. 42. Singh, A., and Pandey, B., 2016. Liver Disorder Diagnosis Using Linear, Nonlinear and Decision Tree Classification Algorithms. International Journal of Engineering and Technology, 8(5), pp. 2059-2069.136 43. Talab, A. M. A., Huang, Z., Xi, F., and Haiming, L., 2016. Detection Crack in Image Using Otsu Method and Multiple Filtering in Image Processing Techniques. Journal of Optik, 127 (3), pp. 1030-1033. 44. Van De Ville, D., Nachtegael, M., Van der Weken, D., Kerre, E. E., Philips, W., and Lemahieu, I., 2003. Noise Reduction by Fuzzy Image Filtering. Proceedings of the 2003 IEEE Transactions on Fuzzy Systems, 11, pp. 429-436. 45. Vani, K. S., and Kumar, K. P., 2016. Protein Fold Identification Using machine Learning Methods on Contact Maps. Proceedings on the 2016 IEEE 46. Wang, D., 1997. A Multiscale Gradient Algorithm for Image Segmentation using Watersheds. Journal of Pattern Recognition, 30 (12), pp. 2043-2052. 47. Wosiak, A., Zamecznik, A., and Niewiadomska-Jarosik, K., 2016. Supervised and Unsupervised Machine Learning for Improved Identification of Intrauterine Growth Restriction Types. Proceedings on the 2016 in Computer Science and Information Systems, 8, pp. 323-329. 48. Wu, S., Liang, J., and Ho, J., 2016. Head Pose Estimation and Its application in TV Viewers Behaviour Analysis. Proceedings of the 2016 IEEE Canadian Conference on Electrical and Computer Engineering, pp. 1-6.137 49. Wu, T., 2016. A Degraded Character of Printed Number Recognition Algorithm. Proceedings of the 2016 of 8th International Conference on Intelligent Human-Machine Syatems and Cybernetics, pp. 156-159. 50. Yuan Li, You Fu Li, Qing Lin, De Xu, and Min Tan, 2010. Measurement and Defect Detection of the Weld Bead Based on Online Vision Inspection. Proceedings of the 2010 IEEE Transaction on Instrumentation and Measurement, 59 (7), pp. 1841-1849. 51. Zhang, C., and Wang, K., 2015. A Switching Median-Mean Filter for Removal of High Density Impulse Noise from Digital Images. Journal of Optik, 126 (9-10), pp. 956-961. 52. Zhang, J. Z. J., and Hu, J. H. J., 2008. Image Segmentation Based on 2D Otsu Method with Histogram Analysis. Proceedings on the 2008 of International Conference on Computer Science and Software Engineering, 6 (1), pp. 105-108. 53. Zhao, B., 2011. Optimization Research of genetic Neural Network Based on Scilab. Proceedings on the 2011 IEEE Workshop on Open-source Software for Scientific Computation, pp. 114-117.

Implementation of vision system for defects classification method using international standard welding imperfection

Similar Items