Abstract
Incomplete penetration is a type of welding defect that severely impacts the quality of weldments. In order to identify penetration levels in pulsed laser and plasma transferred arc (laser-PTA) hybrid welding, this paper uses structure-borne acoustic sensors to detect acoustic signals. Their characteristics are then analyzed with respect to the time and frequency domains. Acoustic signals characteristic of incomplete-penetration defects were extracted using a Butterworth band-pass filter. Physical mechanisms of laser-excited acoustic wave were then studied by analyzing the correlation between incomplete-penetration defects and their characteristic acoustic signals. The results showed that acoustic signals correlating to incomplete-penetration defects have characteristic frequencies ranging from 0 to 10 kHz, which are generated by interaction between the pulsed laser beam and molten pool. An incomplete-penetration defect constitutes an acoustic cavity, which is an acoustic transmission structure. The structure of phonation sources and the acoustic cavity are affected by levels of penetration, giving rise to acoustic signals with different characteristics. In general, the study of physical mechanisms of laser-excited acoustic wave lays a foundation for on-line identification of incomplete-penetration defects in laser-PTA hybrid welding.
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References
Steen W (1980) Arc augmented laser processing of materials. J Appl Phys 51(11):5636–5641
Hu B, Den Ouden G (2013) Synergetic effects of hybrid laser/arc welding. Sci Technol Weld Join 10(4):427–431
Xiao R, Wu S (2008) Research progress in laser arc hybrid welding. China Laser 35(11):1680–1685
Bappa A (2018) Hybrid laser arc welding: state-of-art review. Opt Laser Technol 99:60–71
Morten K, Farhang F, Ewa K, Sigurd V (2017) Application of hybrid laser arc welding for the joining of large offshore steel foundations. Phys Procedia 89:197–204
Allen C, Verhaeghe G, Hilton P, Heason C, Prangnell P (2006) Laser and hybrid laser-MIG welding of 6.35 and 12.7mm thick aluminium aerospace alloy. Mater Sci Forum 519–521:1139–1144
Lu N, Zhong J, Chen H, Lin T, Chen S (2014) Real-time control of welding penetration during robotic GTAW dynamical process by audio sensing of arc length. Int J Adv Manuf Technol 74:235–249
Wang Y, Zhao P (2001) Noncontact acoustic analysis monitoring of plasma arc welding. Int J Press Vessels Pip 78:43–47
Liu L, Lan H, Zheng H (2010) Feature evaluation and selection of penetration arc sound signal based on neural network. Transactions of the China Welding Institution 31(3):25–28
Wu D, Huang Y, Chen H, He Y, Chen S (2017) VPPAW penetration monitoring based on fusion of visual and acoustic signals using t-SNE and DBN model. Mater Des 123:1–14
Liang R, Yu R, Luo Y, Zhang Y (2019) Machine learning of weld joint penetration from weld pool surface using support vector regression. J Manuf Process 41:23–28
Song S, Chen H, Lin T, Wu D, Chen S (2016) Penetration state recognition based on the double-sound-sources characteristic of VPPAW and hidden Markov Model. J Mater Process Technol 234:33–44
Lv N, Xu Y, Li S, Yu X, Chen S (2017) Automated control of welding penetration based on audio sensing technology. J Mater Process Technol 250:81–98
Wu D, Chen H, Huang Y, He Y, Hu M, Chen S (2017) Monitoring of weld joint penetration during variable polarity plasma arc welding based on the keyhole characteristics and PSO-ANFIS. J Mater Process Technol 239:113–124
Huang W, Kovacevic R (2011) A neural network and multiple regression method for the characterization of the depth of weld penetration in laser welding based on acoustic signatures. J Intell Manuf 22:131–143
Chen B, Wang J, Chen S (2010) Prediction of pulsed GTAW penetration status based on BP neural network and D-S evidence theory information fusion. Int J Adv Manuf Technol 48:83–94
Sumesh A, Rameshkumar K, Mohandas K, Shyam BR (2015) Use of machine learning algorithms for weld quality monitoring using acoustic signature. Procedia Comput Sci 50:316–322
Yusof MF, Ishak M, Ghazali MF (2020) Classification of weld penetration condition through synchrosqueezed-wavelet analysis of sound signal acquired from pulse mode laser welding process. J Mater Process Technol 279:116559
Zhu T, Shi Y, Cui S, Cui Y (2019) Recognition of weld penetration during K-TIG welding based on acoustic and visual sensing. Sensing and Imaging 20:3
Luo Y, Zhu L, Han JT, Xie XJ, Wan R, Zhu Y (2019) Study on the acoustic emission effect of plasma plume in pulsed laser welding. Mech Syst Signal Process 124:715–723
Zhao Y, Wang H, Zheng Y, Zhuang Y, Zhou N (2020) High sampling rate or high resolution in a sub-Nyquist sampling system. Measurement 166:108175
Funding
This work was supported by the Natural Science Foundation Project of Chongqing Science and Technology Bureau of China (Grant No. cstc2021jcyj-msxmX0189) and Science and Technology Research Program of Chongqing Municipal Education Commission of China (Grant No. KJZD-M202001102).
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Yuhua Cai performed the data analyses and wrote the manuscript. Yi Luo contributed to the conception of the study. Xinxin Wang contributed significantly to analysis and manuscript preparation. Shuqing Yang performed the experiment. Fuyuan Zhang helped perform the analysis with constructive discussions. Fanshun Tang performed the experiment. Yanrui Peng helped perform the analysis with constructive discussions.
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Cai, Y., Luo, Y., Wang, X. et al. Physical mechanism of laser-excited acoustic wave and its application in recognition of incomplete-penetration welding defect. Int J Adv Manuf Technol 120, 6091–6105 (2022). https://doi.org/10.1007/s00170-022-09143-x
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DOI: https://doi.org/10.1007/s00170-022-09143-x