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Automatic Recognition of Truck Chassis Welding Defects Using Texture Features and Artificial Neural Networks
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 2, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Welding is an excellent attachment or repair method. The advanced industries such as oil, automotive industries, and other important industries need to rely on reliable welding operations; collapse because of this welding may lead to an excessive cost in money and risk in human life. In the present research, an automatic system has been described to detect, recognize and classify welding defects in radiographic images. Such system uses a texture feature and neural network techniques. Image processing techniques were implemented to help in the image array of weld images and the detection of weld defects. Therefore, a proposed program was build in-house to automatically classify and recognize eleven types of welding defects met in practice. The proposed system is tested and verified on eleven welding defects as follows; center line crack, elongated slag lines, cap undercut, lack of interpass fusion, lack of side wall fusion, lack of root penetration, misalignment, root undercut, root crack, root pass aligned, and transverse crack. It was found that only two welding defects are failed in a total 3 from 308 images in the overall recognition. The lowest classification percent was found in case of lack of side wall fusion defect (92.9%). The overall average discrimination rate results from a combined technique of texture feature and neural network are about 99%.
CitationAl-Ghamdi, S., Emam, A., and Abouelatta, O., "Automatic Recognition of Truck Chassis Welding Defects Using Texture Features and Artificial Neural Networks," SAE Technical Paper 2019-01-1119, 2019, https://doi.org/10.4271/2019-01-1119.
Data Sets - Support Documents
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- Mohr, G.A. and Fock, T., “X-ray Inspection in the Aerospace Industry - State of the Art, Challenges, and Emerging Technologies,” Proc Sixt World Conf Nondestruct Testing, Can Inst NDE 2-3, 2004.
- Kodama, S and Ishida, Y., “Arc Welding Technology for Automotive Steel Sheets,” 2013.
- Abdulhadi, A. and Ahtaiba, A., “Estimation of the Quality of Spot Welding Electrode Tips in Automotive Industry in Real Time Using Digital Image Processing and Image Segmentation Techniques,” Recent Adv Electr Eng Eval 9:181-191, 2015.
- Mirapeix, J., Cobo, A., Conde, O., Madruga, F.J. et al., “Non-invasive Spectroscopic System for Non-destructive Arc-Welding Analysis,” ECNDT1-7, 2006.
- Timm, F., Martinetz, T., and Barth, E., “Optical Inspection of Welding Seams,” Commun Comput Inf Sci 68:269-282, 2010, doi:10.1007/978-3-642-11840-1_20.
- Węgrzyn, T., Miros, M., Hadryś, D., and da Silva, A.M.P., “Truck Frame Welding Reparation by Steel Covered,” Transp Probl an Int Sci J 5:87-94, 2010.
- Bergmann, R.B., Bessler, F.T., and Bauer, W.. “Non-Destructive Testing in the Automotive Supply Industry- Requirements, Trends and Examples Using X-ray CT,” in Proc ECNDT 2006 Conf, 2006, 1-10.
- Ditchburn, R.J., Burke, S.K., and Scala, C.M., “NDT of Welds: State of the Art,” NDT E Int 29:111-117, 1996, doi:10.1016/0963-8695(96)00010-2.
- Margrave, F.W., Rigas, K., Bradley, D.A., and Barrowcliffe, P., “The Use of Neural Networks in Ultrasonic Flaw Detection,” Measurement 25:143-154, 1999, doi:10.1016/S0263-2241(98)00075-X.
- Kasban, H., Zahran, O., Arafa, H., El-Kordy, M. et al., “Welding Defect Detection from Radiography Images with a Cepstral Approach,” NDT E Int 44:226-231, 2011, doi:10.1016/j.ndteint.2010.10.005.
- Liu, Y.P., Sun, X., and Wen, Z.H., “The Application of MATLAB in Welding Image Processing,” Appl Mech Mater 201-202:300-303, 2012, doi:10.4028/www.scientific.net/AMM.201-202.300.
- Liao, T.W. and Ni, J., “An Automated Radiographic NDT System for Weld Inspection: Part I - Weld Extraction,” NDT E Int J 29:157-162, 1996.
- Liao, T.W. and Li, Y., “An Automated Radiographic NDT System for Weld Inspection: Part II - Flaw Detection,” NDT E Int 31:183-192, 1998.
- Wang, G. and Liao, T.W., “Automatic Identification of Different Types of Welding Defects in Radiographic Images,” NDT E Int 35:519-528, 2002. http://dx.doi.org/10.1016/S0963-8695(02)00025-7.
- Liao, T.W., “Classification of Welding Flaw Types with Fuzzy Expert Systems,” Expert Syst Appl 25:101-111, 2003, doi:10.1016/S0957-4174(03)00010-1.
- Liao, T.W., “Improving the Accuracy of Computer-Aided Radiographic Weld Inspection by Feature Selection,” NDT E Int 42:229-239, 2009, doi:10.1016/j.ndteint.2008.11.002.
- Vilar, R., Zapata, J., and R n, R., “An Automatic System of Classification of Weld Defects in Radiographic Images,” NDT E Int J 42:467-476, 2009, doi:10.1016/j.ndteint.2009.02.004.
- Zapata, J., Vilar, R., and Ruiz, R., “Automatic Inspection System of Welding Radiographic Images Based on ANN Under a Regularisation Process,” J Nondestruct Eval 31:34-45, 2012, doi:10.1007/s10921-011-0118-4.
- Shafeek, H.I., Gadelmawla, E.S., Abdel-Shafy, A.A., and Elewa, I.M., “Assessment of Welding Defects for Gas Pipeline Radiographs Using Computer Vision,” NDT E Int 37:291-299, 2004, doi:10.1016/j.ndteint.2003.10.003.
- Wang, Y., Sun, Y., Lv, P., and Wang, H., “Detection of Line Weld Defects Based on Multiple Thresholds and Support Vector Machine,” NDT E Int 41:517-524, 2008, doi:10.1016/j.ndteint.2008.05.004.
- Da Silva, R.R., Calôba, L.P., Siqueira, M.H.S., and Rebello, J.M.A., “Pattern Recognition of Weld Defects Detected by Radiographic Test,” NDT E Int 37:461-470, 2004, doi:10.1016/j.ndteint.2003.12.004.
- Haralick, R., Shanmugan, K., and Dinstein, I., “Textural Features for Image Classification,” IEEE Trans Syst Man Cybern 3:610-621, 1973, doi:10.1109/TSMC.1973.4309314.
- Chantler, M.J., “The Effect of Variation in Illuminant Direction on Texture Classification,” 1994.
- Thakare, V.S., Patil, N.N., and Sonawane, J.S., “Survey on Image Texture Classification Techniques,” Int J Adv Technol 4:97-104, 2013.
- Gadelmawla, E.S., Eladawi, A.E., Abouelatta, O.B., and Elewa, I.M., “Investigation of the Cutting Conditions in Milling Operations Using Image Texture Features,” Proc Inst Mech Eng Part B J Eng Manuf 222, 2008, doi:10.1243/09544054JEM1173.
- Abouelatta, O.B., “Classification of Copper Alloys Microstructure Using Image Processing and Neural Network,” J Am Sci 9:213-223, 2013.
- NDE, “Radiograph Interpretation - Welds,” 2016, 1-7, https://www.nde-ed.org/EducationResources/CommunityCollege/Radiography/TechCalibrations/RadiographInterp.php/11/2015.
- Al-Ghamdi, S.A., Emam, A.S., and Abouelatta, O.B., “Automatic Classification of Welding Defects Using Neural Network and Image Processing Techniques,” J Basic Appl Sci 1:17-25, 2017.
- Alaknanda, Anand, R.S., and Kumar, P., “Flaw Detection in Radiographic Weld Images Using Morphological Approach,” NDT E Int 39:29-33, 2006. http://dx.doi.org/10.1016/j.ndteint.2005.05.005.
- Alaknanda, Anand, R.S., and Kumar, P., “Flaw Detection in Radiographic Weldment Images Using Morphological Watershed Segmentation Technique,” NDT E Int 42:2-8, 2009, doi:10.1016/j.ndteint.2008.06.005.
- Aoki, K. and Suga, Y., “Application of Artificial Neural Network to Discrimination of Defect Type in Automatic Radiographic Testing of Welds,” ISIJ Int 39:1081-1087, 1999, doi:10.2355/isijinternational.39.1081.
- Jacobsen, C. and Nockemamt, C., “Crack Detection in Digitized Radiographs with Neuronal Methods,” Materialprufung 40:335-341, 1998.
- Nafaa, N, Redouane, D, and Amar, B., “Weld Defect Extraction and Classification in Radiographic Testing Based Artificial Neural Networks,” in Proc 15th World Conf Nondestruct Test, 2000, 1-7.
- Li, Y.F. and Liao, T.W., “Weld Defect Detection Based on Gaussian Curve,” Southeast Symp Syst Theory227-231, 1996.
- da Silva, R.R. and Mery, D., “The State of the Art of Weld Seam Radiographic Testing: Part I, Image Processing,” Mater Eval 65:643-647, 2007.
- Boaretto, N. and Centeno, T.M., “Automated Detection of Welding Defects in Pipelines from Radiographic Images DWDI,” NDT E Int 86:7-13, 2017, doi:10.1016/j.ndteint.2016.11.003.
- Zapata, J., Vilar, R., and Ruiz, R., “An Adaptive-Network-Based Fuzzy Inference System for Classification of Welding Defects,” NDT E Int 43:191-199, 2010, doi:10.1016/j.ndteint.2009.11.002.
- Carrasco, M. and Mery, D., “Segmentation of Welding Defects Using a Robust Algorithm,” Mater Eval 62:1142-1147, 2004.
- Mery, D. and Filbert, D., “Automated Flaw Detection in Aluminum Castings Based on the Tracking of Potential Defects in a Radioscopic Image Sequence,” IEEE Trans Robot Autom 18:890-901, 2002, doi:10.1109/TRA.2002.805646.