1 |
LI D Z, CHRYSANTHOU A, PATEL I, et al. Self-piercing riveting-a review[J]. International Journal of Advanced Manufacturing Technology, 2017, 92: 1777-1824.
|
2 |
岁波, 都东, 常保华, 等. 轻型车身自冲铆连接技术的发展[J]. 汽车工程, 2006, 28(1): 85-89.
|
|
SUI B,DU D,CHANG B H,et al. A review on the development of self-piercing riveting technology for lightweight vehicle body[J]. Automotive Engineering, 2006, 28(1): 85-89.
|
3 |
李永兵, 马运五, 楼铭, 等. 轻量化多材料汽车车身连接技术进展[J]. 机械工程学报, 2016, 52(24): 1-23.
|
|
LI Y B, MA Y W, LOU M, et al. Advances in welding and joining processes of multi-material lightweight car body[J]. Journal of Mechanical Engineering, 2016, 52(24): 1-23.
|
4 |
CHERAGHI S H. Effect of variations in the riveting process on the quality of riveted joints[J]. The International Journal of Advanced Manufacturing Technology, 2008, 39(11): 1144-1155.
|
5 |
ECKSTEIN J, ROOS E, ROLL K, et al. Experimental and numerical investigations to extend the process limits in self-pierce riveting[J]. American Institute of Physics, 2007, 907(1): 279-286.
|
6 |
LIU F F, LIU S P, ZHANG Q L, et al. Quantitative non-destructive evaluation of drilling defects in SiCf/SiC composites using low-energy X-ray imaging technique[J]. NDT & E International, 2020, 116: 102364.
|
7 |
LE M, KIM J, KIM S, et al. B-scan ultrasonic testing of rivets in multilayer structures based on short-time Fourier transform analysis[J]. Measurement, 2018, 128: 495-503.
|
8 |
HE Y Z, LUO F L, PAN M C. Pulsed eddy current technique for defect detection in aircraft riveted structures[J]. NDT & E International, 2010, 43(2): 176-181.
|
9 |
JANOVEC M, SMETANA M, BUGAJ M. Eddy current array inspection of Zlin 142 fuselage riveted joints[J]. Transportation Research Procedia, 2019, 40: 279-286.
|
10 |
武新军, 张卿, 沈功田. 脉冲涡流无损检测技术综述[J]. 仪器仪表学报, 2016, 37(8): 1698-1712.
|
|
WU X J, ZHANG Q, SHEN G T. Review on advances in pulsed eddy current nondestructive testing technology[J]. Chinese Journal of Scientific Instrument, 2016, 37(8): 1698-1712.
|
11 |
胡祥超. 集成涡流无损检测系统设计与关键技术研究[D]. 长沙: 国防科学技术大学, 2012.
|
|
HU X C. Research on key technologies and design of integrated eddy current nondestructive testing system[D]. Changsha: National University of Defense Technology, 2012.
|
12 |
FU Y W, LEI M L, LI Z X, et al. Lift-off effect reduction based on the dynamic trajectories of the received-signal fast Fourier transform in pulsed eddy current testing[J]. NDT & E International, 2017(87): 85-92.
|
13 |
SHAO H D, JIANG H K, ZHANG H Z, et al. Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensing[J]. Mechanical Systems and Signal Processing, 2018, 100: 743-765.
|
14 |
YAN Hao, PENG Y M, SHANG W J, et al. Open-circuit fault diagnosis in voltage source inverter for motor drive by using deep neural network[J]. Engineering Applications of Artificial Intelligence, 2023, 120: 105866.
|
15 |
TERCAN H, MEISEN T. Machine learning and deep learning based predictive quality in manufacturing: a systematic review[J]. Journal of Intelligent Manufacturing, 2022, 33: 1879-1905.
|
16 |
XIE Q, LU D N, HUANG A Y, et al. RRCNet: rivet region classification network for rivet flush measurement based on 3-D point cloud[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 70: 1-12.
|
17 |
AMOSOV O S, AMOSOVA S G, IOCHKOV I O. Detection and recognition of manufacturing defects of rivet joints by their video images using deep neural networks[C]. International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon), 2019: 1-4.
|
18 |
AMOSOV O S, AMOSOVA S G, IOCHKOV I O. Defects detection and recognition in aviation riveted joints by using ultrasonic echo signals of non-destructive testing[J]. IFAC-PapersOnLine, 2021, 54(1): 484-489.
|
19 |
EBRAHIMKHANLOU A, DUBUC B, SALAMONE S. A generalizable deep learning framework for localizing and characterizing acoustic emission sources in riveted metallic panels[J]. Mechanical Systems and Signal Processing, 2019, 130: 248-272.
|
20 |
LEE H, GROSSE R, RANGANATH R, et al. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations[C]. Proceedings of the 26th Annual International Conference on Machine Learning, 2009: 609-616.
|
21 |
SALAKHUTDINOV R, HINTON G. Using deep belief nets to learn covariance kernels for gaussian processes[C]. Proceedings of Advances in Neural Information Processing Systems, 2008, 20: 1249-1256.
|
22 |
SHAO H D, JIANG H K, ZHANG H Z, et al. Electric locomotive bearing fault diagnosis using a novel convolutional deep belief network[J]. IEEE Transactions on Industrial Electronics, 2018, 65(3): 2727-2736.
|
23 |
HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780.
|