[1] |
WANG C Y, GAO X D, MA N J, et al. Magneto-optical imaging detection of laser welding defects under multi-directional magnetic field excitation[J]. Laser Technology, 2020, 44(5): 592-599(in Chinese). |
[2] |
SU Sh X, YU Y L, FEI W, et al. Research of characteristics of weld formation of aluminum alloy by high power fiber laser welding[J]. Laser Technology, 2017, 41(3): 322-327(in Chinese). |
[3] |
LIU T Y, BAO J S, WANG J L, et al. Laser welding penetration state recognition method fused with timing information[J]. Chinese Journal of Lasers, 2021, 48(6): 0602119(in Chinese). doi: 10.3788/CJL202148.0602119 |
[4] |
CHEN Z Q, GAO X D, WANG Y, et al. Weldment back of weld width prediction based on neural network during high-power laser welding[J]. Transactions of The China Welding Institution, 2018, 39(11): 48-52(in Chinese). |
[5] |
HUANG Y, SHEN C, JI X R, et al. Correlation between gas-dyna-mic behaviour of a vapour plume and oscillation of keyhole size during laser welding of 5083 Al-alloy[J]. Journal of Materials Processing Technology, 2020, 283: 116721. doi: 10.1016/j.jmatprotec.2020.116721 |
[6] |
SUDER W, GANGULY S, WILLIAMS S, et al. Penetration and mixing of filler wire in hybrid laser welding[J]. Journal of Materials Processing Technology, 2021, 291: 117040. doi: 10.1016/j.jmatprotec.2020.117040 |
[7] |
FANG J F. Study on the mechanism of penetration mode for thin steel laser deep penetration welding[D]. Harbin: Harbin Institute of Technology, 2007: 41-42(in Chinese). |
[8] |
WANG J, WANG C M, MENG X X, et al. Study on the periodic oscillation of plasma/vapour induced during high power fibre laser penetration welding[J]. Optics and Laser Technology, 2011, 44(1): 67-70. |
[9] |
PANG S Y, CHEN X, SHAO X Y, et al. Dynamics of vapor plume in transient keyhole during laser welding of stainless steel: Local evaporation, plume swing and gas entrapment into porosity[J]. Optics and Lasers in Engineering, 2016, 82: 28-40. doi: 10.1016/j.optlaseng.2016.01.019 |
[10] |
VOLPP J. Impact of fume particles in the keyhole vapour[J]. Applied Physics, 2019, A125(1): 70-77. |
[11] |
LIU X F, JIA C B, WU C S, et al. Measurement of the keyhole entrance and topside weld pool geometries in keyhole plasma arc welding with dual CCD cameras[J]. Journal of Materials Processing Technology, 2017, 248: 39-48. doi: 10.1016/j.jmatprotec.2017.05.012 |
[12] |
ROOZBAHANI H, MARTTINEN P, SALMINEN A. Real-time monitoring of laser scribing process of CIGS solar panels utilizing high speed camera[J]. IEEE Photonics Technology Letters, 2018, 30(20): 1741-1744. doi: 10.1109/LPT.2018.2867274 |
[13] |
CAI W, WANG J Z, JIANG P, et al. Application of sensing techniques and artificial intelligence-based methods to laser welding real-time monitoring: Acritical review of recent literature[J]. Journal of Manufacturing Systems, 2020, 57: 1-18. doi: 10.1016/j.jmsy.2020.07.021 |
[14] |
WANG B C, HU S J, SUN L, et al. Intelligent welding system technologies: State-of-the-artreview and perspectives[J]. Journal of Manufacturing Systems, 2020, 56: 373-391. doi: 10.1016/j.jmsy.2020.06.020 |
[15] |
GAO X D, LI Z M, WANG L, et al. Detection of weld imperfection in high-power disk laser welding based on association analysis of multi-sensing features[J]. Optics and Laser Technology, 2019, 115: 306-315. doi: 10.1016/j.optlastec.2019.01.053 |
[16] |
MA G H, YU L S, YUAN H T, et al. A vision-based method for lap weld defects monitoring of galvanized steel sheets using convolutional neural network[J]. Journal of Manufacturing Processes, 2021, 64: 130-139. doi: 10.1016/j.jmapro.2020.12.067 |
[17] |
HUANG J F, XUE L, HUANG J Q, et al. GMAW penetration state prediction based on visual sensing[J]. Journal of Mechanical Engineering, 2019, 55(17): 41-47(in Chinese). doi: 10.3901/JME.2019.17.041 |
[18] |
ZHANG Y X, YOU D Y, GAO X D, et al. Online monitoring of welding status based on a DBN model during laser welding[J]. Engineering, 2019, 5(4): 169-185. |
[19] |
MA X, DU Zh H, CAI Y, et al. Research on improved median filtering algorithm fused with gradient information[J]. Transducer and Microsystem Technologies, 2021, 40(3): 48-51(in Chinese). |
[20] |
PRAKASH A, STORER J, FLORENCIO D, et al. RePr: Improved Training of convolutional filters[C]// IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2019: 10658-10667. |