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Detection research of telecentric bright field imaging system based on multi-angle illumination in ultra-precision machining components

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Języki publikacji
EN
Abstrakty
EN
Due to the rapid development of industrial automation and intelligence, the performance requirements of the machine vision inspection system are increased, especially for the surface of components with different properties. Firstly, the detection of single-sided polished optical element surface based on coaxial incidence telecentric bright field imaging system is proposed, and the upper and lower surfaces with different properties are compared. Secondly, the gray value change of defect location is distinguished, and then different scratch defect information is extracted. Finally, the detection data of the sample is calculated, and the weak information extraction algorithm based on visual difference excitation and double discrete the Fourier Transform is proposed. The average diameter of the sample is 6109.50 pixels, the average nominal value is 101.60 mm, the scratch pixel length and width are 184 pixels and 7.23 pixels respectively, and the actual length and width are 3.06 mm and 0.12 mm, respectively. The experimental results show that the detection technology of weak defects on the surface of single-sided polished optical elements can realize the nondestructive automatic quantitative detection of sapphire substrate, and can provide theoretical reference and technical support for the development of intelligent industrial applications.
Czasopismo
Rocznik
Strony
art. no. 2025101
Opis fizyczny
Bibliogr. 28 poz., rys., tab.
Twórcy
autor
  • Moral Education Integrated Teaching and Research Training Department, Jilin Provincial Institute of Education, Changchun, 130000, China
Bibliografia
  • 1. Tang B, Chen L, Sun W, Lin Z. Review of surface defect detection of steel products based on machine vision. IET Image Processing. 2023;17(14):303-322. https://doi.org/10.1049/IPR2.12647.
  • 2. Singh SA, Desai KA. Automated surface defect detection framework using machine vision and convolutional neural networks. Journal of Intelligent Manufacturing. 2023;34(4):1995-2011. https://doi.org/10.1007/S10845-021-01878-W.
  • 3. Doshvarpassand S, Wang X, Zhao X. Sub-surface metal loss defect detection using cold thermography and dynamic reference reconstruction (DRR). Structural Health Monitoring 2022;21(2):354-369. https://doi.org/10.1177/1475921721999599.
  • 4. Xu L, Hu J. A method of defect depth recognition in active infrared thermography based on GRU networks. Applied Sciences. 2021;11(14):63-87. https://doi.org/10.3390/app11146387.
  • 5. Mao Y, Wang S, Yu S, Zhao J. Automatic image detection of multi-type surface defects on wind turbine blades based on cascade deep learning network. Intelligent Data Analysis. 2021;25(2):463-482. https://doi.org/10.3233/IDA-205143.
  • 6. Duan H, Wei J, Qi L, Wang X, Liu Y, Yao M. Longitudinal crack detection approach based on principal component analysis and support vector machine for slab continuous casting. Steel Research International. 2021;92(10):168-178. https://doi.org/10.1002/srin.202100168.
  • 7. Kang SH, Kang M, Kang LH. Defect detection on the curved surface of a wind turbine blade using piezoelectric flexible line sensors. Structural Health Monitoring. 2022;21(3):1207-1217. https://doi.org/10.1177/14759217211026192.
  • 8. Hua S, Li B, Shu L, Jiang P, Cheng S. Defect detection method using laser vision with model-based segmentation for laser brazing welds on car body surface. Measurement. 2021;178(1):1-13. https://doi.org/10.1016/j.measurement.2021.109370.
  • 9. Preeyanka N, Sarkar M. Probing how various metal ions interact with the surface of QDs: Implication of the interaction event on the photophysics of QDs. Langmuir. 2021;37(23):6995-7007. https://doi.org/10.1021/acs.langmuir.1c00548.
  • 10. Ai Y, Zhang Y, Cao X, Zhang W. A defect detection method for the surface of metal materials based on an adaptive ultrasound pulse excitation device and infrared thermal imaging technology. Complexity. 2021;2021(7):1-9. https://doi.org/10.1155/2021/8199013.
  • 11. Zhao H, Yang Z, Li J. Detection of metal surface defects based on YOLOv4 algorithm. Journal of Physics: Conference Series 2021; 1907(1): 012-043. https://doi.org/10.1088/1742-6596/1907/1/012043.
  • 12. Yu X, Wang K, Wang S. Research on image recognition of building wall design defects based on partial differential equation. Advances in Mathematical Physics. 2021;2021(3):1-10. https://doi.org/10.1155/2021/1229660.
  • 13. Aydin I, Akin E, Karakose M. Defect classification based on deep features for railway tracks in sustainable transportation. Applied Soft Computing. 2021;111(7): 107706-107720. https://doi.org/10.1016/j.asoc.2021.107706.
  • 14. Wang H, Gao C, Ling Y. A deep learning-based method for aluminum foil-surface defect recognition. International Journal of Information and Communication Technology. 2021;19(3):231-241. https://doi.org/10.1504/IJICT.2021.117532.
  • 15. Zhuang J, Peng Q, Wu F, Guo B. Multi-component attention-based convolution network for color difference recognition with wavelet entropy strategy. Advanced Engineering Informatics. 2022;2022(52): 613-626. https://doi.org/10.1016/j.aei.2022.101603.
  • 16. Zachman MJ, Yang Z, Du Y, Chi M. Robust atomicresolution imaging of lithium in battery materials by center-of-mass scanning transmission electron microscopy. ACS Nano. 2022;16(1):1358-1367. https://doi.org/10.1021/acsnano.1c09374.
  • 17. Chen Y, Shu Y, Li X, Xiong C, Cao S, Wen X, Xie Z. Research on detection algorithm of lithium battery surface defects based on embedded machine vision. Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology. 2021;41(3):4327- 4335. https://doi.org/10.3233/JIFS-189693.
  • 18. Danjuma MU, Yusuf B, Yusuf I. Reliability, availability, maintainability, and dependability analysis of cold standby series-parallel system. Journal of Computational and Cognitive Engineering. 2022; 1(4):193-200. https://doi.org/10.47852/bonviewJCCE2202144.
  • 19. Rabha D, Biswas S, Chamuah N, Chamuah N, Mandal M, Nath P. Wide-field multi-modal microscopic imaging using smartphone. Opticsand Lasers in Engineering. 2021;137(2):431-438. https://doi.org/10.1016/j.optlaseng.2020.106343.
  • 20. Lu L, Li J, Shu Y, Sun J, Zhou J, Ylam E, Chen Q, Zou C. Hybrid brightfield and darkfield transport of intensity approach for high-throughput quantitative phase microscopy. Advanced Photonics. 2022;4(5): 15-26. https://doi.org/10.1117/1.AP.4.5.056002.
  • 21. Chen TB, Zeng XF, Zhang ZY, Zhang F, Bai YY, Zhang XJ. REM: A simplified revised entropy image reconstruction for photonics integrated interference imaging system. Optics Communications. 2021; 501(1):11-16. https://doi.org/10.1016/j.optcom.2021.127341.
  • 22. Schrimpf M, Graefe PA, Kaczyna AE, Vorholt AJ, Leitner W. Measuring droplet sizes generated by 3Dprinted stirrers in a lean gas-liquid-liquid system using borescopy. Industrial & Engineering Chemistry Research. 2022;61(7):2701-2713. https://doi.org/10.1021/acs.iecr.1c03707.
  • 23. Yue Y, Sun M, Li X, Liu J, Lu Y, Chen J, Peng Y, Maraj M, Zhang J, Sun W. Quality improvement mechanism of sputtered AlN films on sapphire substrates with high-miscut-angles along different directions. Cryst Eng Comm. 2021;23(39):6871-6878. https://doi.org/10.1039/D1CE00654A.
  • 24. Chen J, Wang G, Meng J, Cheng Y, Yin Z, Yan T, Huang J, Zhang S, Wu J, Zhang X. Low-temperature direct growth of few-layer hexagonal boron nitride on catalyst-free sapphire substrates. ACS Applied Materials&Interfaces. 2022;14(5):7004-7011. https://doi.org/10.1021/ACSAMI.1C22626.
  • 25. Zhang W, Lei H, Liu W, Zhang Z. Effect of the carboxyl group number of the complexing agent on polishing performance of alumina slurry in sapphire CMP. Ceramics International. 2023;49(9):13687-13697. https://doi.org/10.1016/j.ceramint.2022.12.246.
  • 26. Tang B, Chen L, Sun ZK. Review of surface defect detection of steel products based on machine vision. IET Image Processing. 2023;17(2):303-322. https://doi.org/10.1049/ipr2.12647.
  • 27. Zhang X, Han X, Fu C. Comparison of object region segmentation algorithms of PCB defect detection. Traitement du Signal. 2023;40(2):797-802. https://doi.org/10.18280/ts.400241.
  • 28. Silenzi A, Castorani V, Tomassini S, Falcionelli N, Contardo P, Bonci A, Dragoni AF, Sernani P. Quality control of carbon look components via surface defect classification with deep neural networks. Sensors. 2023;23(17):1-18. https://doi.org/10.3390/s23177607.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-83410b06-d00e-46f6-97eb-7a185676dd2c
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