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1
Peng, Peiran, Ying Wang, Can Hao, Zhizhong Zhu, Tong Liu, and Weihu Zhou. "Automatic Fabric Defect Detection Method Using PRAN-Net." Applied Sciences 10, no. 23 (November 26, 2020): 8434. http://dx.doi.org/10.3390/app10238434.
Повний текст джерелаАнотація:
Fabric defect detection is very important in the textile quality process. Current deep learning algorithms are not effective in detecting tiny and extreme aspect ratio fabric defects. In this paper, we proposed a strong detection method, Priori Anchor Convolutional Neural Network (PRAN-Net), for fabric defect detection to improve the detection and location accuracy of fabric defects and decrease the inspection time. First, we used Feature Pyramid Network (FPN) by selected multi-scale feature maps to reserve more detailed information of tiny defects. Secondly, we proposed a trick to generate sparse priori anchors based on fabric defects ground truth boxes instead of fixed anchors to locate extreme defects more accurately and efficiently. Finally, a classification network is used to classify and refine the position of the fabric defects. The method was validated on two self-made fabric datasets. Experimental results indicate that our method significantly improved the accuracy and efficiency of detecting fabric defects and is more suitable to the automatic fabric defect detection.
2
Zhao, Weidong, Hancheng Huang, Dan Li, Feng Chen, and Wei Cheng. "Pointer Defect Detection Based on Transfer Learning and Improved Cascade-RCNN." Sensors 20, no. 17 (September 1, 2020): 4939. http://dx.doi.org/10.3390/s20174939.
Повний текст джерелаАнотація:
To meet the practical needs of detecting various defects on the pointer surface and solve the difficulty of detecting some defects on the pointer surface, this paper proposes a transfer learning and improved Cascade-RCNN deep neural network (TICNET) algorithm for detecting pointer defects. Firstly, the convolutional layers of ResNet-50 are reconstructed by deformable convolution, which enhances the learning of pointer surface defects by feature extraction network. Furthermore, the problems of missing detection caused by internal differences and weak features are effectively solved. Secondly, the idea of online hard example mining (OHEM) is used to improve the Cascade-RCNN detection network, which achieve accurate classification of defects. Finally, based on the fact that common pointer defect dataset and pointer defect dataset established in this paper have the same low-level visual characteristics. The network is pre-trained on the common defect dataset, and weights are transferred to the defect dataset established in this paper, which reduces the training difficulty caused by too few data. The experimental results show that the proposed method achieves a 0.933 detection rate and a 0.873 mean average precision when the threshold of intersection over union is 0.5, and it realizes high precision detection of pointer surface defects.
3
Shan, Ning, Xia Liu, and Yong Zhong Ma. "Experiment Research on an Optical Fiber F-p Ultrasound Sensor for Detecting Internal Defects of Metal Materials." Advanced Materials Research 549 (July 2012): 593–96. http://dx.doi.org/10.4028/www.scientific.net/amr.549.593.
Повний текст джерелаАнотація:
Metal materials have been used in aero industry widely because of its excellent characteristics. So its internal defects are very important. Ultrasound detection technology for detecting metal materials internal defects is related to piezoelectric ultrasonic sensor. This has a few of disadvantages. So the double wavelength optical fiber F-P ultrasound sensing system is designed in this paper. The ultrasound detecting experiment devices for internal defects of metal materials is established based on the optical fiber F-P sensing system. Experimental research of detecting the internal defects is developed. The experimental results show this sensor can detect the ultrasound signals effectively. And it’s proved that this method can be effective used in the internal defect of metal materials.
4
Yin, Xiaokang, Zhuoyong Gu, Wei Wang, Xiaorui Zhang, Xin'an Yuan, Wei Li, and Guoming Chen. "Detection of Outer Wall Defects on Steel Pipe Using an Encircling Rotating Electromagnetic Field Eddy Current (RoFEC) Technique." Strojniški vestnik - Journal of Mechanical Engineering 68, no. 1 (January 15, 2022): 27–38. http://dx.doi.org/10.5545/sv-jme.2021.7288.
Повний текст джерелаАнотація:
In recent years, the rotating electromagnetic field eddy current (RoFEC) testing technique has attracted widespread attention due to its various advantages for inspecting tubular structures. However, most of the related work was focused on detecting inner wall defects on metal pipes using feed-through probes, which are often not applicable for outer wall defect detection. This work pushes forward the encircling RoFEC technique and demonstrates its feasibility for detecting outer wall defects. First, the basic principle of the encircling RoFEC technique is introduced. A three-dimensional finite element (FE) model was built in COMSOL to analyse the distribution of the rotating electromagnetic field and study the interaction between the defects and eddy currents. The axial component of the resultant magnetic field due to defects was selected as a characteristic signal and obtained from the FE models to study the factors, including pipe tilt, defect circumferential location, defect orientation and defect size, that influence the detection performance. An encircling RoFEC system using a probe with six excitation windings and a single bobbin pickup coil was constructed and used to inspect a steel pipe with one axial and one circumferential defect. The obtained voltage signal due to defects can form a Lissajous pattern in the impedance plane and be used for defect evaluation. The results showed that the encircling RoFEC technique can detect outer wall defects of both orientations and determine the circumferential location of the defect.
5
Odgaard, P. F., J. Stoustrup, and P. Andersen. "Detection of Surface Defects on Compact Discs." Journal of Control Science and Engineering 2007 (2007): 1–10. http://dx.doi.org/10.1155/2007/36319.
Повний текст джерелаАнотація:
Online detection of surface defects on optical discs is of high importance for the accommodation schemes handling these defects. These surface defects introduce defect components to the position measurements of focus and radial tracking positions. The respective controllers will accordingly try to suppress these defect components resulting in a wrong positioning of the optical disc drive. In this paper, two novel schemes for detecting these surface defects are introduced and compared. Both methods, which are an extended threshold scheme and a wavelet packet-based scheme, improve the detection compared with a standard threshold scheme. The extended threshold scheme detects the four tested defects with a maximal detection delay of 3 samples while the wavelet packet-based scheme has a maximal detection delay of 6 samples. Simulations of focus and radial positions in the presence of a surface defect are performed in order to inspect the importance and consequences of the size of the detection delay, from which it can be seen that focus and radial position errors increase significantly due to the defect as the detection delay increases.
6
Amini, Amin, Jamil Kanfoud, and Tat-Hean Gan. "An Artificial-Intelligence-Driven Predictive Model for Surface Defect Detections in Medical MEMS." Sensors 21, no. 18 (September 13, 2021): 6141. http://dx.doi.org/10.3390/s21186141.
Повний текст джерелаАнотація:
With the advancement of miniaturization in electronics and the ubiquity of micro-electro-mechanical systems (MEMS) in different applications including computing, sensing and medical apparatus, the importance of increasing production yields and ensuring the quality standard of products has become an important focus in manufacturing. Hence, the need for high-accuracy and automatic defect detection in the early phases of MEMS production has been recognized. This not only eliminates human interaction in the defect detection process, but also saves raw material and labor required. This research developed an automated defects recognition (ADR) system using a unique plenoptic camera capable of detecting surface defects of MEMS wafers using a machine-learning approach. The developed algorithm could be applied at any stage of the production process detecting defects at both entire MEMS wafer and single component scale. The developed system showed an F1 score of 0.81 U on average for true positive defect detection, with a processing time of 18 s for each image based on 6 validation sample images including 371 labels.
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Yang, Ya-xun, Wen-hao Chai, De-chuang Liu, Wei-de Zhang, Jia-cheng Lu, and Zhi-kui Yang. "An Impact-Echo Experimental Approach for Detecting Concrete Structural Faults." Advances in Civil Engineering 2021 (December 20, 2021): 1–8. http://dx.doi.org/10.1155/2021/8141015.
Повний текст джерелаАнотація:
For the current problem of detection of grouting defects in posttensioned prestressed concrete members, the paper takes a single-layer arrangement of prestressed pipes as the object of study. The influence law of the main factors such as pipe material, defect size, defect critical surface location, and prestressing reinforcement location on the results of the impact-echo method for detecting concrete grouting defects was studied. Firstly, the ABAQUS finite element software was used to simulate these factors to obtain the influence law on the detection results, and a modal test was conducted to verify them. The results show that the impact-echo method can effectively test the location of defects and the degree of burial depth, and the pipe material influences the test results, and the impact of corrugated metal pipe is smaller and more accurate than the PVC pipe. In addition, the greater the plate thickness frequency drift rate, the larger the transverse size of the defect, so the plate thickness frequency drift rate and the measured defect depth are combined to quantitatively determine the depth of the defect.
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Sakurada, Yuichi, Mai Takashima, Toshiyuki Yasuhara, Yoshinao Iwamoto, Makoto Matsuo, and Naoto Ohtake. "Detecting Method of Bulk Defects in DLC Films Using Light Scattering." Key Engineering Materials 523-524 (November 2012): 793–98. http://dx.doi.org/10.4028/www.scientific.net/kem.523-524.793.
Повний текст джерелаАнотація:
Diamond-like carbon (DLC) film has various micro-size defects like pinhole, void and particle. When DLC film is exposed to white light, light is scattered in all direction at defects in DLC film. In this paper, defects in DLC film are detected by observing scattering light from defects under dark-field microscope. DLC film has wavelength dependence of transmittance. Therefore, using its wavelength dependence allows to separate surface and inside defects of DLC film. This paper describes development of bulk defects detecting system using optical filtering and scattering light detecting. Bulk defects of DLC films were successfully separated into surface defects and inside defects. This detecting method of defect is nondestructive and easy, and applicable to DLC films as well as other coating films.
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Yang, Shihao, Dongmei Jiao, Tongkun Wang, and Yan He. "Tire Speckle Interference Bubble Defect Detection Based on Improved Faster RCNN-FPN." Sensors 22, no. 10 (May 21, 2022): 3907. http://dx.doi.org/10.3390/s22103907.
Повний текст джерелаАнотація:
With the development of neural networks, object detection based on deep learning is developing rapidly, and its applications are gradually increasing. In the tire industry, detecting speckle interference bubble defects of tire crown has difficulties such as low image contrast, small object scale, and large internal differences of defects, which affect the detection precision. To solve these problems, we propose a new feature pyramid network based on Faster RCNN-FPN. It can fuse features across levels and directions to improve small object detection and localization, and increase object detection precision. The method has proven its effectiveness through cross-validation experiments. On a tire crown bubble defect dataset, the mAP [0.5:0.95] increased by 2.08% and the AP0.5 increased by 2.4% over the original network. The results show that the improved network significantly improves detecting tire crown bubble defects.
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Ahmad, Rais, and Tribikram Kundu. "Structural Health Monitoring of Steel Pipes under Different Boundary Conditions and Choice of Signal Processing Techniques." Advances in Civil Engineering 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/813281.
Повний текст джерелаАнотація:
Guided wave technique is an efficient method for monitoring structural integrity by detecting and forecasting possible damages in distributed pipe networks. Efficient detection depends on appropriate selection of guided wave modes as well as signal processing techniques. Fourier analysis and wavelet analysis are two popular signal processing techniques that provide a flexible set of tools for solving various fundamental problems in science and engineering. In this paper, effective ways of using Fourier and Wavelet analyses on guided wave signals for detecting defects in steel pipes are discussed for different boundary conditions. This research investigates the effectiveness of Fourier transforms and Wavelet analysis in detecting defects in steel pipes. Cylindrical Guided waves are generated by piezo-electric transducers and propagated through the pipe wall boundaries in a pitch-catch system. Fourier transforms of received signals give information regarding the propagating guided wave modes which helps in detecting defects by selecting appropriate modes that are affected by the presence of defects. Continuous wavelet coefficients are found to be sensitive to defects. Several types of mother wavelet functions such as Daubechies, Symlet, and Meyer have been used for the continuous wavelet transform to investigate the most suitable wavelet function for defect detection. This research also investigates the effect of different boundary conditions on wavelet transforms for different mother wavelet functions.
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Rice, Andrew, Edward Aftandilian, Ciera Jaspan, Emily Johnston, Michael Pradel, and Yulissa Arroyo-Paredes. "Detecting argument selection defects." Proceedings of the ACM on Programming Languages 1, OOPSLA (October 12, 2017): 1–22. http://dx.doi.org/10.1145/3133928.
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Kim, Kwang-Baek, and Young Woon Woo. "Defect Detection in Ceramic Images Using Sigma Edge Information and Contour Tracking Method." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 1 (February 1, 2016): 160. http://dx.doi.org/10.11591/ijece.v6i1.9343.
Повний текст джерелаАнотація:
In this paper, we suggest a method of detecting defects by applying Hough transform and least squares on ceramic images obtained from non-destructive testing. In the ceramic images obtained from non-destructive testing, the background area, where the defect does not exist, commonly shows gradual change of luminosity in vertical direction. In order to extract the background area which is going to be used in the detection of defects, Hough transform is performed to rotate the ceramic image in a way that the direction of overall luminosity change lies in the vertical direction as much as possible. Least squares is then applied on the rotated image to approximate the contrast value of the background area. The extracted background area is used for extracting defects from the ceramic images. In this paper we applied this method on ceramic images acquired from non-destructive testing. It was confirmed that extracted background area could be effectively applied for searching the section where the defect exists and detecting the defect.
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Kim, Kwang-Baek, and Young Woon Woo. "Defect Detection in Ceramic Images Using Sigma Edge Information and Contour Tracking Method." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 1 (February 1, 2016): 160. http://dx.doi.org/10.11591/ijece.v6i1.pp160-166.
Повний текст джерелаАнотація:
In this paper, we suggest a method of detecting defects by applying Hough transform and least squares on ceramic images obtained from non-destructive testing. In the ceramic images obtained from non-destructive testing, the background area, where the defect does not exist, commonly shows gradual change of luminosity in vertical direction. In order to extract the background area which is going to be used in the detection of defects, Hough transform is performed to rotate the ceramic image in a way that the direction of overall luminosity change lies in the vertical direction as much as possible. Least squares is then applied on the rotated image to approximate the contrast value of the background area. The extracted background area is used for extracting defects from the ceramic images. In this paper we applied this method on ceramic images acquired from non-destructive testing. It was confirmed that extracted background area could be effectively applied for searching the section where the defect exists and detecting the defect.
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Chen, Liang Chia, Chia Cheng Kuo, and Ping Ang Yen. "Automatic Optical Inspection on TFT-LCD Mura Defects Using Background Image Reconstruction." Key Engineering Materials 364-366 (December 2007): 400–403. http://dx.doi.org/10.4028/www.scientific.net/kem.364-366.400.
Повний текст джерелаАнотація:
A mura defect detection algorithm for thin-film transistor liquid crystal display (TFTLCD) is developed for automatic detection of mura defects using Discrete Cosine Transform (DCT) principle for background image reconstruction. Detecting blob-mura defects in a LCD panel can be difficult due to non-uniform brightness background and slightly different brightness levels between the defect region and the background. To resolve this issue, a DCT-based background reconstruction algorithm was developed to establish the background image. The background of the inspected images can be first extracted and reconstructed by using the DCT principle and an image filtering strategy. Mura defects can then be detected by the developed segmented strategy. Actual performance of the developed method was evaluated on industrial LCD panels containing natural mura defects.
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Zz, Mi, C. Cong, Y. Cheng, and Zhang Hm. "Study on defects detection technique of precise optical element." E3S Web of Conferences 53 (2018): 01037. http://dx.doi.org/10.1051/e3sconf/20185301037.
Повний текст джерелаАнотація:
Aiming at the problems of low efficiency of traditional detection methods for surface defects of precision optical element and inconvenient detection for optical elements of different calibers, a adjustable optical element defects detecting device for large laser devices is designed. The key technical points of system composition, detection environment, illumination design and image stitching are expounded. According to the characteristics of surface defects of optical element, such as the difference of contour, gray scale, contrast and ambiguity, a classification method based on FCM is proposed. The experimental results show that the system can realize the automatic detection of surface defects, also it can effectively distinguishes micron-scale defects and has good defect recognition performance. The overall average recognition rate reached to 93.3%.
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Bessghaier, Narjes, Makram Soui, Christophe Kolski, and Mabrouka Chouchane. "On the Detection of Structural Aesthetic Defects of Android Mobile User Interfaces with a Metrics-based Tool." ACM Transactions on Interactive Intelligent Systems 11, no. 1 (March 10, 2021): 1–27. http://dx.doi.org/10.1145/3410468.
Повний текст джерелаАнотація:
Smartphone users are striving for easy-to-learn and use mobile apps user interfaces. Accomplishing these qualities demands an iterative evaluation of the Mobile User Interface (MUI). Several studies stress the value of providing a MUI with a pleasing look and feel to engaging end-users. The MUI, therefore, needs to be free from all kinds of structural aesthetic defects. Such defects are indicators of poor design decisions interfering with the consistency of a MUI and making it more difficult to use. To this end, we are proposing a tool (Aesthetic Defects DEtection Tool (ADDET)) to determine the structural aesthetic dimension of MUIs. Automating this process is useful to designers in evaluating the quality of their designs. Our approach is composed of two modules. (1) Metrics assessment is based on the static analysis of a tree-structured layout of the MUI. We used 15 geometric metrics (also known as structural or aesthetic metrics) to check various structural properties before a defect is triggered. (2) Defects detection: The manual combination of metrics and defects are time-consuming and user-dependent when determining a detection rule. Thus, we perceive the process of identification of defects as an optimization problem. We aim to automatically combine the metrics related to a particular defect and optimize the accuracy of the rules created by assigning a weight, representing the metric importance in detecting a defect. We conducted a quantitative and qualitative analysis to evaluate the accuracy of the proposed tool in computing metrics and detecting defects. The findings affirm the tool’s reliability when assessing a MUI’s structural design problems with 71% accuracy.
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Ding, Jian Hua, Yao Lu, Wei Huang, and Ming Qin. "A Background Subtraction Method for Defect Detection of Printed Image." Applied Mechanics and Materials 462-463 (November 2013): 421–27. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.421.
Повний текст джерелаАнотація:
Background subtraction is often used to detect the moving objects from static cameras. The difficult of defect detecting of printing matter is how to detect the unknown flaws in complicate background effectively. Inspired by the background modeling approach for moving objects detection, a background modeling method in defect detection of printed image is suggested in this paper. Those pixels without defects are regarded as background, while the flaw pixels are defined as foreground. Firstly, we select LBP histogram as texture feature and HSV histogram as color feature to model the background respectively. Then, lots of printed images in which there are no defects are used to update these two models. Finally, we utilize the models to detect defects of printing images. Experimental results show that this background model works well in our defect detection.
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Xu, Zhenying, Ziqian Wu, and Wei Fan. "Improved SSD-assisted algorithm for surface defect detection of electromagnetic luminescence." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 235, no. 5 (February 18, 2021): 761–68. http://dx.doi.org/10.1177/1748006x21995388.
Повний текст джерелаАнотація:
Defect detection of electromagnetic luminescence (EL) cells is the core step in the production and preparation of solar cell modules to ensure conversion efficiency and long service life of batteries. However, due to the lack of feature extraction capability for small feature defects, the traditional single shot multibox detector (SSD) algorithm performs not well in EL defect detection with high accuracy. Consequently, an improved SSD algorithm with modification in feature fusion in the framework of deep learning is proposed to improve the recognition rate of EL multi-class defects. A dataset containing images with four different types of defects through rotation, denoising, and binarization is established for the EL. The proposed algorithm can greatly improve the detection accuracy of the small-scale defect with the idea of feature pyramid networks. An experimental study on the detection of the EL defects shows the effectiveness of the proposed algorithm. Moreover, a comparison study shows the proposed method outperforms other traditional detection methods, such as the SIFT, Faster R-CNN, and YOLOv3, in detecting the EL defect.
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Lee, Wei-Chen, and Pei-Ling Tai. "Defect Detection in Striped Images Using a One-Dimensional Median Filter." Applied Sciences 10, no. 3 (February 4, 2020): 1012. http://dx.doi.org/10.3390/app10031012.
Повний текст джерелаАнотація:
Defect detection is a key element of quality assurance in many modern manufacturing processes. Defect detection methods, however, often involve a great deal of time and manual work. Image processing has become widely used as a means of reducing the required detection time and effort in manufacturing. To this end, this study proposes an image-processing algorithm for detecting defects in images with striped backgrounds—defect types include scratches and stains. In order to detect defects, the proposed method first pre-processes images and rotates them to align the stripes horizontally. Then, the images are divided into two parts: blocks and intervals. For the blocks, a one-dimensional median filter is used to generate defect-free images, and the difference between the original images and the defect-free images is calculated to find defects. For the intervals, defects are identified using image binarization. Finally, the method superposes the results found in the blocks and intervals to obtain final images with all defects marked. This study evaluated the performance of the proposed algorithm using 65 synthesized images and 20 actual images. The method achieved an accuracy of 97.2% based on the correctness of the defect locations. The defects that could not be identified were those whose greyscales were very close to those of the background.
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Yu, Jung-Doung, Sang Yeob Kim, and Jong-Sub Lee. "Variations in Velocity and Sensitivity of Electromagnetic Waves in Transmission Lines Configured in Model Piles with Necking Defects Containing Soils." Sensors 20, no. 22 (November 16, 2020): 6541. http://dx.doi.org/10.3390/s20226541.
Повний текст джерелаАнотація:
This study investigates variations in the velocity and sensitivity of electromagnetic waves in transmission lines configured in defective model piles for the detection of necking defects containing soil. Experiments are performed with model piles containing defects filled with different materials, such as air, sands, and clay. Five different types of transmission lines are configured in model piles. The electromagnetic waves are generated and detected using a time domain reflectometer. The velocity of electromagnetic waves is highest when the defect is filled with air, and it decreases with an increase in the water content. The velocity is lowest when the defect is filled with clay. The sensitivity of transmission lines for detecting defects decreases with an increase in soil water contents. The transmission line with a single electrical wire and epoxy-coated rebar exhibits the highest sensitivity, followed by that with three and two parallel electrical wires. Transmission lines with a single electrical wire and uncoated rebar and those with two parallel electrical wires wrapped with a sheath exhibit poor sensitivity when the defect is filled with clay. This study demonstrates that electromagnetic waves can be effective tools for detecting necking defects with wet and conductive soils in bored piles.
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Zhou, Hongju, Liping Sun, Hongwei Zhou, Man Zhao, Xinpei Yuan, and Jicheng Li. "Tree Internal Defected Imaging Using Model-Driven Deep Learning Network." Applied Sciences 11, no. 22 (November 19, 2021): 10935. http://dx.doi.org/10.3390/app112210935.
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The health of trees has become an important issue in forestry. How to detect the health of trees quickly and accurately has become a key area of research for scholars in the world. In this paper, a living tree internal defect detection model is established and analyzed using model-driven theory, where the theoretical fundamentals and implementations of the algorithm are clarified. The location information of the defects inside the trees is obtained by setting a relative permittivity matrix. The data-driven inversion algorithm is realized using a model-driven algorithm that is used to optimize the deep convolutional neural network, which combines the advantages of model-driven algorithms and data-driven algorithms. The results of the comparison inversion algorithms, the BP neural network inversion algorithm, and the model-driven deep learning network inversion algorithm, are analyzed through simulations. The results shown that the model-driven deep learning network inversion algorithm maintains a detection accuracy of more than 90% for single defects or homogeneous double defects, while it can still have a detection accuracy of 78.3% for heterogeneous multiple defects. In the simulations, the single defect detection time of the model-driven deep learning network inversion algorithm is kept within 0.1 s. Additionally, the proposed method overcomes the high nonlinearity and ill-posedness electromagnetic inverse scattering and reduces the time cost and computational complexity of detecting internal defects in trees. The results show that resolution and accuracy are improved in the inversion image for detecting the internal defects of trees.
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Zou, Zhong Quan, Xu Wang, and Zhi Mei Wang. "Application of Ultrasonic Testing in Concrete Filled Steel Tubular Arch Bridge." Advanced Materials Research 639-640 (January 2013): 1025–28. http://dx.doi.org/10.4028/www.scientific.net/amr.639-640.1025.
Повний текст джерелаАнотація:
Concrete Filled Steel Tube(CFST) is widely used in civil engineering structures because of its superior mechanical performance. Yet the mechanical behavior of CFST is highly depended on the construction quality of the filled concrete. Hence it is very important for the inspection of the construction quality of the filled concrete in CFST structures. In this paper, the ultrasonic testing technique was used to detect the defect of the filled concrete of a CFST arch bridge. During the inspection, the ultrasonic transducer was moved along the circumference of the cross-section of the arch, and the defect of the concrete was comprehensively judged by detecting the change of sonic time, sonic amplitude and sonic frequency. Based on the analysis of the ultrasonic transmission path, the influences of different defects on the sonic time, sonic amplitude and sonic frequency were discussed. The detecting results were verified by core-drilling method. The verification showed that different kinds of defects defected by ultrasonic testing was in good accordance with the drilling samples, which demonstrates the adaptability of the ultrasonic detection technique in the construction quality inspection of CFST structures.
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Bocz, Péter, Ákos Vinkó, and Zoltán Posgay. "A practical approach to tramway track condition monitoring: vertical track defects detection and identification using time-frequency processing technique." Selected Scientific Papers - Journal of Civil Engineering 13, s1 (March 1, 2018): 135–46. http://dx.doi.org/10.1515/sspjce-2018-0013.
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Abstract This paper presents an automatic method for detecting vertical track irregularities on tramway operation using acceleration measurements on trams. For monitoring of tramway tracks, an unconventional measurement setup is developed, which records the data of 3-axes wireless accelerometers mounted on wheel discs. Accelerations are processed to obtain the vertical track irregularities to determine whether the track needs to be repaired. The automatic detection algorithm is based on time–frequency distribution analysis and determines the defect locations. Admissible limits (thresholds) are given for detecting moderate and severe defects using statistical analysis. The method was validated on frequented tram lines in Budapest and accurately detected severe defects with a hit rate of 100%, with no false alarms. The methodology is also sensitive to moderate and small rail surface defects at the low operational speed.
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Lu, Yuzhen, and Renfu Lu. "Detection of Surface and Subsurface Defects of Apples Using Structured- Illumination Reflectance Imaging with Machine Learning Algorithms." Transactions of the ASABE 61, no. 6 (2018): 1831–42. http://dx.doi.org/10.13031/trans.12930.
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Abstract. Machine vision technology coupled with uniform illumination is now widely used for automatic sorting and grading of apples and other fruits, but it still does not have satisfactory performance for defect detection because of the large variety of defects, some of which are difficult to detect under uniform illumination. Structured-illumination reflectance imaging (SIRI) offers a new modality for imaging by using sinusoidally modulated structured illumination to obtain two sets of independent images: direct component (DC), which corresponds to conventional uniform illumination, and amplitude component (AC), which is unique for structured illumination. The objective of this study was to develop machine learning classification algorithms using DC and AC images and their combinations for enhanced detection of surface and subsurface defects of apples. A multispectral SIRI system with two phase-shifted sinusoidal illumination patterns was used to acquire images of ‘Delicious’ and ‘Golden Delicious’ apples with various types of surface and subsurface defects. DC and AC images were extracted through demodulation of the acquired images and were then enhanced using fast bi-dimensional empirical mode decomposition and subsequent image reconstruction. Defect detection algorithms were developed using random forest (RF), support vector machine (SVM), and convolutional neural network (CNN), for DC, AC, and ratio (AC divided by DC) images and their combinations. Results showed that AC images were superior to DC images for detecting subsurface defects, DC images were overall better than AC images for detecting surface defects, and ratio images were comparable to, or better than, DC and AC images for defect detection. The ensemble of DC, AC, and ratio images resulted in significantly better detection accuracies over using them individually. Among the three classifiers, CNN performed the best, with 98% detection accuracies for both varieties of apples, followed by SVM and RF. This research demonstrated that SIRI, coupled with a machine learning algorithm, can be a new, versatile, and effective modality for fruit defect detection. Keywords: Apple, Defect, Bi-dimensional empirical mode decomposition, Machine learning, Structured illumination.
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Chen, Shuguang, Jingyang Gao, Di Zhao, Pinjie Xu, and Tian Zhang. "Detection of Chip Layered Defects Based on Dual Focus Mechanism." Journal of Physics: Conference Series 2216, no. 1 (March 1, 2022): 012091. http://dx.doi.org/10.1088/1742-6596/2216/1/012091.
Повний текст джерелаАнотація:
Abstract Chip layering defects affect the performance of chips and lead to the failure of chips, so chip layering defects detection is an important step in the quality acceptance of chip production. Chip layering defects, which are characterized by insignificant color change in defect area, small defect area and difficult localization, bring challenges to traditional detection. In recent years, deep learning has shown its powerful ability to solve complex problems in computer vision. In this paper, semantic segmentation method is used to study the problem of chip hierarchical defect detection. Dual focus mechanism first applies whiteboard network structure to identify the true hierarchical area. Afterwards the defective layer area and the original map, the layered defect is recognized in the whiteboard attention. Since the contrast of the layered defect is not obvious, the precise layered defect tag extraction is another important factor affecting network performance. Based on the fuzzy-c-mean clustering algorithm and expert acceptance principle, obtaining the precise layered defect label, the practicality of this method is further enhanced. The effectiveness of the method for detecting the chip layering defects is verified by testing the chip image provided by Huawei.
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Yu, Liya, Zheng Wang, and Zhongjing Duan. "Detecting Gear Surface Defects Using Background-Weakening Method and Convolutional Neural Network." Journal of Sensors 2019 (November 19, 2019): 1–13. http://dx.doi.org/10.1155/2019/3140980.
Повний текст джерелаАнотація:
A novel, efficient, and accurate method to detect gear defects under a complex background during industrial gear production is proposed in this study. Firstly, we first analyzed image filtering and smoothing techniques, which we used as a basis to develop a complex background-weakening algorithm for detecting the microdefects of gears. Subsequently, we discussed the types and characteristics of gear manufacturing defects. Under the complex background of image acquisition, a new model S-YOLO is proposed for online detection of gear defects, and it was validated on our experimental platform for online gear defect detection under a complex background. Results show that S-YOLO has better recognition of microdefects under a complex background than the YOLOv3 target recognition network. The proposed algorithm has good robustness as well. Code and data have been made available.
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Kersemans, Mathias, Erik Verboven, Joost Segers, Saeid Hedayatrasa, and Wim Van Paepegem. "Non-Destructive Testing of Composites by Ultrasound, Local Defect Resonance and Thermography." Proceedings 2, no. 8 (July 30, 2018): 554. http://dx.doi.org/10.3390/icem18-05464.
Повний текст джерелаАнотація:
Different non-destructive testing techniques have been evaluated for detecting and assessing damage in carbon fiber reinforced plastics: (i) ultrasonic C-scan, (ii) local defect resonance of front/back surface and (iii) lock-in infrared thermography in reflection. Both artificial defects (flat bottom holes and inserts) and impact damage (barely visible impact damage) have been considered. The ultrasonic C-scans in reflection shows good performance in detecting the defects and in assessing actual defect parameters (e.g., size and depth), but it requires long scanning procedures and water coupling. The local defect resonance technique shows acceptable defect detectability, but has difficulty in extracting actual defect parameters without a priori knowledge. The thermographic inspection is by far the fastest technique, and shows good detectability of shallow defects (depth < 2 mm). Lateral sizing of shallow damage is also possible. The inspection of deeper defects (depth > 3–4 mm) in reflection is problematic and requires advanced post-processing approaches in order to improve the defect contrast to detectable limits.
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Si, Xiao Shu, Hong Zheng, and Xue Min Hu. "Fabric Defect Detection Based on SRG-PCNN." Advanced Materials Research 148-149 (October 2010): 1319–26. http://dx.doi.org/10.4028/www.scientific.net/amr.148-149.1319.
Повний текст джерелаАнотація:
Defect segmentation has been a focal point in fabric inspection research, and it remains challenging because it detects delicate features of defects complicated by variations in weave textures and changes in environmental factors. According to the different features between the normal fabric image and defect image, this paper presents an adaptive image segmentation method based on a simplified region growing pulse coupled neural network (SRG-PCNN) for detecting fabric defects. The validation tests on the developed algorithms were performed with fabric images, and results showed that SRG-PCNN is a feasible and efficient method for defect detection.
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Stoynova, Anna, and Borislav Bonev. "Active Thermography Diagnostics of Hidden Defects in Multilayer FR-4 Substrates." International Journal of Circuits, Systems and Signal Processing 16 (March 10, 2022): 787–92. http://dx.doi.org/10.46300/9106.2022.16.97.
Повний текст джерелаАнотація:
This paper discusses the applicability of pulsed and lock-in thermography using Fourier transform processing to diagnose specific types of defects in Multilayer FR-4 Substrates. Digital thermal models of test specimens with different types of defects have been created. Based on the obtained results, the methods are compared in terms of applicability and reliability in defect detection. The results show that by these methods can be detected and characterized in terms of geometric dimensions and type of certain types of specific defects arising in the production of FR-4 multilayer substrates. The presented results show that there is no obstacle to the detection of defects in different layers of the multilayer substrate within the same measurement, as well as the possibility of detecting many types of defects.
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Liu, Yan Bei, Zhi Tao Xiao, Fang Zhang, and Jun Wu. "Fabric Defect Detection Method Based on Gabor Filters." Advanced Materials Research 301-303 (July 2011): 229–34. http://dx.doi.org/10.4028/www.scientific.net/amr.301-303.229.
Повний текст джерелаАнотація:
In this paper, a fabric defect detection method based on Gabor filter bank is present. In this method, the parameters of Gabor filter bank depend on the fabric texture feature. Using the Gabor filter bank with multi-frequency and multi-orientation, a textile image produces multi-image. Then, the images are reconstructed into one image for detecting defects. It is illustrated that most kinds of defects are correctly detected and segmented. The experimental results show that the algorithm is robust and has good detection effect.
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Keeler, R., A. D. Singh, and H. S. Dua. "Detecting defects: the McHardy Perimeter." British Journal of Ophthalmology 95, no. 5 (April 19, 2011): 600. http://dx.doi.org/10.1136/bjophthalmol-2011-300191.
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Schroder, D. K. "New life in detecting defects." IEEE Circuits and Devices Magazine 14, no. 6 (1998): 14–20. http://dx.doi.org/10.1109/101.735790.
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Benito, Mónica, and Daniel Peña. "Detecting defects with image data." Computational Statistics & Data Analysis 51, no. 12 (August 2007): 6395–403. http://dx.doi.org/10.1016/j.csda.2007.02.016.
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Kangozhin, B. R., S. S. Dautov, M. S. Zharmagambetova, and M. A. Kosilov. "Thermal-Imaging Method for Monitoring the Insulation Condition of Oil-Filled Equipment." Journal of Computational and Theoretical Nanoscience 17, no. 7 (July 1, 2020): 3141–45. http://dx.doi.org/10.1166/jctn.2020.9151.
Повний текст джерелаАнотація:
The method of thermal imaging inspection allowing detecting defects at an early stage of their development is considered. The essence of the method consists in recalculation of measured temperature drops into insulating characteristics measured under operating voltage. It is shown that defects are detected at the early stage of their development using the defect detection criterion (tgδmeas-tgδcalc). It is concluded that it is possible to abandon a number of traditional methods of testing with electrical equipment shutdown.
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YAŞAR ÇIKLAÇANDIR, Fatma Günseli, Semih UTKU, and Hakan ÖZDEMİR. "Kumaşlarda Hatayı Yerel Olarak Arayan Denetimsiz Bir Sistem." Tekstil ve Mühendis 27, no. 120 (December 30, 2020): 252–59. http://dx.doi.org/10.7216/1300759920202712005.
Повний текст джерелаАнотація:
Defects in the fabrics during or after weaving reduce the quality of them. With the development of technology, the frequency of the defects seen in fabrics has decreased, but still occurs. In the process of detecting fabric defects, the quality control unit tries to detect fabric defects. This process is both personal and time consuming, leading to costly and personal Errors. For this reason, solutions have been proposed in studies to carry out and automate the process under computer control. In this study, fabric images are divided into blocks of equal sizes to find out whether there are any defects in the fabrics. The features, which are Extracted by applying feature extraction method to each block of the image, are inserted into the K-means clustering algorithm. Two different methods are applied for feature extraction (gray level co-formation matrix and median difference) and their performances have been compared. The success rate of detecting the defect increases up to 97.99% when the gray level co-occurrence matrix is used. The success rate of detecting the defect increases up to 86.91% when the median differences are used. In addition, In addition, when the success rates are calculated separately for the defects in the weft direction and the defects in the warp direction, it is concluded that the defects in the weft direction are easier to find than the defects in the warp direction.
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Wang, Quan, Zhijie Zhang, Wuliang Yin, Haoze Chen, and Yushan Liu. "Defect Detection Method for CFRP Based on Line Laser Thermography." Micromachines 13, no. 4 (April 13, 2022): 612. http://dx.doi.org/10.3390/mi13040612.
Повний текст джерелаАнотація:
A continuous line laser scanning inspection technique for tracing load-bearing structures was developed and applied to defect detection of unidirectional carbon-fiber-reinforced polymers for aero engines. The heat transfer model of the material was analyzed using the finite element software COMSOL. Meanwhile, a laser platform was built and an image algorithm was used to verify the feasibility of the method. The potential of this technique for detecting defects and providing information on the location of defects in carbon fiber composites was analyzed. Results indicate line laser thermal imaging can successfully determine the size, location, and crack angle of surface damage with extremely high accuracy. The positioning accuracy error for impact and fracture defects is less than 20%, and the detection rate can reach 100% if the defect is in the special position of just leaving the heating area. The angle detection of fracture cracks can be accurate within 10°.
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Jin, Rui, and Qiang Niu. "Automatic Fabric Defect Detection Based on an Improved YOLOv5." Mathematical Problems in Engineering 2021 (September 30, 2021): 1–13. http://dx.doi.org/10.1155/2021/7321394.
Повний текст джерелаАнотація:
Fabric defect detection is particularly remarkable because of the large textile production demand in China. Traditional manual detection method is inefficient, time-consuming, laborious, and costly. A deep learning technique is proposed in this work to perform automatic fabric defect detection by improving a YOLOv5 object detection algorithm. A teacher-student architecture is used to handle the shortage of fabric defect images. Specifically, a deep teacher network could precisely recognize fabric defects. After information distillation, a shallow student network could do the same thing in real-time with minimal performance degeneration. Moreover, multitask learning is introduced by simultaneously detecting ubiquitous and specific defects. Focal loss function and central constraints are introduced to improve the recognition performance. Evaluations are performed on the publicly available Tianchi AI and TILDA databases. Results indicate that the proposed method performs well compared with other methods and has excellent defect detection ability in the collected textile images.
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Cha, Jaegyeong, and Jongpil Jeong. "Improved U-Net with Residual Attention Block for Mixed-Defect Wafer Maps." Applied Sciences 12, no. 4 (February 20, 2022): 2209. http://dx.doi.org/10.3390/app12042209.
Повний текст джерелаАнотація:
Detecting defect patterns in semiconductors is very important for discovering the fundamental causes of production defects. In particular, because mixed defects have become more likely with the development of technology, finding them has become more complex than can be performed by conventional wafer defect detection. In this paper, we propose an improved U-Net model using a residual attention block that combines an attention mechanism with a residual block to segment a mixed defect. By using the proposed method, we can extract an improved feature map by suppressing irrelevant features and paying attention to the defect to be found. Experimental results show that the proposed model outperforms those in the existing studies.
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Hazra, S., D. Williams, R. Roy, and R. Aylmore. "Detecting subtle cosmetic defects in automotive skin panels." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 222, no. 11 (November 1, 2008): 2203–7. http://dx.doi.org/10.1243/09544062jmes910.
Повний текст джерелаАнотація:
Cosmetic defects, such as ‘hollows’, are deviations in topology of automotive skin panels that form as a result of springback at the end of the forming process. These deviations are usually too small and local to be detected by discrete measurements of the panel but become visually apparent after the application of paint. As a result, the perceived quality of a panel may become unacceptable and considerable time may be dedicated to minimizing their occurrence through tool modifications. This paper proposes that there are three aspects to the problem. The first is the springback of the panel, the second is the optics of the painted panel, and the third is the ability of human sight to perceive a defect. In particular, it is argued that hollows cause optical distortions that inform the human eye of the presence of a defect. The paper then suggests that signal processing techniques, in particular the wavelet transform, provide a way to relate the geometry of a hollow to the resulting optical distortion. The transform was applied to two physical parts and the paper will discuss the effectiveness of the transform in locating and quantifying the relative severities of the defects that were present.
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Zhang, Jian, Siwen Wei, Ming Qi, and Pengfei Wang. "Improved Aircraft Flared Tube Defect Detection Algorithm of YOLOv4 Network Structure." Journal of Physics: Conference Series 2252, no. 1 (April 1, 2022): 012050. http://dx.doi.org/10.1088/1742-6596/2252/1/012050.
Повний текст джерелаАнотація:
Abstract When detecting aircraft flared tube defect with the YOLOv4 network structure, tiny object defects will be missed, and resulting in a high missed detection rate and low mAP (mean Average Precision) value. This paper proposes an improved aircraft flared tube defect detection algorithm of YOLOv4 network structure. Firstly, in order to improve the feature extraction capability of the YOLOv4 network for tiny defects, a convolution operation is added to the SPP (Spatial Pyramid Pooling) and PANet (Path Aggregation Network) structure. Secondly, the representation of the feature pyramid is enhanced utilizing the improved PANet. Thirdly, the decoupled head is utilized to improve the model performance. Finally, we construct the aircraft flared tube dataset by labeling the defect samples, and experiment with the improved YOLOv4 network. Experimental results show that the mAP value of defect detection task is increased from 91.26% to 95.31%, average detection time increased from 346.17ms to 278.61ms.
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Zhang, Jian, Siwen Wei, Ming Qi, and Pengfei Wang. "Improved Aircraft Flared Tube Defect Detection Algorithm of YOLOv4 Network Structure." Journal of Physics: Conference Series 2252, no. 1 (April 1, 2022): 012050. http://dx.doi.org/10.1088/1742-6596/2252/1/012050.
Повний текст джерелаАнотація:
Abstract When detecting aircraft flared tube defect with the YOLOv4 network structure, tiny object defects will be missed, and resulting in a high missed detection rate and low mAP (mean Average Precision) value. This paper proposes an improved aircraft flared tube defect detection algorithm of YOLOv4 network structure. Firstly, in order to improve the feature extraction capability of the YOLOv4 network for tiny defects, a convolution operation is added to the SPP (Spatial Pyramid Pooling) and PANet (Path Aggregation Network) structure. Secondly, the representation of the feature pyramid is enhanced utilizing the improved PANet. Thirdly, the decoupled head is utilized to improve the model performance. Finally, we construct the aircraft flared tube dataset by labeling the defect samples, and experiment with the improved YOLOv4 network. Experimental results show that the mAP value of defect detection task is increased from 91.26% to 95.31%, average detection time increased from 346.17ms to 278.61ms.
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Vikram, Abhishek, and Vineeta Agarwal. "Patterning approach for detecting defect in device manufacturing." Физика и техника полупроводников 51, no. 12 (2017): 1716. http://dx.doi.org/10.21883/ftp.2017.12.45192.8203.
Повний текст джерелаАнотація:
Compact handheld devices which were a dream in the past are now a reality; this has been enabled by miniaturization of circuit architectures including power devices. Scaling down of the design feature sizes does come with a price with an increase in systematic defects during chip manufacturing. There are generally two methods of inline defect detection adopted to monitor the semiconductor device fabrication --- optical inspection and electron beam inspection. The optical inspection uses ultra-violet and deep ultra-violet (UV/DUV) light to find patterning defects on the wafer. While the electron- beam inspection uses electron charge and discharge measurement to find electrical connection defects, both are a costly procedure in terms of resources and time. The physical limit of feature resolution of the optical source is now making the defect inspection job difficult in miniaturized application specific integrated circuit (ASIC). This study is designed to test the patterning optimization approach on both inspection platforms. Using hotspot analysis weak locations are identified in the full chip design, and then they are verified in the inline wafer inspection. The criterion for hot-spot determination is also discussed in this paper. DOI: 10.21883/FTP.2017.12.45192.8203
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Allam, Abdelrahman, Medhat Moussa, Cole Tarry, and Matthew Veres. "Detecting Teeth Defects on Automotive Gears Using Deep Learning." Sensors 21, no. 24 (December 19, 2021): 8480. http://dx.doi.org/10.3390/s21248480.
Повний текст джерелаАнотація:
Gears are a vital component in many complex mechanical systems. In automotive systems, and in particular vehicle transmissions, we rely on them to function properly on different types of challenging environments and conditions. However, when a gear is manufactured with a defect, the gear’s integrity can become compromised and lead to catastrophic failure. The current inspection process used by an automotive gear manufacturer in Guelph, Ontario, requires human operators to visually inspect all gear produced. Yet, due to the quantity of gears manufactured, the diverse array of defects that can arise, the time requirements for inspection, and the reliance on the operator’s inspection ability, the system suffers from poor scalability, and defects can be missed during inspection. In this work, we propose a machine vision system for automating the inspection process for gears with damaged teeth defects. The implemented inspection system uses a faster R-CNN network to identify the defects, and combines domain knowledge to reduce the manual inspection of non-defective gears by 66%.
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Sheng, Zhenwen, and Guiyun Wang. "Fast Method of Detecting Packaging Bottle Defects Based on ECA-EfficientDet." Journal of Sensors 2022 (February 23, 2022): 1–9. http://dx.doi.org/10.1155/2022/9518910.
Повний текст джерелаАнотація:
Conventional methods of detecting packaging defects face challenges with multiobject simultaneous detection for automatic filling and packaging of food. Targeting this issue, we propose a packaging defect detection method based on the ECA-EfficientDet transfer learning algorithm. First, we increased the complexity in the sampled data using the mosaic data augmentation technique. Then, we introduced a channel-importance prediction mechanism and the Mish activation function and designed ECA-Convblock to improve the specificity in the feature extractions of the backbone network. Heterogeneous data transfer learning was then carried out on the optimized network to improve the generalization capability of the model on a small population of training data. We conducted performance testing and a comparative analysis of the trained model with defect data. The results indicate that, compared with other algorithms, our method achieves higher accuracy of 99.16% with good stability.
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Hu, Haibing, Bo Zhang, Dongjian Xu, and Guo Xia. "Battery Surface and Edge Defect Inspection Based on Sub-Regional Gaussian and Moving Average Filter." Applied Sciences 9, no. 16 (August 19, 2019): 3418. http://dx.doi.org/10.3390/app9163418.
Повний текст джерелаАнотація:
Detecting the defects of a battery on the surface and edge has always been difficult, especially for concave and convex ones, thereby seriously affecting its quality. Thus, sub-regional Gaussian and moving average filtering are innovatively proposed in this study considering the effect of the nonuniform background illumination of the battery edge and the difference between the edge background and the internal surface defects of the battery. The battery surface image is divided into two areas, namely, edge area W 1 and inner area W 2 . Gaussian and moving average filtering are carried out row-by-row and column-by-column in the inner area W 2 and the edge area W 1 , respectively. The algorithm is tested on 600 battery samples that mainly possess concave and convex defects. The proposed method has higher detection accuracy and lower omission detection rate than the traditional unpartitioned processing method, especially in detecting the accuracy of edge defects. The accuracy rates were approximately 20% higher than that obtained by the traditional processing algorithm. The proposed method has remarkable real-time performance that can process four 8192 × 10,240 pixel battery images per second, thereby meeting the industrial production line speed requirements while satisfying accuracy. The proposed method has been applied in actual production for defect inspection.
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Song, Kechen, and Yunhui Yan. "Micro Surface Defect Detection Method for Silicon Steel Strip Based on Saliency Convex Active Contour Model." Mathematical Problems in Engineering 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/429094.
Повний текст джерелаАнотація:
Accurate detection of surface defect is an indispensable section in steel surface inspection system. In order to detect the micro surface defect of silicon steel strip, a new detection method based on saliency convex active contour model is proposed. In the proposed method, visual saliency extraction is employed to suppress the clutter background for the purpose of highlighting the potential objects. The extracted saliency map is then exploited as a feature, which is fused into a convex energy minimization function of local-based active contour. Meanwhile, a numerical minimization algorithm is introduced to separate the micro surface defects from cluttered background. Experimental results demonstrate that the proposed method presents good performance for detecting micro surface defects including spot-defect and steel-pit-defect. Even in the cluttered background, the proposed method detects almost all of the microdefects without any false objects.
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BADAWI, EMAD A. "DETECTING DEFECTS IN AIRCRAFT MATERIALS BY NUCLEAR TECHNIQUE (PAS)." Surface Review and Letters 12, no. 01 (February 2005): 1–6. http://dx.doi.org/10.1142/s0218625x05006718.
Повний текст джерелаАнотація:
Positron annihilation spectroscopy (PAS) is one of the nuclear techniques used in material science. The present measurements are used to study the behavior of defect concentration in one of the most important materials aluminum alloys which is the 7075 alloy. It has been shown that positrons can become trapped at imperfect locations in solids and their mean lifetime can be influenced by changes in the concentration of such defects. No changes have been observed in the mean lifetime values after the saturation of defect concentration. The mean lifetime and trapping rates are studied for samples deformed up to 58.3%. The concentration of defect range vary from 1015 to 1018 cm -3 at the thickness reduction from 2.3 to 58.3%. The dislocation density varies from 108 to 1011 cm/cm 3.
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BADAWI, EMAD A. "DETECTING DEFECTS IN AIRCRAFT MATERIALS BY NUCLEAR TECHNIQUE (PAS)." International Journal of Modern Physics B 19, no. 22 (September 10, 2005): 3475–82. http://dx.doi.org/10.1142/s0217979205032280.
Повний текст джерелаАнотація:
Positron annihilation spectroscopy (PAS) is one of the nuclear techniques used in material science. The present measurements are used to study the behavior of defect concentration in one of the most important materials — aluminum alloy — which is a 7075 alloy. It has been shown that positrons can become trapped in imperfect locations in solids and their mean lifetime can be influenced by changes in the concentration of such defects. No changes have been observed in the mean lifetime values after the saturation of defect concentration. The mean lifetime and trapping rates were studied for samples deformed up to 58.3%. The concentration of defect range varies (from 1015 to 1018 cm-3) at the thickness reduction, (from 2.3 to 58.3%). The range of the dislocation density varies (from 108 to 1011 cm/cm3).
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Dengale, Pranav, Raj Karkhanis, Bhargav Phadke, and Prof Vaishali Gaidhane. "Metal Surface Defect Detection using Segmentation and Decision Networks." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 3063–69. http://dx.doi.org/10.22214/ijraset.2022.41978.
Повний текст джерелаАнотація:
Abstract: With set Quality Control and Quality Assurance processes, defects in manufacturing are rare but when they occur they can result in huge losses for the manufacturer if unchecked. Detecting these defects with the help of machine learning and deep learning techniques is now an interesting and promising area of research and industrial application. The project aims to build a model that will detect surface level cracks on metal commutators with deep learning techniques like image segmentation. Since in industry, defects are rare the segmentation based deep learning model will learn on fewer training samples as compared to a typical deep learning model which would require hundreds and thousands of training samples. The model is trained on the publicly available dataset provided by the Kolektor Group, which contains images of commutators and annotated if there are any surface cracks on the commutators. Keywords: Quality Assurance, Defect detection, commutators defect detection, Kolektor Dataset
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Srividhya, R., K. Shanmugapriya, and K. Sindhu Priya. "Automatic Detection of Surface Defects in Industrial Materials Based on Image Processing." International Journal of Engineering & Technology 7, no. 3.34 (September 1, 2018): 61. http://dx.doi.org/10.14419/ijet.v7i3.34.18717.
Повний текст джерелаАнотація:
In the field of industry, corrosion and defects are amongst the most frequent operations. Industrial Materials have periodic defects that are difficult to detect during production even by experienced human inspectors. Defects are difficult to detect during production even by experienced human inspectors. Usually, the colour transfer process contains an image segmentation phase and an image construction phase. Therefore, we introduce an image processing method for automatically detecting the defects in surfaces. We show how barely visible defect can be optically enhanced to improve annual assessment as well as how descriptor-based image processing and machine learning can be used to allow automated detection. Image enhancement is performed by applying manual calculation. We implement this simulation using MATLAB R2013a. Results show that the proposed allows training both tested classifiers with good classification rates around 98.9%.