Porosity detection by analyzing arc sound signal acquired during the welding process of gas pipeline steel

Detection of severe defects such as porosity underneath the weld bead during the welding process is vital during installation of a gas pipeline network because such defects might lead to fatigue crack. In this study, the work associated with detection of porosity through analysis of the acquired arc...

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Bibliographic Details
Published inInternational journal of advanced manufacturing technology Vol. 89; no. 9-12; pp. 3661 - 3670
Main Authors Yusof, M. F. M., Kamaruzaman, M. A., Ishak, M., Ghazali, M. F.
Format Journal Article
LanguageEnglish
Published London Springer London 01.04.2017
Springer Nature B.V
Subjects
CAE) and Design
Computer-Aided Engineering (CAD
Crack propagation
Defects
Empirical analysis
Engineering
Fatigue failure
Gas metal arc welding
Gas pipelines
Gas welding
Industrial and Production Engineering
Inert gas welding
Mechanical Engineering
Media Management
Natural gas
On-line systems
Original Article
Pipelines
Porosity
Rare gases
Signal processing
Sound
Spectrum analysis
Structural steels
Weld defects
Metal-inert gas welding
Arc sound
Porosity
Hilbert-Huang transform
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Summary:Detection of severe defects such as porosity underneath the weld bead during the welding process is vital during installation of a gas pipeline network because such defects might lead to fatigue crack. In this study, the work associated with detection of porosity through analysis of the acquired arc sound is presented. Air-borne acoustic signal was acquired during the metal inert gas welding process on API 5L X70 gas pipeline steel. Then, the acquired signal was analyzed using Hilbert Huang transform (HHT), which uses empirical mode decomposition for the purpose of filtering unrelated-to-damage signal components, and Hilbert spectral analysis to obtain the energy-frequency-distance plot. Results showed a significant energy amplitude pattern at the region where both surface- and subsurface-pores existed. Thus, the application of HHT analysis to the acquired arc sound signal has significantly assisted in identifying hidden information that is related to the existence of defects. This finding would enhance the development of an online welding defect detection system during the welding process.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-016-9343-4