In this study, we propose a deep learning-based method for large-area inspection aimed at the high-speed detection of micro hole diameters. Micro holes are detected and stored in large images using YOLOv8, an object detection model. A super-resolution technique utilizing ESRGAN, an adversarial neural network, is applied to images of small micro holes, enhancing them to high resolution before measuring their diameters through image processing. When comparing the diameters measured after 8x super-resolution with the results from existing inspection equipment, the average error rate is remarkably low at 0.504%. The time taken to measure an image of one micro hole is 0.470 seconds, which is ten times faster than previous inspection methods. These results can significantly contribute to high-speed measurement and quality improvement through deep learning.
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A Review of Intelligent Machining Process in CNC Machine Tool Systems Joo Sung Yoon, Il-ha Park, Dong Yoon Lee International Journal of Precision Engineering and Manufacturing.2025; 26(9): 2243. CrossRef
This paper presents a vision system for machined hole quality inspection of mechanical parts in an automobile. Automobile parts have various shapes and holes created by press punches. However, if the press punch pin is broken, a hole is not created on the mechanical parts. This problem causes serious part quality defects. To solve this problem, we proposed a vision system that could easily and cheaply inspect the quality of holes in automotive machining parts. A software development environment was created to build an economical vision inspection system. Images were gathered using the Near-real-time method to overcome the low frame-per-second of inexpensive Complementary Metal Oxide Semiconductor (CMOS) webcams. Status of the hole was determined using template matching and distance between holes as a feature. The hardware required for vision inspection was designed so that it could be directly applied to the automotive part manufacturing process. When the proposed vision inspection system was tested by installing it in an automobile parts factory for 3 months, the system showed an inspection accuracy of at least 97.9%. This demonstrates the effectiveness of the proposed method with accuracy and speed of hole defect inspection of machined parts.
Currently digital transformation has a huge impact on human lives. Digital transformation does not just mean a transformation of a (non-) physical element to a digitally identifiable element. It focuses on the utilization of digital technology for transforming (improving) procedures or routines of business and operation. The manufacturing industry has been adopting the most recent digital technology, and lots of digital data are being created. To utilize the stored data, data analysis is essential. Because the manufacturing data is created in a different format at every manufacturing step, the integration of the data is always the bottleneck of the data analysis. Querying of the right data at the proper time is fundamental for high-level data analysis. The digital thread is introduced to provide the inter-reference of digital data based on a context. This paper proposes a digital thread framework for the machining process. The context of the proposed framework consists of the questions of how the product will be machined, how it is (was) being produced, and how it was made. A prototype software was developed to verify the proposed framework by implementing the creating, storing, and querying modules for simulation, monitoring, and inspection data.
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A Review of Intelligent Machining Process in CNC Machine Tool Systems Joo Sung Yoon, Il-ha Park, Dong Yoon Lee International Journal of Precision Engineering and Manufacturing.2025; 26(9): 2243. CrossRef
The pipe inspection robot using the MFL non-destructive inspection equipment, has high inspection efficiency in the pipe with high magnetic permeability. However, this equipment generates attractive force between the pipe and the permanent magnet, requiring a high driving force for the robot, and sometimes causes the robot to be incapable of driving. In this study, the development of a spiral running type magnetic leakage detection pipe inspection robot system is described. Multi-body dynamics analysis was performed on the designed robot, to confirm the robot"s driving performance. After that, the performance of the robot was verified, by testing the manufactured robot in a standardized test bed.
The fourth industrial revolution is rapidly emerging as a new innovation trend for industrial automation. Accordingly, the demand for inspection equipment is highly increasing and vision sensor technologies are continuously evolving. Machine vision algorithms applied to deep learning are also being rapidly developed to maximize the performance of inspection equipment. In this review, we highlight the recent progress of vision sensor technology for the industrial inspection system. In particular, inspection principles and industrial applications of a vision sensor are classified according to the vision scanning methods. We also discuss machine vision-based inspection techniques containing rule- and deep learning-based image processing algorithms. We believe that this review provides novel approaches for various inspection fields of agriculture, medicine, and manufacturing industries.
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Image Data-based Product Classification and Defect Detection Hye-Jin Lee, Do-Gyeong Yuk, Jung Woo Sohn Transactions of the Korean Society for Noise and Vibration Engineering.2022; 32(6): 601. CrossRef
The demand for inspection of high-speed systems for machined Carbon Fiber Reinforced Plastics parts for automobileindustry and aviation industry is constantly rising. One of the factors that degrade the performance of an inspection system is micro-vibration from the ground or structure where is placed. Various isolation systems that suppress the vibration have been studied classified as either passive or active system. The passive system is composed of a spring and a damper while the active system suppresses the vibration through an electronic control system using sensors and actuators. In this study, a voice coil motor (force constant 55N/A) acting as the actuator is optimally designed using permeance method and sequential quadratic programming algorithm to suppress the vibration and reaction force by a specimen moving stage. The two optimized voice coil motors are attached to a pneumatic mount that has an advantage in design based on the force and size constraints required by the user for an active vibration isolator with velocity sensors (GS-11d). The active vibration isolation system with the four active vibration isolators -23 dB and -20 dB at resonance frequencies in horizontal and vertical transmissibility performs better than a passive vibration isolation system.
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An Active Geophone Sensor with Optimized State Variable Filter for Measuring Low-Band Frequencies Jinsoo Choi, Hongki Yoo, Eunjong Choi, Kihyun Kim, Hyo-Young Kim International Journal of Precision Engineering and Manufacturing.2024; 25(5): 981. CrossRef
Effect of inertia variations for active vibration isolation systems Jinsoo Choi, Kihyun Kim, Hyoyoung Kim, SeokWoo Lee Precision Engineering.2020; 66: 507. CrossRef
Defects in the insulator bushing are the major caution of destroying a switchgear. An epoxy bushing is composed of epoxy-molded insulator layer and a conductor. That means, a porosity or delamination defect could be included in the insulating layer by the manufacturing process. An inspection method is required to secure integrity of the bushing. An ultrasonic-immersion system has the power to produce a required effect to examine critically an epoxy material with high degree of fineness. In this research, an optimized ultrasonic immersion system was developed and applied to examine critically the epoxy-layer of bushings. As results of the result of a careful examination, both artificial defects and delamination were detected by the system. Currently, the ultrasonic-immersion system should be applied for examining the epoxy-layer of the bushing carefully.
This paper presents a machine vision-based surface inspection system for journal bearings. Traditionally, human operator inspection classifies the defective and defect-free bearings. Although the operator has capability to find a defect on the bearing surface, his/her inspection ability is influenced by fatigue and physical conditions related to repetitive work. Therefore, machine vision systems are widely used for quality control in order to reduce costs and to improve product quality. In this paper, we develop a machine vision system for journal bearing surface inspection that can inspect various types of defects on the bearing surface, such as laser marking quality, gas pockets, 2 Φ hole measurements, rust and so on. The proposed system was evaluated and installed on a journal bearing manufacturing line. The journal bearing manufacturer reported that the proposed system has high accuracy and efficiency.
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Development of Vision System for Quality Inspection of Machined Holes of Automobile Mechanical Parts Min Yong Han, Ki Hyun Kim, Hyo Young Kim, Kwang In Ko, Kyo Mun Ku, Dong Ju Ki, Jae Hong Shim Journal of the Korean Society for Precision Engineering.2023; 40(6): 499. CrossRef
Image Data-based Product Classification and Defect Detection Hye-Jin Lee, Do-Gyeong Yuk, Jung Woo Sohn Transactions of the Korean Society for Noise and Vibration Engineering.2022; 32(6): 601. CrossRef
Specular highlight region restoration using image clustering and inpainting Hosun Kang, Dokyung Hwang, Jangmyung Lee Journal of Visual Communication and Image Representation.2021; 77: 103106. CrossRef
Modified ORB-SLAM Algorithm for Precise Indoor Navigation of a Mobile Robot Yongjin Ock, Hosun Kang, Jangmyung Lee Journal of Korea Robotics Society.2020; 15(3): 205. CrossRef
Real-time Reflection Light Detection Algorithm using Pixel Clustering Data Dokyung Hwang, Jongwoo An, Hosun Kang, Jangmyung Lee Journal of Korea Robotics Society.2019; 14(4): 301. CrossRef
The automated inspection method of paper cups by using a deep learning classifier is proposed. Unlike conventional inspection methods requiring defect detection, feature extraction, and classification stages, the proposed method gives a unified inspection approach where three separate stages are replaced by one deep-learning model. The images of paper cups are grabbed using a CCD (Charge Coupled Device) camera and diffused LED lights. The defect patches are extracted from the gathered images and then augmented to be trained by the deep- learning classifier. The random rotation, width and height shift, horizontal and vertical flip, shearing, and zooming are used as data augmentation. Negative patches are randomly extracted and augmented from gathered images. The VGG (Visual Geometry Group)-like classifier is used as our deep-learning classifier and has five convolutional layers and max-pooling layers for every two convolutional layers. The drop-outs are adopted to prevent overfitting. In the paper, we have tested four kinds of defects and nondefects. The optimal classifier model was obtained from train and validation data and the model shows 96.5% accuracy for test data. The results conclude that the proposed method is an effective and promising approach for paper cup inspection.
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Research and Evaluation on an Optical Automatic Detection System for the Defects of the Manufactured Paper Cups Ping Wang, Yang-Han Lee, Hsien-Wei Tseng, Cheng-Fu Yang Sensors.2023; 23(3): 1452. CrossRef
Method and Installation for Efficient Automatic Defect Inspection of Manufactured Paper Bowls Shaoyong Yu, Yang-Han Lee, Cheng-Wen Chen, Peng Gao, Zhigang Xu, Shunyi Chen, Cheng-Fu Yang Photonics.2023; 10(6): 686. CrossRef
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