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Machine vision-based cutting process for LCD glass defect detection system

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Abstract

In this research, the automatic optical detection system is developed for detecting the sectional profile and the surface of the thin-film transistor liquid crystal display (TFT-LCD) panels after being treated through the cutting process. Traditional image processing inspection relying on pre-determined thresholding cannot achieve ideal results in slight defects in glass substrates. The proposed image pre-processing process was integrated with the deep learning technique to further enhance the detection process of inconspicuous defects in glass substrates. In addition, the photoelastic reflection lighting technique was used to highlight subtle defects in low-contrast surface images of glass substrates. When tested with the sectional profile photodetector, uniformed lighting effect is achieved by combining the concentrated light source of the inner coaxial lens with the line light source in order to test the surface coarseness-related characteristics of the glass sectional profile so as to indicate the defect while intensifying the contrast effect of the sectional profile background. When detecting the sectional profile features, it is conducted by separating the rib mark features through the U-Net network model developed for the deep learning; in result, 100% accuracy can be achieved. When detecting the sectional profile defect, Auto Encoder network model of the deep learning is used to learn the background picture retrieved from the original picture through the linear regression process. As a next step, the model is used again to predict the result by subtracting the background picture from the original picture, and then the defect position is highlighted; as a result, 98% accuracy is achieved. When detecting the model acceleration, it is conducted by revising the model weighting data format. In terms of U-Net Model, the reading time has been shortened for 4.28 s; in individual picture, the prediction time has been shortened for 0.29 s; in Auto Encoder, the model reading time has been shortened for 19.23 s; and the individual picture prediction time has been shortened for 0.94 s. As for the surface detection, the circular polariscope is developed. During the detection, the photoelastic reflection theory is employed by projecting the circularly polarized light vertically onto the panel surface in order to produce the interfering halo at the deformed area surrounding the denting defect, and the resulting features are referenced to identify the denting defect. By screening the mean luminance value of the sliding window and the discrete value, 90% detection accuracy can be achieved. In the meantime, it can also be used to pinpoint the denting defect that is over 3° in average angle change as seen on the normal line surrounding the defect.

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Availability of data and materials

The datasets analyzed during the current article are available from corresponding author.

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References

  1. Abd Al Rahman M, Alireza Mousavi (2020) A review and analysis of automatic optical inspection and quality monitoring methods in electronics industry. IEEE Access 8:183192–183271

  2. Fan MR (2008) Study of the characteristics of the light/thin LCD panel fabrication. Yuan Ze University, Master's Thesis 

  3. Wee Keong Neo, Dennis et al (2021) Edge grinding characteristics of display glass substrate. J Manuf Mater Process 5.1:20

  4. Chen CC (2012) The product-mix decisions for LCD and EPD product in TFT LCD industry. Chiao Tung University, Master's Thesis

  5. Zhong ZW, Tian YB, **e TG (2016) Investigation of subsurface damage of ground glass edges. Int J Adv Manuf Technol 87(9):3261–3269

    Article  Google Scholar 

  6. Lin YT (2002) Development and exploration of line scan machine vision inspection systems. Master's Thesis, Chung Hua University

  7. Lu R, Wu A, Zhang T, Wang Y (2018) Review on automated optical (visual) inspection and its applications in defect detection. ACTAOPTICASINICA 38(08):23–58

    Google Scholar 

  8. Imai K et al (2019) Dynamic observation of crack generation during wheel scribing from lateral and back sides using a high-speed camera. Precis Eng 60:421–427

    Article  Google Scholar 

  9. Pan CT et al (2008) Study of cutting quality for TFT-LCD glass substrate. Int J Adv Manuf Technol

  10. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. in International Conference on Medical image computing and computer-assisted intervention, Springer, pp 234–241 

  11. Lin XC (2020) Comparing the segmentation performances between U-net and data density functional method in MRI datasets. National Central University, Master's Thesis 

  12. Jo H, Kim J (2019) Regularized auto-encoder-based separation of defects from backgrounds for inspecting display devices. Electronics 8(5):533

    Article  Google Scholar 

  13. Xu K, Wang JQ, Liu XW (2017) Novel image feature extraction algorithm based on fusion AutoEncoder and CNN. Application Research of Computers 34(12):3839G3843

  14. Vanhoucke V, Senior A, Mao MZ (2011) Improving the speed of neural networks on CPUs. Deep Learning and Unsupervised Feature Learning Workshop

  15. Lawn BR, Swain MV (1975) Microfracture beneath point indentations in brittle solids. J Mater Sci 10(1):113–122

    Article  Google Scholar 

  16. Shin D-C, Hawong J-S (2011) Development of a hybrid method of reflection photoelasticity for crack problems in anisotropic plates. Exp Mech 51(2):183–198

    Article  Google Scholar 

  17. His-Chao Chen, Kuo-Ting Huang, Po-Wei Cheng (2017) Thin-film stress measurement technology development for optoelectronic components. Journal of Taiwan Vacuum Society 30(2) 

  18. Minami, Hirofumi et al (2011) Manufacturing and inspection equipment for efficient production of large LCDs. Hitachi Rev 60.5:228–232

  19. Chi YC (2012) Optical properties of Cr, CrxOy, CrxNy thin films. Master's thesis, Chung Hsing University

  20. Sárosi Z, Knapp W, Kunz A, Wegener K (2010) Evaluation of reflectivity of metal parts by a thermo-camera. InfraMation 2010 proceedings pp 475–486

  21. Yang LH (2005) Machine vision-based light-on test for LCD panel. Chung Hua University, Master's thesis 

  22. Matthias S, Kästner M, Reithmeier E (2016) Comparison of LASER and LED illumination for fiber optic fringe projection. in Optical Sensing and Detection IV 9899:989905 International Society for Optics and Photonics

  23. Chuang YC (2009) Study on inspection method for glass substrate’s surface scratch study on inspection method for glass substrate’s surface scratch study on inspection method for glass substrate’s scratch defects. National Kaohsiung University of Applied Science and Technology, Master's thesis 

  24. Chuang CI (2004) The study of a linear dichroism and retarder combined system by multi-wavelength photoelastic modulated ellipsometry. Jiaotong University, Master's thesis 

  25. Stokes GG (1851) On the composition and resolution of streams of polarized light from different sources. Trans Cambridge Philos Soc 9:399

    Google Scholar 

  26. Mueller H (1943) Memorandum on the polarization optics of the photoelastic shutter. Report of the OSRD project OEMsr-576 2

  27. Zhang M, Yin L (2022) Solar cell surface defect detection based on improved YOLO v5. IEEE Access 10:80804–80815

    Article  Google Scholar 

  28. Chen Y, Dai L (2016) Nature of crack-tip plastic zone in metallic glasses. Int J Plast 77:54–74

    Article  Google Scholar 

  29. Hsu WC, Chang WC, Sun PL, Chang LW (2015) Electron backscatter diffraction study on early recrystallization of ultra-low carbon steel. Min Metall 59(3):59–68

    Google Scholar 

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Funding

This research was supported by the Ministry of Science and Technology, Taiwan, under Grant no. 110–2221-E-027 -079 -MY2.

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Authors and Affiliations

  1. Graduate Institute of Manufacturing Technology, Department of Mechanical Engineering, National Taipei University of Technology, Taipei, Taiwan

    Chao-Ching Ho, Hao-** Wang & Yuan-Cheng Chiao

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  1. Chao-Ching Ho
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  2. Hao-** Wang
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  3. Yuan-Cheng Chiao
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Contributions

Chao-Ching Ho conceived and designed the experiments; Hao-** Wang performed the experiments; Chao-Ching Ho and Hao-** Wang analyzed the data; Yuan-Cheng Chiao and Hao-** Wang contributed deep learning implementations; Chao-Ching Ho wrote the paper.

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Correspondence to Chao-Ching Ho.

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Ho, CC., Wang, HP. & Chiao, YC. Machine vision-based cutting process for LCD glass defect detection system. Int J Adv Manuf Technol 123, 1477–1498 (2022). https://doi.org/10.1007/s00170-022-10261-9

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  • DOI: https://doi.org/10.1007/s00170-022-10261-9

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