Weld defect identification in friction stir welding through optimized wavelet transformation of signals and validation through X-ray micro-CT scan
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For control of real-time Internet of Things (IoT)-based remote welding process, continuous detection of defects occurring in the weld sample is of utmost importance so that welding parameters can be changed accordingly to avoid further occurrence of such defects. Time-frequency domain signal processing method, such as discrete wavelet transform (DWT), can be applied for detection of such defects. DWT continuously decomposes a signal into detailed and approximate coefficients through its associated filter banks and provides a time-frequency domain representation of a signal. Different levels of decomposition capture different frequency components, and hence, there is a need for optimization of the level of decomposition of force and power signals recorded during joining of two aluminum sheets by friction stir welding (FSW), for correct identification and localization of defects occurring in the process. Internal defects in the weld samples are further verified by CT scan images. Statistical tools have been used to study the variations in the DWT coefficients due to both internal and surface defects. An attempt has been made to compare between force and power signals as to which gives better defect detection.
KeywordsDiscrete wavelet transform Optimization Force and power signals Friction stir welding Internal and surface defects Micro-CT scan
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- 1.Walden S, Michael G, Temple-smith P (1995) 5:460–317Google Scholar
- 17.Jiménez-Macías E, Sánchez-Roca A, Carvajal-Fals H, Blanco-Fernández J, Martínez-Cámara E (2014) Wavelets application in prediction of friction stir welding parameters of alloy joints from vibroacoustic ANN-based model. Abstr Appl Anal 2014:1–11. https://doi.org/10.1155/2014/728564 CrossRefGoogle Scholar
- 21.Das B, Pal S, Bag S (2014) Monitoring of friction stir welding process through signals acquired during the welding. 1–7Google Scholar
- 22.Jose A, Babu AS, Kumar VSS (2013) Analysis of acoustic signals in friction stir welding. 161–164Google Scholar
- 28.Babajanzade Roshan S, Behboodi Jooibari M, Teimouri R, Asgharzadeh-Ahmadi G, Falahati-Naghibi M, Sohrabpoor H (2013) Optimization of friction stir welding process of AA7075 aluminum alloy to achieve desirable mechanical properties using ANFIS models and simulated annealing algorithm. Int J Adv Manuf Technol 69:1803–1818. https://doi.org/10.1007/s00170-013-5131-6 CrossRefGoogle Scholar
- 29.Kamal Babu K, Panneerselvam K, Sathiya P, Noorul Haq A, Sundarrajan S, Mastanaiah P, Srinivasa Murthy CV (2017) Parameter optimization of friction stir welding of cryorolled AA2219 alloy using artificial neural network modeling with genetic algorithm. Int J Adv Manuf Technol 94:3117–3129. https://doi.org/10.1007/s00170-017-0897-6 CrossRefGoogle Scholar
- 33.Adelson EH, Anderson CH, Bergen JR, Burt PJ, Ogden JM (1984) Pyramid methods in image processing. RCA Eng:33–41Google Scholar
- 35.Kumari S, Jain R, Kumar U, Yadav I, Ranjan N, Kumari K, Kesharwani RK, Kumar S, Pal S, Pal SK, Chakravarty D (2016) Defect identification in friction stir welding using continuous wavelet transform. J Intell Manuf 1–12. https://doi.org/10.1007/s10845-016-1259-1
- 39.Liu F, Liu S, Guo E, Li L (2008) Ultrasonic evaluation of friction stir welding. 17th World Conf Nondestruct. 25–28Google Scholar