Real-Time Image-Based Defect Inspection System of Internal Thread for Nut

Lin, Chun-Fu; Lin, Shih‐Yen; Hwang, Chi‐Hung; Tu, Hao-Kai; Chen, Chih-Yen; Weng, Chun-Jen

doi:10.1109/tim.2018.2872310

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…However, this method does not perform well on reflective surfaces or transparent materials, as the laser might be scattered or refracted, affecting the accuracy of measurements. Image-based systems, utilizing linear lasers and template matching techniques [ 7 ], can quickly detect thread defects. Yet, for parts with complex or non-standard thread shapes, it may be necessary to create multiple templates, increasing the system’s complexity and computational demands.…”

Section: Introductionmentioning

confidence: 99%

Internal thread defect detection system based on multi-vision

Dou,

Xue,

Zhang

et al. 2024

PLoS ONE

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In the realm of industrial inspection, the precise assessment of internal thread quality is crucial for ensuring mechanical integrity and safety. However, challenges such as limited internal space, inadequate lighting, and complex geometry significantly hinder high-precision inspection. In this study, we propose an innovative automated internal thread detection scheme based on machine vision, aimed at addressing the time-consuming and inefficient issues of traditional manual inspection methods. Compared with other existing technologies, this research significantly improves the speed of internal thread image acquisition through the optimization of lighting and image capturing devices. To effectively tackle the challenge of image stitching for complex thread textures, an internal thread image stitching technique based on a cylindrical model is proposed, generating a full-view thread image. The use of the YOLOv8 model for precise defect localization in threads enhances the accuracy and efficiency of detection. This system provides an efficient and intuitive artificial intelligence solution for detecting surface defects on geometric bodies in confined spaces.

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Section: Introductionmentioning

confidence: 99%

Internal thread defect detection system based on multi-vision

Dou,

Xue,

Zhang

et al. 2024

PLoS ONE

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“…In the industrial production processes of the fastener industry, several defects can occur and cause high manufacturing costs as well as a high amount of rejects [1,2]. The quality inspection of screws is a critical process due to the complex structure and variety of defects [2][3][4]. Screws are indispensable elements of many mechanical components, and it is crucial that those elements do not negatively affect the product.…”

Section: Introductionmentioning

confidence: 99%

Automated Defect Detection of Screws in the Manufacturing Industry Using Convolutional Neural Networks

Breitenbach¹,

Eckert²,

Mahal³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Defect detection in industrial production processes is an important and necessary part of quality control. Many defects can occur during the manufacturing process, causing high manufacturing costs. Thus the inspection of screws, which represent an indispensable element of many mechanical components, is a critical process. To reduce manufacturing costs and increase efficiency, a reliable method for inspection is Deep Learning. It can help simplify the process of quality control and increase the velocity and volume of detected defects in screws. This approach uses a CNN model to classify non-defective and defective screws with different types of defects. Instead of manual quality control methods, which can be easily biased, our CNN approach is accurate, cost-efficient, and fast, with an accuracy of over 97 percent. With this approach corresponding to industrial production processes, different defects in screws and non-defective screws can be classified from images according to a real-world industrial inspection scenario.

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“…Their performance affects the connection effect of the structure. Compared with the current mainstream thread-measuring equipment, three-dimensional (3D) threadmeasuring machines can accurately measure the main parameters of the thread in 3D space [1][2][3][4][5]. As the main measuring system of 3D thread-measuring machines, the probe can greatly affect their accuracy.…”

Section: Introductionmentioning

confidence: 99%

Coupling error model for the contact probe of a three-dimensional screw thread-measuring machine

Yu¹,

Yang²,

Cai³

et al. 2022

Meas. Sci. Technol.

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Coupling error is an important factor that affects the measurement accuracy of the contact scanning probe of a three-dimensional thread-measuring machine. To address this issue, this paper proposes a coupling error model for contact scanning probes. First, the mechanical structure of the contact scanning probe was introduced, and the main source of its coupling error was analyzed. Second, material mechanics was used to conduct a geometry–force analysis on the guiding mechanism of the probe, thereby establishing a coupling error model. Finally, the experiments were carried out to verify the accuracy of the coupling error model. From the experimental verification, the movement of the measuring head by 10 μm increased the deviation of the probe displacement by 2 μm, indicating the probe deflection. Moreover, the calculated probe deflection angle was consistent with the theoretical value, validating the proposed coupling error model. This method provides an important theoretical basis for the decoupling of measuring probe, thereby improving the accuracy of the probe guiding mechanism.

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Real-Time Image-Based Defect Inspection System of Internal Thread for Nut

Cited by 9 publications

References 17 publications

Internal thread defect detection system based on multi-vision

Internal thread defect detection system based on multi-vision

Automated Defect Detection of Screws in the Manufacturing Industry Using Convolutional Neural Networks

Coupling error model for the contact probe of a three-dimensional screw thread-measuring machine

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