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DETECTION OF SURFACE DEFECTS IN FRICTION STIR WELDED JOINTS BY USING A NOVEL MACHINE LEARNING APPROACH
Authors:
Akshansh Mishra1
, Saloni Bhatia Dutta2
1Center for Artificial Intelligence and Friction Stir Welding, Stir Research Technologies, India
2School of Electrical and Electronics Engineering, Gurgaon, India
Received: 19.12.2019.
Accepted: 19.02.2020.
Available: 31.03.2020.
Abstract:
The Friction stir welding process is a new entrant in welding technology. The FSW joints have high strength and helps in weight saving considerably than the other joining process as no filler material is added during welding. The weld quality is affected because of various kinds of defects occurring during the FSW process. Defects like cavity, surface grooves and flash could occur due to inappropriate set of process parameters which results in excessive or insufficient heat input.
Defects analysis can be done by several non-destructive methods like immersion ultrasonic techniques, X-ray radiography, thermography, eddy current testing, synchrotron technique etc. In the present work the image processing techniques are applied over the test samples to detect the surface defects like pin holes, surface grooves etc.
Keywords:
Friction Stir Welding, Machine Learning, Defects, Image Processing, Image Pyramid
References:
[1] R.S. Mishra, Z.Y. Ma, Friction stir welding and processing. Materials science and engineering: R: reports, 50(1-2), 2005: 1-78. https://doi.org/10.1016/j.mser.2005.07.001
[2] P. Vilaça, W. Thomas, Friction stir welding technology. In Structural Connections for Lightweight Metallic Structures. SpringerVerlag Berlin Heidelberg, 2011, pp. 85-124. https://doi.org/10.1007/978-3-642-18187-0
[3] S.W. Kallee, W.M. Thomas, E., Dave Nicholas, Friction stir welding of lightweight materials. Magnesium alloys and their applications, 2000: pp.173-190. https://doi.org/10.1002/3527607552.ch29
[4] V. Balasubramanian, Relationship between base metal properties and friction stir welding process parameters. Materials Science and Engineering: A, 480(1-2), 2008: 397-403. https://doi.org/10.1016/j.msea.2007.07.048
[5] P.L. Threadgill, Terminology in friction stir welding. Science and Technology of Welding and Joining, 12 (4), 2007: 357-360. https://doi.org/10.1179/174329307X197629
[6] J.W. Qian, J.L.Li, F. Sun, J.T. Xiong, F.S. Zhang, X. Lin, An analytical model to optimize rotation speed and travel speed of friction stir welding for defect-free joints. Scripta Materialia, 68 (3-4), 2013: 175-178.
https://doi.org/10.1016/j.scriptamat.2012.10.008
[7] D.J. Huggett, M.W. Dewan, M.A. Wahab, A. Okeil, T.W. Liao, Phased array ultrasonic testing for post-weld and online detection of friction stir welding defects. Research in Nondestructive Evaluation, 28(4), 2017: 187-210.
https://doi.org/10.1080/09349847.2016.1157660
[8] H.B. Chen, K. Yan, T. Lin, S.B. Chen, C.Y. Jiang, Y. Zhao, The investigation of typical welding defects for 5456 aluminum alloy friction stir welds. Materials Science and Engineering: A, 433(1-2), 2006: 64-69.
https://doi.org/10.1016/j.msea.2006.06.056
[9] U. Kumar, I. Yadav, S. Kumari, K. Kumari, N. Ranjan, R.K. Kesharwani, R. Jain, S., Pal, S. Kumar, D. Chakravarty, S.K. Pal, Defect identification in friction stir welding using discrete wavelet analysis. Advances in Engineering Software, 85, 2015: 43-50. https://doi.org/10.1016/j.advengsoft.2015.02.001
[10] S. Verma, M. Gupta, J.P. Misra, Performance evaluation of friction stir welding using machine learning approaches. MethodsX, 5, 2018: 1048-1058. https://doi.org/10.1016/j.mex.2018.09.002
[11] Y. Du, T. Mukherjee, T. DebRoy, Conditions for void formation in friction stir welding from machine learning. npj Computational Materials, 5(1), 2019: 1-8. https://doi.org/10.1038/s41524-019-0207-y
[12] T.A. Mathis, Predicting Hardness of Friction Stir Processed 304L Stainless Steel using a Finite Element Model and a Random Forest Algorithm, Ira A. Fulton College of Engineering and Technology; Mechanical Engineering, 2019, Theses and Dissertations, 7591. https://scholarsarchive.byu.edu/etd/7591
[13] Ersozlu, I. and Celik, S., Artificial Neural Network application to the friction welding of AISI 316 and Ck 45 steels. Kovove materialy, 57(3), 2019: 199-205. https://doi.org/10.4149/km_2019_3_199
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
Volume 9
Number 3
September 2024
Last Edition
Volume 9
Number 3
September 2024
How to Cite
A. Mishra, S.B. Dutta. (2020). Detection of Surface Defects in Friction Stir Welded Joints by Using a Novel Machine Learning Approach. Applied Engineering Letters. 5(1), 16–21.
https://doi.org/10.18485/aeletters.2020.5.1.3
More Citation Formats
Mishra, A., & Saloni Bhatia Dutta. (2020). Detection of Surface Defects in Friction Stir Welded Joints by Using a Novel Machine Learning Approach. Applied Engineering Letters. 5(1), 16–21. https://doi.org/10.18485/aeletters.2020.5.1.3
Mishra, Akshansh, and Saloni Bhatia Dutta. “Detection of Surface Defects in Friction Stir Welded Joints by Using a Novel Machine Learning Approach.” Applied Engineering Letters, vol. 5, no. 1, 2020, pp. 16–21, https://doi.org/10.18485/aeletters.2020.5.1.3.
Mishra, Akshansh, and Saloni Bhatia Dutta. 2020. “Detection of Surface Defects in Friction Stir Welded Joints by Using a Novel Machine Learning Approach.” Applied Engineering Letters 5 (1): 16–21. https://doi.org/10.18485/aeletters.2020.5.1.3.
Mishra, A. and Saloni Bhatia Dutta (2020). Detection of Surface Defects in Friction Stir Welded Joints by Using a Novel Machine Learning Approach. Applied Engineering Letters. 5(1), pp.16–21. doi: 10.18485/aeletters.2020.5.1.3.
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DETECTION OF SURFACE DEFECTS IN FRICTION STIR WELDED JOINTS BY USING A NOVEL MACHINE LEARNING APPROACH
Authors:
Akshansh Mishra1
, Saloni Bhatia Dutta2
1Center for Artificial Intelligence and Friction Stir Welding, Stir Research Technologies, India
2School of Electrical and Electronics Engineering, Gurgaon, India
Received: 19.12.2019.
Accepted: 19.02.2020.
Available: 31.03.2020.
Abstract:
The Friction stir welding process is a new entrant in welding technology. The FSW joints have high strength and helps in weight saving considerably than the other joining process as no filler material is added during welding. The weld quality is affected because of various kinds of defects occurring during the FSW process. Defects like cavity, surface grooves and flash could occur due to inappropriate set of process parameters which results in excessive or insufficient heat input.
Defects analysis can be done by several non-destructive methods like immersion ultrasonic techniques, X-ray radiography, thermography, eddy current testing, synchrotron technique etc. In the present work the image processing techniques are applied over the test samples to detect the surface defects like pin holes, surface grooves etc.
Keywords:
Friction Stir Welding, Machine Learning, Defects, Image Processing, Image Pyramid
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0)
Volume 9
Number 3
September 2024
Last Edition
Volume 9
Number 3
September 2024