Detection of defects on weld bead through the wavelet analysis of the acquired arc sound signal

M.F.M. Yusof; M.A. Kamaruzaman; M. Zubair; M. Ishak

doi:10.15282/jmes.10.2.2016.8.0192

Authors

M.F.M. Yusof Faculty of Mechanical Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia
M.A. Kamaruzaman Faculty of Mechanical Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia
M. Zubair Faculty of Mechanical Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia
M. Ishak Faculty of Mechanical Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang, Malaysia

DOI:

https://doi.org/10.15282/jmes.10.2.2016.8.0192

Keywords:

MIG welding, sound signal, discrete wavelet transform.

Abstract

Recently, the development of online quality monitoring system based on the arc sound signal has become one of the main interests due its ability to provide the non-contact measurement. Notwithstanding, numerous unrelated-to-defect sources which influence the sound generation are one of the aspects that increase the difficulties of applying this method to detect the defect during welding process. This work aims to reveal the hidden information that associates with the existence of irregularities and porosity on the weld bead from the acquired arc sound by applying the discrete wavelet transform. To achieve the aim, the arc sound signal was captured during the metal inert gas (MIG) welding process of three API 5L X70 steel specimens. Prior to the signal acquisition process, the frequency range was set from 20 Hz to 10 000 Hz which is in audible range. In the next stage, a discrete wavelet transform was applied to the acquired sound in order to reveal the hidden information associated with the occurrence of discontinuity and porosity. According to the results, it was clear that the acquired arc sound was not giving an obvious indication of the presence of defect as well as its location due to the high noise level. More interesting findings have been obtained when the discrete wavelet transform (DWT) analysis was applied. The analysis results indicate that the level 8 of the approximate and detail wavelet coefficient have given a significant sign associated with the presence of irregularities and porosity respectively. Moreover, despite giving the information on the surfaces pores, the detail wavelet coefficient was found to give a clear indication of the sub-surface porosity formation during welding process. Hence, it could be concluded that the hidden information with respect to the occurrence of discontinuity and porosity on the weld bead could be obtained by applying the discrete wavelet transform.

References

Asl HM, Vatani A. Numerical analysis of the burn-through at in-service welding of 316 stainless steel pipeline. International Journal of Pressure Vessels and Piping. 2013;105–106:49-59.

Jha AK, Manwatkar SK, Narayanan PR, Pant B, Sharma SC, George KM. Failure analysis of a high strength low alloy 0.15C–1.25Cr–1Mo–0.25V steel pressure vessel. Case Studies in Engineering Failure Analysis. 2013;1:265-72.

Hval M, Lamvik T. Parameters affecting the weld defect acceptance criteria of clad submarine pipelines installed by S-lay or reel-lay. Engineering Failure Analysis. 2015; 58(2):394-406.

Charde N. Characterization of spot weld growth on dissimilar joints with different thickness. Journal of Mechanical Engineering and Sciences. 2012;2:172-80.

Ishak M, Nordin NFM, Razali ASK, Shah LHA, Romlay FRM. Effect of filler on weld metal structure of AA6061 alluminium alloy by tungsten inert gas welding. International Journal of Automotive and Mechanical Engineering. 2015;11:2438 - 46.

Razak NAA, Ng SS. Investigation of effects of MIG welding on corrosion behaviour of AISI 1010 carbon steel. Journal of Mechanical Engineering and Sciences. 2014;7:1168-78.

Sathari NAA, Razali AR, Ishak M, Shah LH. Mechanical strength of dissimilar AA7075 and AA6061 alluminium alloy using friction stir welding. International Journal Of Automotive and Mechanical Engineering. 2015;11:2713-21.

Shah LH, Mohammad UK, Yaakob KI, Razali AR, Ishak M. Lap joint dissimilar welding of alluminium AA6061 and galvanized iron using TIG welding. Journal of Mechanical Engineering and Sciences. 2016;10:1817-26.

Wor LC, Rahman MM. Stress behavior of tailor welded blanks for dissimilar metal using finite element method. International Journal of Automotive and Mechanical Engineering. 2015;11:2541 - 54.

Carlson M, Johnson JA, Kunerth DC. Control of GMAW: Detection of Discontinuities in the Weld Pool. Welding Research Supplement. 1990:256 - 63.

Ulbrich D, Kowalczyk J, Jósko M, Selech J. The analysis of spot welding joints of steel sheets with closed profile by ultrasonic method. Case Studies in Nondestructive Testing and Evaluation. 2015;4:8-14.

Bentley PG, Dawson DG, Prine DW. An evaluation of acoustic emission for the detection of defects produced during fusion welding of mild and stainless steels. NDT International. 1982;15:243-9.

Alaknanda, Anand RS, Kumar P. Flaw detection in radiographic weld images using morphological approach. NDT & E International. 2006;39:29-33.

Kasban H, Zahran O, Arafa H, El-Kordy M, Elaraby SMS, Abd El-Samie FE. Welding defect detection from radiography images with a cepstral approach. NDT & E International. 2011;44:226-31.

Valavanis I, Kosmopoulos D. Multiclass defect detection and classification in weld radiographic images using geometric and texture features. Expert Systems with Applications. 2010;37:7606-14.

Vilar R, Zapata J, Ruiz R. An automatic system of classification of weld defects in radiographic images. NDT & E International. 2009;42:467-76.

Yu H, Xu Y, Song J, Pu J, Zhao X, Yao G. On-line monitor of hydrogen porosity based on arc spectral information in Al–Mg alloy pulsed gas tungsten arc welding. Optics & Laser Technology. 2015;70:30-8.

Zhang Z, Kannatey-Asibu E, Jr., Chen S, Huang Y, Xu Y. Online defect detection of Al alloy in arc welding based on feature extraction of arc spectroscopy signal. Int J Adv Manuf Technol. 2015;79:2067-77.

You DY, Gao XD, Katayama S. Review of laser welding monitoring. Science and Technology of Welding and Joining. 2014;19:181-201.

Saad E, Wang H, Kovacevic R. Classification of molten pool modes in variable polarity plasma arc welding based on acoustic signature. Journal of Materials Processing Technology. 2006;174:127-36.

Wang Y, Chen Q, Sun Z, Sun J. Relationship between sound signal and weld pool status in plasma arc welding. Transaction Nonferrous Metal Society of China. 2001;11:4.

Wang Y, Zhao P. Noncontact acoustic analysis monitoring of plasma arc welding. International Journal of Pressure Vessels and Piping. 2001;78:43-7.

Grad L, Grum J, Polajnar I, Marko Slabe J. Feasibility study of acoustic signals for on-line monitoring in short circuit gas metal arc welding. International Journal of Machine Tools and Manufacture. 2004;44:555-61.

Luo H, Zeng H, Hu L, Hu X, Zhou Z. Application of Artificial Neural Network in Laser Welding Defect Diagnosis. Journal Of Material Processing Technology. 2005;170:403-11.

Luo Z, Liu W, Wang Z, Ao S. Monitoring of laser welding using source localization and tracking processing by microphone array. Int J Adv Manuf Technol. 2015:1-8.

Prabhakar S, Mohanty AR, Sekhar AS. Application of Discrete Wavelent Transform for Detection of Ball Bearing Race Faults Tribology International. 2002:793-800.

McFadden PD, Smith JD. Model for the Vibration Produced by A Single Point Defect in A Rolling Element Bearing. Journal of Sound and Vibration 1984 96:69-82.

Mallat SG. A theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Transaction on Pattern Analysis and Machine Intelligence. 1989 11 674-93.

Mendez PF, Eagar TW. Penetration and Defect Formation in High-current Arc Welding. Welding Research Supplement. 2003:296-306.

Jr. RWM. Principle of Welding: Process, Physics, Chemistry and Metallurgy. Singapore: Wiley VCH Verlag GmbH & Co.; 2004.