Detection Of Defects On Weld Bead Through The Wavelet Analysis Of The Acquired Arc Sound Signal

• Main Authors: Mohd Fadhlan, Mohd Yusof, M. A., Kamaruzaman, M., Zubair, M., Ishak
• Format: Article
• Language: English
• Published: Faculty Mechanical Engineering, UMP 2016
• Subjects: TJ Mechanical engineering and machinery
• Online Access: http://umpir.ump.edu.my/id/eprint/16188/1/fkm-2016-8_yusof%20et%20al.pdf; http://umpir.ump.edu.my/id/eprint/16188/; http://jmes.ump.edu.my/images/Volume%2010%20Issue%202%20Sept%202016/8_yusof%20et%20al.pdf
• Institution: Universiti Malaysia Pahang Al-Sultan Abdullah

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