| Citation: | YU Dongling, LIAO Xianqi, REN Haoyang, et al. Micro- and nano-scale feature extraction method for aviation silicon nitride turbine blades based on adaptive Perona-Malik enhancement and multi-scale Canny segmentation[J]. Journal of Aerospace Power, 2026, 41（5）：20250488 doi: 10.13224/j.cnki.jasp.20250488
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To address the issues of dense noise, blurred edges, and feature loss in images of aviation silicon nitride turbine blades with micro- and nano-scale features (5—15 μm), a coupled method based on adaptive Perona-Malik enhancement and multi-scale Canny segmentation was proposed. This approach enabled high-precision, low-loss extraction of aviation silicon nitride turbine blades with micro- and nano- scale features. By analyzing the gradient distribution and noise characteristics of feature images, an adaptive mechanism based on the median gradient and 90th percentile diffusion coefficient was constructed. A multi-scale pyramidal hierarchical strategy was designed to perform non-maximum suppression and dual-threshold segmentation at different scales. Finally, through weighted fusion, the results were restored to the original scale, achieving comprehensive extraction and refinement of multi-scale edges. The enhanced image structure similarity index (SSIM) reached
| [1] |
WU Yuhou, GUO Jiancheng, ZHANG Xiaochen, et al. Research on vibration characteristics of silicon nitride 6206 full-ceramic bearing with different pre-tightening force and oil supply rate[J]. The International Journal of Advanced Manufacturing Technology, 2023, 127(9): 4943-4957.
|
| [2] |
NAZIR M H, AHMAD KHAN Z, SAEED A. Experimental analysis and modelling of c-crack propagation in silicon nitride ball bearing element under rolling contact fatigue[J]. Tribology International, 2018, 126: 386-401. doi: 10.1016/j.triboint.2018.04.030
|
| [3] |
WANG Xiaolong, LI Songhua, FAN Shengqi, et al. Study on properties and tribological behaviors of silicon nitride-based Ti-DLC films[J]. Surface and Coatings Technology, 2025, 506: 132131. doi: 10.1016/j.surfcoat.2025.132131
|
| [4] |
MCGARRITY K, TUMURUGOTI P, NING Kaijie, et al. Fractography of silicon nitride based ceramics to guide process improvements[J]. Journal of the European Ceramic Society, 2020, 40(14): 4746-4752. doi: 10.1016/j.jeurceramsoc.2020.02.017
|
| [5] |
OHJI T, TATAMI J. Processing-structure-microscale properties of silicon nitride[J]. Ceramics International, 2024, 50(19): 37282-37290. doi: 10.1016/j.ceramint.2024.04.238
|
| [6] |
HEGEDÜS N, BALÁZSI K, BALÁZSI C. Silicon nitride and hydrogenated silicon nitride thin films: a review of fabrication methods and applications[J]. Materials, 2021, 14(19): 5658. doi: 10.3390/ma14195658
|
| [7] |
WEI Wanxin, LIANG Hanqin, SU Yunfeng, et al. Fretting damage behaviors of silicon nitride balls with different sintering aids and processes under grease lubrication[J]. Tribology International, 2022, 171: 107572. doi: 10.1016/j.triboint.2022.107572
|
| [8] |
ZOU Wanyan, TONG Yuanjian, WANG Yu, et al. Characteristics of compressive failure behavior of polyacrylonitrile-based carbon fiber multifilament[J]. Polymer Composites, 2024, 45(1): 924-932. doi: 10.1002/pc.27826
|
| [9] |
LEÓN-BECERRA J, HIDALGO-SALAZAR M Á, GONZÁLEZ-ESTRADA O A. Progressive damage analysis of carbon fiber-reinforced additive manufacturing composites[J]. The International Journal of Advanced Manufacturing Technology, 2023, 126(5): 2617-2631. doi: 10.1007/s00170-023-11256-w
|
| [10] |
YE C C, RU H Q, CHEN D L. Fatigue behavior of silicon nitride ceramics[J]. Ceramics International, 2023, 49(17): 28405-28414. doi: 10.1016/j.ceramint.2023.06.095
|
| [11] |
JIANG Yi, HU Kun, ZHANG Xin, et al. A saturation channel detection method for surface defects of silicon nitride bearing rollers based on adaptive gamma correction-edge threshold segmentation coupling algorithm[J]. Materials Today Communications, 2023, 36: 106397. doi: 10.1016/j.mtcomm.2023.106397
|
| [12] |
JIA Haiyuan, LIN Bin, LIU Zaiwei, et al. Laser-excited surface acoustic wave method for detecting subsurface damage of processed silicon nitride ceramics[J]. Ceramics International, 2024, 50(21): 42081-42091. doi: 10.1016/j.ceramint.2024.08.051
|
| [13] |
PU Yezhuang, ZHAO Yugang, ZHAO Guoyong, et al. Experimental study on laser assisted machining of silicon nitride ceramics based on acoustic emission detection[J]. Optics & Laser Technology, 2025, 180: 111497. doi: 10.1016/j.optlastec.2024.111497
|
| [14] |
HIGA E, SOMETANI M, HIRAI H, et al. Electrically detected magnetic resonance study on interface defects at nitrided Si-face, a-face, and m-face 4H-SiC/SiO2 interfaces[J]. Applied Physics Letters, 2020, 116(17): 171602. doi: 10.1063/5.0002944
|
| [15] |
ZWANENBURG E A, NORMAN D G, QIAN C, et al. Effective X-ray micro computed tomography imaging of carbon fibre composites[J]. Composites Part B: Engineering, 2023, 258: 110707. doi: 10.1016/j.compositesb.2023.110707
|
| [16] |
MA Mengyuan, WANG Zhongxin, GAO Zhihao, et al. Ultrasonic phased array testing and identification of multiple-type internal defects in carbon fiber reinforced plastics based on convolutional neural network[J]. Materials, 2025, 18(2): 318. doi: 10.3390/ma18020318
|
| [17] |
RASHIDI A, OLFATBAKHSH T, CRAWFORD B, et al. A review of current challenges and case study toward optimizing micro-computed X-ray tomography of carbon fabric composites[J]. Materials, 2020, 13(16): 3606. doi: 10.3390/ma13163606
|
| [18] |
YU Dongling, ZHANG Huiling, ZHANG Xiaohui, et al. Si3N4 ceramic ball surface defects’ detection based on SWT and nonlinear enhancement[J]. Mathematical Problems in Engineering, 2021, 2021(1): 4922315. doi: 10.1155/2021/4922315
|
| [19] |
YU Dongling, ZHU Zuoxiang, MIN Jianliang, et al. Multi-scale decomposition enhancement algorithm for surface defect images of Si3N4 ceramic bearing balls based on stationary wavelet transform[J]. Advances in Applied Ceramics, 2021, 120(1): 47-57. doi: 10.1080/17436753.2020.1858010
|
| [20] |
JIANG Yi, TANG Mengtao, DONG Wenjie, et al. Microcrack feature extraction method for chaotic optical surface of silicon nitride ceramic bearing roller based on multi-scale wavelet transform enhancement and optimized PSO-FCM coupling[J]. AIP Advances, 2025, 15: 015214. doi: 10.1063/5.0244948
|
| [21] |
WANG Nannan, ZHANG Yongxia. Adaptive and fast image superpixel segmentation approach[J]. Image and Vision Computing, 2021, 116: 104315. doi: 10.1016/j.imavis.2021.104315
|
| [22] |
LEI Tao, JIA Xiaohong, LIU Tongliang, et al. Adaptive morphological reconstruction for seeded image segmentation[J]. IEEE Transactions on Image Processing, 2019, 28(11): 5510-5523. doi: 10.1109/TIP.2019.2920514
|
| [23] |
HUANG J T, TING C H. Deep learning object detection applied to defect recognition of memory modules[J]. The International Journal of Advanced Manufacturing Technology, 2022, 121(11): 8433-8445. doi: 10.1007/s00170-022-09716-w
|
| [24] |
CHO C, LEE Y H, PARK J, et al. A self-spatial adaptive weighting based U-Net for image segmentation[J]. Electronics, 2021, 10(3): 348. doi: 10.3390/electronics10030348
|
| [25] |
CHEN Liping, GAO Jinhui, LOPES A M, et al. Adaptive fractional-order genetic-particle swarm optimization Otsu algorithm for image segmentation[J]. Applied Intelligence, 2023, 53(22): 26949-26966. doi: 10.1007/s10489-023-04969-8
|
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