Dynamic observation of crack generation during wheel scribing from lateral and back sides using a high-speed camera

Imai, Kentaro; Saito, Masahiro; Matsusaka, Satoshi; Matsumoto, Yoshiji; Hidai, Hirofumi; Chibá, Akira; Morita, Noboru

doi:10.1016/j.precisioneng.2019.06.013

Cited by 9 publications

(2 citation statements)

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“…As described in the research result [5] , the panel strength will be weakened if the percentage of rib mark depth is bigger than 20%. Therefore, the U-Net Model is used in this research for carrying out the separation of rib mark features.…”

Section: Rib Mark Feature Detectionmentioning

confidence: 83%

“…are applied to indicate the defection position according to the specific defect reflection phenomenon. In aspect of TFT-LCD cutting process, the research conducted by Kentaro Imai [5] for the TFT-LCD glass cutting process in 2019 indicated that the conditions of cutter wheel can be judged according to the rib mark presented on the penal sectional profile. According to the research conducted by C. T. Pan in 2008 [6], it is also learned that the panel cutting process will be divided into upper and lower cutter wheel cutting for both panels.…”

Section: Study On Literaturesmentioning

confidence: 99%

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

Wang²,

Chiao³

2022

Int J Adv Manuf Technol

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In this research, the automatic optical detection system is developed for detecting the sectional profile and the surface of the TFT-LCD panel after being treated through the cutting process. 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; in result, 96% 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 seconds; in individual picture, the prediction time has been shortened for 0.29 second; in Auto Encoder, the model reading time has been shortened for 19.23 seconds; and the individual picture prediction time has been shortened for 0.94 second. 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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Section: Rib Mark Feature Detectionmentioning

confidence: 83%

Section: Study On Literaturesmentioning

confidence: 99%