• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1996.03a
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• pp.24-29
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• 1996

The seniconductor, which is precision product, requires many inspection processes. The surface conditions of the semiconductor chip effect on the functions of the semiconductors. The defects of the chip surface is crack or void. Because general inspection method requires many inspection processes, the inspection system which searches immediately and preciselythe defects of the semiconductor chip surface. We propose the inspection method by using the computer vision system. This study presents an image processing algorithm for inspecting the surface defects(crack, void)of the semiconductor test samples. The proposed image processing algorithm aims to reduce inspection time, and to analyze those experienced operator. This paper regards the chip surface as random texture, and deals with the image modeling of randon texture image for searching the surface defects. For texture modeling, we consider the relation of a pixel and neighborhood pixels as noncasul model and extract the statistical characteristics from the radom texture field by using the 2D AR model(Aut oregressive). This paper regards on image as the output of linear system, and considers the fidelity or intelligibility criteria for measuring the quality of an image or the performance of the processing techinque. This study utilizes the variance of prediction error which is computed by substituting the gary level of pixel of another texture field into the two dimensional AR(autoregressive model)model fitted to the texture field, estimate the parameter us-ing the PAA(parameter adaptation algorithm) and design the defect detection filter. Later, we next try to study the defect detection search algorithm.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.585-590
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• 2024

In this study, rail surface defects are increasing due to the aging of urban railway rails, but in the detailed guidelines for track performance evaluation established by the country, rail surface damage is inspected with the naked eye of engineers and simple measuring tools. With the recent enactment of the Track Diagnosis Act, a large budget has been invested and the volume of rail diagnosis is rapidly increasing, but it is difficult to secure the reliability of diagnosis results using labor-intensive visual inspection techniques. It is very important to discover defects in the rail surface through periodic track tours and visual inspection. However, evaluating the severity of defects on the rail surface based on the subjective judgment of the inspector has significant limitations in predicting damage inside the rail. In this study, the rail internal crack characteristics due to rail surface damage were studied. In field measurements, rail surface damage locations were selected, samples of various damage types were collected, and the rail surface damage status was evaluated. In indoor testing, we intend to analyze the correlation between rail surface defects and internal defects using a electron scanning microscope (SEM). To determine the crack growth rate of urban railway rails currently in use, the Gaussian probability density function was applied and analyzed.

• Design & Manufacturing
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• v.13 no.4
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• pp.57-62
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• 2019

In this study, we investigated the halo surface defection of various phenomenon occurred during the injection molding process which is caused by the thinning of the product thickness and the importance of the appearance. Surface analysis was performed to observe the difference between the surface where defects appeared and the surface which did not appear. Based on these results, we analyzed the phenomenon of halo surface defects was caused by unstable flow of resin generated in injection molding and velocity change of flow front. Furthermore, we will conduct a clear analysis of halo surface defects through observations through optical microscopy and subsequent observations with atomic force microscope. It has been analyzed that halo in PP is due to the rheological difference between the crystalline and amorphous regions while that in PC/ABS is due to shear separation of PC and ABS.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.5
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• pp.669-674
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• 2018

Data augmentation is an efficient way to reduce overfitting on models and to improve a performance supplementing extra data for training. It is more important in deep learning based industrial machine vision. Because deep learning requires huge scale of learning data to learn a model, but acquisition of data can be limited in most of industrial applications. A very generic method for augmenting image data is to perform geometric transformations, such as cropping, rotating, translating and adjusting brightness of the image. The effectiveness of data augmentation in image classification has been reported, but it is rare in defect inspections. We explore and compare various basic augmenting operations for the metal surface defects. The experiments were executed for various types of defects and different CNN networks and analysed for performance improvements by the data augmentations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.7
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• pp.865-870
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• 2018

A machine vision based industrial inspection includes defects detection and classification. Fast inspection is a fundamental problem for many applications of real-time vision systems. It requires little computation time and localizing defects robustly with high accuracy. Deep learning technique have been known not to be suitable for real-time applications. Recently a couple of fast region-based CNN algorithms for object detection are introduced, such as Faster R-CNN, and YOLOv2. We apply these methods for an industrial inspection problem. Three CNN based detection algorithms, VOV based CNN, Faster R-CNN, and YOLOv2, are experimented for defect detection on metal surface. The results for inspection time and various performance indices are compared and analysed.

• Journal of Periodontal and Implant Science
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• v.53 no.4
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• pp.295-305
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• 2023

Purpose: Various methods have been proposed to achieve the nearly complete decontamination of the surface of implants affected by peri-implantitis. We investigated the in vitro debridement efficiency of multiple decontamination methods (Gracey curettes [GC], glycine air-polishing [G-Air], erythritol air-polishing [E-Air] and titanium brushes [TiB]) using a novel spectrophotometric ink-model in 3 different bone defect settings (30°, 60°, and 90°). Methods: Forty-five dental implants were stained with indelible ink and mounted in resin models, which simulated standardised peri-implantitis defects with different bone defect angulations (30°, 60°, and 90°). After each run of instrumentation, the implants were removed from the resin model, and the ink was dissolved in ethanol (97%). A spectrophotometric analysis was performed to detect colour remnants in order to measure the cumulative uncleaned surface area of the implants. Scanning electron microscopy images were taken to assess micromorphological surface changes. Results: Generally, the 60° bone defects were the easiest to debride, and the 30° defects were the most difficult (ink absorption peak: 0.26±0.04 for 60° defects; 0.32±0.06 for 30° defects; 0.27±0.04 for 90° defects). The most effective debridement method was TiB, independently of the bone defect type (TiB vs. GC: P<0.0001; TiB vs. G-Air: P=0.0017; TiB vs. GE-Air: P=0.0007). GE-Air appeared to be the least efficient method for biofilm debridement. Conclusions: T-brushes seem to be a promising decontamination method compared to the other techniques, whereas G-Air was less aggressive on the implant surface. The use of a spectrophotometric model was shown to be a novel but promising assessment method for in vitro ink studies.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.5
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• pp.870-876
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• 2017

In this paper, we proposed the automatic vision inspection system using multi-layer perceptron to detect the defects occurred on rail surface. The proposed system consists of image acquisition part and analysis part. Rail surface image is acquired as equal interval using line scan camera and lighting. Mean filter and dynamic threshold is used to reduce noise and segment defect area. Various features to characterize the defects are extracted. And they are used to train and distinguish defects by MLP-classifier. The system is installed on HEMU-430X and applied to analyze the rail surface images acquired from Honam-line at high speed up to 300 km/h. Recognition rate is calculated through comparison with manual inspection results.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.1
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• pp.26-34
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• 1996

One of the challenging tasks in visual inspection using CCD camera is to identify surface defects in an image with complex textured backgeound. In microscopic view, the surface of real objects shows regular or random textured patterns. In this paper, we present a visual inspection algorithm to extract abnormal surface defects in an image with textured background. The algorithm uses the space and frequency information at the same time by introducing the Dominant Frequency Map(DFM) which can describe the frequency characteristics of every small local region of an input image. We demonstrate the feasibility and effectiveness of the method through a series of real experiments for a 14" TV CRT mold. The method successfully identifies a variety of infinitesimal defects, whose size is larger than $50\mu\textrm{m}$, of the mold. The experimental results show that the DFM based method is less sensitive to the environmental changes, such as illumination and defocusing, than conventional vision techniques.ques.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.123-128
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• 2016

In manufacturing processing of a secondary battery, the visual inspection system is studied and developed to check the surface defects of the electrode plates. It consists of two parts, one is the hardware control and the other software implementation. The former is made up to the system configuration and the design of the optical system, the illuminations and the controllers. The latter is the detection algorithms of the surface defects. This system achieves the quality improvement of the electrode process and the price competitiveness. By using the proposed defects detection algorithms this system demonstrates the high reliability of spot, line, manhole, extraneous substance, scratch, and crater defect of a electrode plate surface.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.17 no.6 s.123
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• pp.507-514
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• 2007

This paper reports on the research which investigates acoustic signals acquired in turning with rough and finish simultaneously. The material is aluminum thin pipe. Two acousto-ultrasonic sensors were set on the finish and the rough bite of the CNC machine. It was first evaluated that one source was affected by the other. It was found that two signals were little affected each other, and that the acoustic signal from the finish bite was more related to the surface defects. Signals from the finish bite only were then analyzed in order to observe several types of surface defects. Second the fundamental experiments were accomplished to study the effects of machine vibration and material state. The signal characteristics due to surface defects were studied from the collected acoustic signals. The analysis was based on real time data, root mean squared average and frequency spectrum by fast fourier transform. As a result, the acoustic signals were made effects by machine condition, material structure. The acoustic signal from the finish bite was closely correlated with surface quality. Two types surface micro defects were then evaluated by the signal characteristics.