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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Online Access: | http://umpir.ump.edu.my/id/eprint/14543/1/Porosity%20detection%20by%20analyzing%20arc%20sound%20signal%20acquired%20during%20the%20welding%20process%20of%20gas%20pipeline%20steel.pdf http://umpir.ump.edu.my/id/eprint/14543/7/fkm-2016-mishak-Porosity%20Detection%20by%20Analyzing%20Arc%20Sound%20Signal.pdf http://umpir.ump.edu.my/id/eprint/14543/ http://dx.doi.org/10.1007/s00170-016-9343-4 |
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my.ump.umpir.145432017-08-23T04:19:33Z http://umpir.ump.edu.my/id/eprint/14543/ Porosity Detection by Analyzing arc Sound Signal Acquired During the Welding Process of Gas Pipeline Steel Mohd Fadhlan, Mohd Yusof Muhammad Aizat, Kamaruzaman M., Ishak M. F., Ghazali TJ Mechanical engineering and machinery 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. Springer-Verlag 2016 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/14543/1/Porosity%20detection%20by%20analyzing%20arc%20sound%20signal%20acquired%20during%20the%20welding%20process%20of%20gas%20pipeline%20steel.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/14543/7/fkm-2016-mishak-Porosity%20Detection%20by%20Analyzing%20Arc%20Sound%20Signal.pdf Mohd Fadhlan, Mohd Yusof and Muhammad Aizat, Kamaruzaman and M., Ishak and M. F., Ghazali (2016) Porosity Detection by Analyzing arc Sound Signal Acquired During the Welding Process of Gas Pipeline Steel. The International Journal of Advanced Manufacturing Technology. pp. 1-10. ISSN 0268-3768 (Print), 1433-3015 (Online) http://dx.doi.org/10.1007/s00170-016-9343-4 DOI: 10.1007/s00170-016-9343-4 |
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TJ Mechanical engineering and machinery Mohd Fadhlan, Mohd Yusof Muhammad Aizat, Kamaruzaman M., Ishak M. F., Ghazali Porosity Detection by Analyzing arc Sound Signal Acquired During the Welding Process of Gas Pipeline Steel |
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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. |
format |
Article |
author |
Mohd Fadhlan, Mohd Yusof Muhammad Aizat, Kamaruzaman M., Ishak M. F., Ghazali |
author_facet |
Mohd Fadhlan, Mohd Yusof Muhammad Aizat, Kamaruzaman M., Ishak M. F., Ghazali |
author_sort |
Mohd Fadhlan, Mohd Yusof |
title |
Porosity Detection by Analyzing arc Sound Signal Acquired During the Welding Process of Gas Pipeline Steel |
title_short |
Porosity Detection by Analyzing arc Sound Signal Acquired During the Welding Process of Gas Pipeline Steel |
title_full |
Porosity Detection by Analyzing arc Sound Signal Acquired During the Welding Process of Gas Pipeline Steel |
title_fullStr |
Porosity Detection by Analyzing arc Sound Signal Acquired During the Welding Process of Gas Pipeline Steel |
title_full_unstemmed |
Porosity Detection by Analyzing arc Sound Signal Acquired During the Welding Process of Gas Pipeline Steel |
title_sort |
porosity detection by analyzing arc sound signal acquired during the welding process of gas pipeline steel |
publisher |
Springer-Verlag |
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2016 |
url |
http://umpir.ump.edu.my/id/eprint/14543/1/Porosity%20detection%20by%20analyzing%20arc%20sound%20signal%20acquired%20during%20the%20welding%20process%20of%20gas%20pipeline%20steel.pdf http://umpir.ump.edu.my/id/eprint/14543/7/fkm-2016-mishak-Porosity%20Detection%20by%20Analyzing%20Arc%20Sound%20Signal.pdf http://umpir.ump.edu.my/id/eprint/14543/ http://dx.doi.org/10.1007/s00170-016-9343-4 |
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13.18916 |