Detection Of Coating Defects Research Articles

Image Data-Centric Visual Feature Selection on Roll-to-Roll Slot-Die Coating Systems for Edge Wave Coating Defect Detection.

Roll-to-roll (R2R) manufacturing depends on a system's capability to deposit high-quality coatings with precise thickness, width, and uniformity. Therefore, consistent maintenance requires the immediate and accurate detection of coating defects. This study proposes a primary color selection (PCS) method to detect edge defects in R2R systems. This method addresses challenges associated with training data demands, complexity, and defect adaptability through a vision data-centric approach, ensuring precise edge coating defect detection. Using color information, high accuracy was achieved while minimizing data capacity requirements and processing time. Precise edge detection was facilitated by accurately distinguishing coated and noncoated regions by selecting the primary color channel based on color variability. The PCS method achieved superior accuracy (95.8%), outperforming the traditional weighted sum method (78.3%). This method is suitable for real-time detection in manufacturing systems and mitigates edge coating defects, thus facilitating quality control and production optimization.

Gaussian process regression based inspection robot for predicting and locating pipeline anticorrosion coating defects

The direct current voltage gradient (DCVG) technology is adept at identifying defects and corrosion issues within the anti-corrosion layer of buried pipelines by measuring changes in voltage gradient above the ground. Its widespread adoption in the field of anti-corrosion layer defect detection for its high precision and accuracy. However, the current DCVG inspection process relies on experienced operators holding electrodes to walk along the pipeline, resulting in a huge workload. To address these challenges, this paper proposes an innovative method that combines Gaussian process regression (GPR) with an intelligent inspection robot for autonomous pipeline anti-corrosion coating defect detection. This method uses environmental data to directly predict the location of defects within a pipeline’s anti-corrosion coating. Through incremental learning, the GPR model is trained to be continuously updated based on new samples such as position coordinates and voltage measurements during autonomous inspections. In addition, the intelligent inspection robot operates collaboratively with crawler wheels and UR robotic arms, enhancing motion stability and flexibility in expanding training data sets. Experimental results confirm that the intelligent inspection robot driven by Gaussian process prediction can achieve accurate defect positioning within 25 iteration cycles, with a positioning accuracy within 0.12 m. This method enhances defect detection accuracy, alleviating operator burden and offering an efficient solution for buried pipeline maintenance.

Research on oriented surface defect detection in the aircraft skin‐coating process based on an attention detector

AbstractAircraft coating process has been an important part in manufacturing process of modern aviation products. For coating defect detection, the manual observation with naked eyes is usually utilized, which leads to low production efficiency. In this paper, the authors propose the improved YOLOv5‐OBB with the channel‐spatial attention block (CSAB), feature pyramid non‐local module (FPNM) and structured sparsity slimming criterion (SSSC). The CSAB can pay more attention to effective channel information features from the channel dimension and the target information area from the spatial dimension. The effective non‐local module called FPNM is proposed to further improve the detection accuracy. The authors utilize the oriented bounding boxes (OBB) to reduce redundant background information for coating defect detection. In addition, the SSSC is proposed to achieve network slimming and trade‐off between the efficiency and accuracy. The experimental results on several datasets demonstrate the effectiveness of the authors’ scheme, which achieves superior performance.

Real-Time Optical Detection of Artificial Coating Defects in PBF-LB/P Using a Low-Cost Camera Solution and Convolutional Neural Networks

Additive manufacturing plays a decisive role in the field of industrial manufacturing in a wide range of application areas today. However, process monitoring, and especially the real-time detection of defects, is still an area where there is a lot of potential for improvement. High defect rates should be avoided in order to save costs and shorten product development times. Most of the time, effective process controls fail because of the given process parameters, such as high process temperatures in a laser-based powder bed fusion, or simply because of the very cost-intensive measuring equipment. This paper proposes a novel approach for the real-time and high-efficiency detection of coating defects on the powder bed surface during the powder bed fusion of polyamide (PBF-LB/P/PA12) by using a low-cost RGB camera system and image recognition via convolutional neural networks (CNN). The use of a CNN enables the automated detection and segmentation of objects by learning the spatial hierarchies of features from low to high-level patterns. Artificial coating defects were successfully induced in a reproducible and sustainable way via an experimental mechanical setup mounted on the coating blade, allowing the in-process simulation of particle drag, part shifting, and powder contamination. The intensity of the defect could be continuously varied using stepper motors. A low-cost camera was used to record several build processes with different part geometries. Installing the camera inside the machine allows the entire powder bed to be captured without distortion at the best possible angle for evaluation using CNN. After several training and tuning iterations of the custom CNN architecture, the accuracy, precision, and recall consistently reached >99%. Even defects that resembled the geometry of components were correctly classified. Subsequent gradient-weighted class activation mapping (Grad-CAM) analysis confirmed the classification results.

Effect of Electrode Spacing on the Detection of Coating Defects in Buried Pipelines Using Direct Current Voltage Gradient Method

Buried piping is subject to soil corrosion, which can be prevented by combining coatings and cathodic protection to maximize corrosion control. However, even with both methods, coatings are subject to damage from external factors and various causes. Buried piping may expose the metal and alter the current flow, which in turn causes corrosion. Therefore, this study analyzed the effect of detection electrode spacing on the direct current voltage gradient (DCVG) magnitude formed for coated pipelines buried in the soil. The DCVG was measured using a real-time coating defect detection system. FEM model simulations were carried out, and then the result was compared to the measured DCVG magnitude. When the spacing of the detection electrodes increased, the detected signal and signal location changed. The detection reliability increased as the noise signal is eliminated at the optimum detection electrode spacing. However, the detection reliability decreased at higher selection electrode spacing as the noise signal and detected signals together were eliminated. The location of the detected signal shifted as the spacing of the detection electrodes increased due to the change in the detection reference point and signal magnitude.

Coating Defect Detection Method Based on Data Augmentation and Network Optimization Design

Coating defect detection is a critical aspect of ensuring product quality in the manufacturing process. However, due to the variety of coating defects and the complex detection background in actual production, detecting these defects can be challenging. To improve the accuracy and robustness of coating defect detection, a coating defect detection method based on data augmentation and network optimization design is proposed. First, a feature image random adaptive weighted mapping (FIRAWM) strategy is proposed, considering the prior accuracy, quantity and context information of each category. Then, several improvements are made to the YOLOv5 network. Specifically, to mitigate the aliasing effects and enhance feature richness during the feature fusion process, an additional detection layer is added, and the coordinate attention module and the adaptively spatial feature fusion (ASFF) module are introduced. Finally, ablation and comparison experiments are performed to demonstrate the effectiveness of the proposed method. The results show that the method achieves a 96.7 mAP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</sub> with a processing speed of 61 FPS on the coating defect dataset, outperforming other popular detectors. Furthermore, the method is versatile and can be applied to detection tasks in various scenarios.

On Detecting Internal Coating Defects in Pipes using EMAT

Pipeline integrity is a must to maintain to ensure safe and smooth operation. Therefore, periodic inspection is required to make timely decisions for replacement or repair. One of the problems faced in a pipeline network, and targeted by this work is internal coating defect detection, whereby it is required to locate and characterize damage in the internal coating of the pipe. Current industry practice is to replace an entire set of pipelines or its large segment due to unknown defect location, implying significant wastage. A widely used NDT technology, electromagnetic acoustic transducer (EMAT) is employed in this laboratory-based work, however, it is a challenging task to extract internal coating damage information from the received acoustic signal as it has low SNR and is highly non-linear and non-stationary, thus warranting the use of sophisticated algorithms for data analysis. Artificial defects in the form of 100% tape-wrap coating removal are introduced with dimensions 100 x 100 mm2, 30 x 30 mm2, and 10 x 10 mm2 (square-shaped). The Carbon-Steel pipe sample has a 12” diameter and 10 mm wall thickness. The objectives are to externally inspect the pipe sample using commercially available hand-held EMAT sensors with Lamb modes, and develop and employ Singular Spectrum Analysis (SSA) algorithm to characterize the internal coating defects. Only the largest defect is detected, whereby A1 Lamb mode shows precise location and shape, and acceptable sizing accuracy compared with A0, S0, and S1. Moreover, the results are repeatable over several scans, thus showing the effectiveness of the adopted methodology. The main novelty lies in the fact that the methodology does not require any modifications to the hardware of the EMAT setup, thus increasing the capability of the available hand-held equipment. Current and future work is focused on the detection of smaller defects.

Long Array of Microwave Sensors for Real-Time Coating Defect Detection

A real-time, inexpensive, and easy-to-install resonator-based microwave sensor array for coating defect detection on metallic structures is presented. The sensor array is composed of 55 identical rectangular spiral resonators coupled to a 120-cm-long transmission line. The array was simulated, fabricated, and measured. A stopband was created at the fundamental resonant frequency of the resonators. The fabricated sensor array was implemented on the coating to monitor defects beneath the sensor. The proposed sensor array can be used to detect water ingress and coating delamination, which are causes for alarm in metallic structure corrosion and cracking. The proposed structure is a low-cost solution for real-time coating failure identification.

Multiresonant Chipless RFID Array System for Coating Defect Detection and Corrosion Prediction

In this article, a fully passive, wireless solution for out-of-sight pipeline monitoring is presented. By predicting corrosion before its occurrence, a proactive response may be taken to mitigate the chance of an environmental disaster. The chipless radio-frequency identification system is a combination of a tag ID, consisting of an array of six rectangular spiral resonators, and a tag antenna consisting of two cross-polarized novel patch antennas etched on a skin-thin microwave laminate. The tags are grounded on a carbon steel pipeline coated with $\text{3 mm}$ Teflon (polyethylene-like electrical characteristics). The proposed system exhibits robust capability to create frequency signatures to detect and monitor defects underneath the pipeline coating owing to water ingress that could eventuate to corrode the pipeline. The reader antenna above the pipeline comprises two identically cross-polarized log periodic dipole antennas with the intent to remotely capture resonances of the tag ID in real-time for early detection and prediction of potential pipeline exposure to moisture. The proposed structure provides low-cost real-time solution.

Metallic Surface Coating Defect Detection Using Firefly Based Adaptive Thresholding and Level Set Method

An innovative approach is introduced to detect surface defects on titanium coated steel surfaces with varied size through the use of image processing techniques. This paper provides techniques which are useful to discover numerous kinds of surface defects present on coating surface. For defect detection, Firefly Algorithm (FA) based adaptive thresholding is proposed and is applied for the gray scale images. The FA ensuing nature inspired algorithm utilized expansively in support of determining various optimization problems and from the reconstructed image contours are extracted using level set method, the predictable images not including textures besides defects contours be compassed. The morphological post processing removes the noise in image and makes defects more distinguishable from the background. The speculative result persists in utilizing synchronous images of metal surface and shows that the proposed method can efficiently segment surface defects and obtain better performance than existing methods.

외면부식직접평가 장비 개발 및 실증 시험에 관한 연구

매설된 강관의 피복이 손상되는 경우에는 부식이 발생할 수 있고, 부식으로 인한 핀홀이 발생하여 가스누출사고가 발생할 수도 있다. 미국과 영국 등 국외에서는 이를 방지하기 위하여 DCVG 또는 ACVG 등을 이용하여 매설된 배관의 피복손상을 탐측하고 이상부위에 대하여 직접 굴착을 통해 배관외면부식여부 등을 확인하고 필요시에는 보수를 하고 있다. 이를 외면부식 직접평가법(ECDA)이라 하며, NACE 기준 등에서 ECDA에 대한 기준을 규정하고 있다. 국내의 경우에는 2014년 중압배관의 정밀안전진단 제도를 도입하면서 KGS 코드에 외면 부식직접평가에 대한 내용을 포함하여 규정하고 있다. 본 연구에서는 매설배관의 외면부식을 탐측할 수 있는 장비를 개발하고, 개발된 장비의 실증시험을 위한 배관시험장을 구축하였다. 또한, 개발된 장비의 성능을 입증하기 위하여 배관 시험장에서 현장 실증시험을 실시하였다. 현장 실증 시험은 개발된 장비와 국내에 도입되어 사용중인 국외 장비와의 피복결함 탐측 성능 비교시험으로 실시하였다. 개발한 장비를 이용하여 매설배관 탐측 시험을 실시한 결과, 개발한 장비는 미국이나 영국에서 개발되어 국내에 보급되어 사용중인 장비에 비해 훨씬 사용이 편리하고 성능이 우수함을 확인할 수 있었다. When the coatings of buried steel pipelines are damaged, corrosion could be occurred on the surface of the damaged areas. Moreover the pinhole occurred by corrosion of pipelines may cause accidents due to gas leakage. To prevent these accidents, foreign countries including UK and USA have carried out coating defect detection on the buried gas pipelines by using a DCVG or a ACVG and have conducted direct assessment of pipelines through digging the ground, and if necessary, have repaired the pipelines. That is called ECDA i.e External Corrosion Direct Assessment which is regulated by NACE standards(SP 0502) and etc. In Korea, the ECDA provisions were included in KGS FS551 in 2014 when the regulations of Safety Validation in Detail for the medium-pressure piping were introduced. We have developed the equipment which can be used to detect external corrosion of the buried gas pipelines. We have also constructed pipeline test bed for empirical test of the developed equipment. In addition, we have carried out the verification experiments of the developed equipment on the test bed to demonstrate the performance of the equipment. The experiments were conducted by comparison tests of the developed equipment and other equipments which have been introduced and used in Korea. As the result, we have found the developed equipment is easier to use and has far superior performance compared to other equipment being used in Korea.

Probability of detection of defects in coatings with electronic shearography : Maddux, G.A.; Horton, C.M.; Lansing, M.D.; Gnacek, W.J.; Newton, P.L. Alabama Univ., Huntsville (United States), N95-12391/5/GAR, 22pp. (Jul. 1994)

The goal of this research was to utilize statistical methods to evaluate the probability of detection (POD) of defects in coatings using electronic shearography. The coating system utilized in the POD studies was to be the paint system currently utilized on the external casings of the NASA Space Transportation System (STS) Revised Solid Rocket Motor (RSRM) boosters. The population of samples was to be large enough to determine the minimum defect size for 90 percent probability of detection of 95 percent confidence POD on these coatings. Also, the best methods to excite coatings on aerospace components to induce deformations for measurement by electronic shearography were to be determined.

Probability of detection of defects in coatings with electronic shearography : Anon National Aeronautics and Space Administration, Huntsville, Alabama (United States), N95-29734/2/GAR, 22pp. (Jun. 1995)

Probability of detection of defects in coatings with electronic shearography : Anon National Aeronautics and Space Administration, Huntsville, Alabama (United States), N95-29734/2/GAR, 22pp. (Jun. 1995)