Inspection Model Research Articles

Optimization of heat transfer in a channel with stretching walls using a magnetized tetra-hybrid nanofluid

Nanoparticles have numerous applications and are used frequently in different cooling, heating, treatment of cancer cells and manufacturing processes. The current investigation covers the utilization of tetra hybrid nanofluid (aluminium oxide, iron dioxide, titanium dioxide and copper) for Crossflow model over a vertical disk by considering the shape effects (bricks, cylindrical and platelet) of nanoparticles, electro-magneto-hydrodynamic effect and quadratic thermal radiation. The present study is devoted to present the mathematical formulation of the tetra-hybrid nanofluid flow in a porous channel with stretching/shrinking walls. The present inspection model is derived from given partial differential equations (PDEs) and then transformed into a system of ordinary differential equations (ODEs) by incorporating similarity variables. The transformed ODEs are solved using the bvp4c methodology, which yields numerical results. From the obtained results it is observed that the maximum amount of [Formula: see text] happens when there is slightly increase in [Formula: see text] with stretched wall that is [Formula: see text]. A high radiation value indicates a considerable contribution from radiative heat transfer, whereas stretching and shrinking increase and reduce the size of the boundary. The combined impact caused an increase in the Nusselt number. The graphical and tabular representations are used to explain the physical behaviour of various model parameters. Previous outcomes are also contrasted with the current outcomes.

Deep Packet Inspection Model Based on Support Vector Machine for Anomaly Detection in Local Area Networks

Deep packet inspection is a network security solution that identifies and flags anomalous network traffic patterns in a local network environment. Traditional signature-based techniques for intrusion detection are limited in identifying different attacks or completely new kinds, which makes them unsuitable in some situations. In addition, most previous methods for anomaly detection have low detection rate and high false alarm. In this study, a deep packet inspection model based on support vector machine (SVM) for anomaly detection in local area networks was proposed. The proposed method combined the SelectKBest method and SVM for the categorization of anomaly in a local network environment. Results showed that the proposed method outperformed other related machine learning methods with accuracy, precision, recall, and F1-score of 94.81%, 94.03%, 94.13%, and 94.0799%, respectively. The accuracy result shows that most network traffic can be correctly identified by the SVM using the SelectKBest approach, with minimal false positives or negatives.

YOLO-RRL: A Lightweight Algorithm for PCB Surface Defect Detection

Printed circuit boards present several challenges to the detection of defects, including targets of insufficient size and distribution, a high level of background noise, and a variety of complex types. These factors contribute to the difficulties encountered by PCB defect detection networks in accurately identifying defects. This paper proposes a less-parametric model, YOLO-RRL, based on the improved YOLOv8 architecture. The YOLO-RRL model incorporates four key improvement modules: The following modules have been incorporated into the proposed model: Robust Feature Downsampling (RFD), Reparameterised Generalised FPN (RepGFPN), Dynamic Upsampler (DySample), and Lightweight Asymmetric Detection Head (LADH-Head). The results of multiple performance metrics evaluation demonstrate that YOLO-RRL enhances the mean accuracy (mAP) by 2.2 percentage points to 95.2%, increases the frame rate (FPS) by 12%, and significantly reduces the number of parameters and the computational complexity, thereby achieving a balance between performance and efficiency. Two datasets, NEU-DET and APSPC, were employed to evaluate the performance of YOLO-RRL. The results indicate that YOLO-RRL exhibits good adaptability. In comparison to existing mainstream inspection models, YOLO-RRL is also more advanced. The YOLO-RRL model is capable of significantly improving production quality and reducing production costs in practical applications while also extending the scope of the inspection system to a wide range of industrial applications.

On-machine inspection and compensation for thin-walled parts with sculptured surface considering cutting vibration and probe posture

Abstract On-machine inspection has a significant impact on improving high-precision and efficient machining of sculptured surfaces. Due to the lack of machining information and the inability to adapt the parameters to the dynamic cutting conditions, theoretical modeling of profile inspection usually leads to insufficient adaptation, which causes inaccuracy problems. To address the above issues, a novel coupled model for profile inspection is proposed by combining the theoretical model and the data-driven model. The key process is to first realize local feature extraction based on the acquired vibration signals. The hybrid sampling model, which fuses geometric feature terms and vibration feature terms, is modeled by the lever principle. Then, the weight of each feature term is adaptively assigned by a multi-objective multi-verse optimizer. Finally, an inspection error compensation model based on the attention mechanism considering different probe postures is proposed to reduce the impact of pre-travel and radius errors on inspection accuracy. The anisotropy of the probe system error and its influence mechanism on the inspection accuracy are analyzed quantitatively and qualitatively. Compared with the previous models, the proposed hybrid profile inspection model can significantly improve the accuracy and efficiency of on-machine sampling. The proposed compensation model is able to correct the inspection errors with better accuracy. Simulations and experiments demonstrate the feasibility and validity of the proposed methods. The proposed model and corresponding new findings contribute to high-precision and efficient on-machine inspection, and help to understand the coupling mechanism of inspection errors.

Dense Convolutional Neural Network-Based Deep Learning Pipeline for Pre-Identification of Circular Leaf Spot Disease of Diospyros kaki Leaves Using Optical Coherence Tomography.

Circular leaf spot (CLS) disease poses a significant threat to persimmon cultivation, leading to substantial harvest reductions. Existing visual and destructive inspection methods suffer from subjectivity, limited accuracy, and considerable time consumption. This study presents an automated pre-identification method of the disease through a deep learning (DL) based pipeline integrated with optical coherence tomography (OCT), thereby addressing the highlighted issues with the existing methods. The investigation yielded promising outcomes by employing transfer learning with pre-trained DL models, specifically DenseNet-121 and VGG-16. The DenseNet-121 model excels in differentiating among three stages of CLS disease (healthy (H), apparently healthy (or healthy-infected (HI)), and infected (I)). The model achieved precision values of 0.7823 for class-H, 0.9005 for class-HI, and 0.7027 for class-I, supported by recall values of 0.8953 for class-HI and 0.8387 for class-I. Moreover, the performance of CLS detection was enhanced by a supplemental quality inspection model utilizing VGG-16, which attained an accuracy of 98.99% in discriminating between low-detail and high-detail images. Moreover, this study employed a combination of LAMP and A-scan for the dataset labeling process, significantly enhancing the accuracy of the models. Overall, this study underscores the potential of DL techniques integrated with OCT to enhance disease identification processes in agricultural settings, particularly in persimmon cultivation, by offering efficient and objective pre-identification of CLS and enabling early intervention and management strategies.

An Efficient and Accurate Quality Inspection Model for Steel Scraps Based on Dense Small-Target Detection

Scrap steel serves as the primary alternative raw material to iron ore, exerting a significant impact on production costs for steel enterprises. With the annual growth in scrap resources, concerns regarding traditional manual inspection methods, including issues of fairness and safety, gain increasing prominence. Enhancing scrap inspection processes through digital technology is imperative. In response to these concerns, we developed CNIL-Net, a scrap-quality inspection network model based on object detection, and trained and validated it using images obtained during the scrap inspection process. Initially, we deployed a multi-camera integrated system at a steel plant for acquiring scrap images of diverse types, which were subsequently annotated and employed for constructing an enhanced scrap dataset. Then, we enhanced the YOLOv5 model to improve the detection of small-target scraps in inspection scenarios. This was achieved by adding a small-object detection layer (P2) and streamlining the model through the removal of detection layer P5, resulting in the development of a novel three-layer detection network structure termed the Improved Layer (IL) model. A Coordinate Attention mechanism was incorporated into the network to dynamically learn feature weights from various positions, thereby improving the discernment of scrap features. Substituting the traditional non-maximum suppression algorithm (NMS) with Soft-NMS enhanced detection accuracy in dense and overlapping scrap scenarios, thereby mitigating instances of missed detections. Finally, the model underwent training and validation utilizing the augmented dataset of scraps. Throughout this phase, assessments encompassed metrics like mAP, number of network layers, parameters, and inference duration. Experimental findings illustrate that the developed CNIL-Net scrap-quality inspection network model boosted the average precision across all categories from 88.8% to 96.5%. Compared to manual inspection, it demonstrates notable advantages in accuracy and detection speed, rendering it well suited for real-world deployment and addressing issues in scrap inspection like real-time processing and fairness.

Toward Fully Automated Inspection of Critical Assets Supported by Autonomous Mobile Robots, Vision Sensors, and Artificial Intelligence.

Robotic inspection is advancing in performance capabilities and is now being considered for industrial applications beyond laboratory experiments. As industries increasingly rely on complex machinery, pipelines, and structures, the need for precise and reliable inspection methods becomes paramount to ensure operational integrity and mitigate risks. AI-assisted autonomous mobile robots offer the potential to automate inspection processes, reduce human error, and provide real-time insights into asset conditions. A primary concern is the necessity to validate the performance of these systems under real-world conditions. While laboratory tests and simulations can provide valuable insights, the true efficacy of AI algorithms and robotic platforms can only be determined through rigorous field testing and validation. This paper aligns with this need by evaluating the performance of one-stage models for object detection in tasks that support and enhance the perception capabilities of autonomous mobile robots. The evaluation addresses both the execution of assigned tasks and the robot's own navigation. Our benchmark of classification models for robotic inspection considers three real-world transportation and logistics use cases, as well as several generations of the well-known YOLO architecture. The performance results from field tests using real robotic devices equipped with such object detection capabilities are promising, and expose the enormous potential and actionability of autonomous robotic systems for fully automated inspection and maintenance in open-world settings.

Utilizing Deep Learning for Defect Inspection in Hand Tool Assembly.

The integrity of product assembly in the precision assembly industry significantly influences the quality of the final products. During the assembly process, products may acquire assembly defects due to personnel oversight. A severe assembly defect could impair the product's normal function and potentially cause loss of life or property for the user. For workpiece defect inspection, there is limited discussion on the simultaneous detection of the primary kinds of assembly anomaly (missing parts, misplaced parts, foreign objects, and extra parts). However, these assembly anomalies account for most customer complaints in the traditional hand tool industry. This is because no equipment can comprehensively inspect major assembly defects, and inspections rely solely on professionals using simple tools and their own experience. Thus, this study proposes an automated visual inspection system to achieve defect inspection in hand tool assembly. This study samples the work-in-process from three assembly stations in the ratchet wrench assembly process; an investigation of 28 common assembly defect types is presented, covering the 4 kinds of assembly anomaly in the assembly operation; also, this study captures sample images of various assembly defects for the experiments. First, the captured images are filtered to eliminate surface reflection noise from the workpiece; then, a circular mask is given at the assembly position to extract the ROI area; next, the filtered ROI images are used to create a defect-type label set using manual annotation; after this, the R-CNN series network models are applied to object feature extraction and classification; finally, they are compared with other object detection models to identify which inspection model has the better performance. The experimental results show that, if each station uses the best model for defect inspection, it can effectively detect and classify defects. The average defect detection rate (1-β) of each station is 92.64%, the average misjudgment rate (α) is 6.68%, and the average correct classification rate (CR) is 88.03%.

Research on collaborative inspection key technologies for multi-UAVs and multi-nests

The single individual capability and insufficient endurance of unmanned aerial vehicles (UAVs) have always been problems in power inspection operations. This article is based on the new development needs of UAV inspection and proposes a new model of multi-UAVs collaborative inspection. On this basis, this article studies the decoupling and intelligent matching technology between airports and UAVs, task merging and splitting technology in complex multi-task situations, and proposes a safety inspection strategy for multi-UAVs collaborative operations to ensure the safety of operations in the multi-UAVs multi-nests collaborative inspection mode. Finally, a complete multi-UAV, multi-nests collaborative inspection strategy is constructed.

Mobile-YOLO: An accurate and efficient three-stage cascaded network for online fiberglass fabric defect detection

The tolerance for different defects in the production process of fiberglass fabric varies. For acceptable defects, only relevant information needs to be recorded and printed, while for intolerable defects, the loom needs to be stopped promptly for handling. To address the issue of inefficient production of fiberglass fabric due to this situation, this paper proposes a task-modularized cascade network model for quality inspection of fiberglass fabric. The three stages cascaded detection network is composed of the improved MobilenetV3-small and the enhanced You Only Look Once v8 Nano (YOLOv8n). For MobilenetV3_small, the Squeeze- and-Excitation(SE) module in the Block layer with a 5 × 5 convolutional kernel is replaced with the Efficient Channel Attention (ECA) mechanism, enhancing channel information while making the model more lightweight. For YOLOv8n, a Global Attention Module (GAM) is added to the network, enhancing the ability to capture global information of input data, making the model more perceptual. Additionally, a Similarity Attention Module (SimAM) is added between the neck and head networks, enabling the network to better capture relevant information in the images, improving the quality of feature maps and enhancing network performance. Finally, a large number of defect images of fiberglass fabric were collected from an industrial site, and different datasets for the three stages were created. Numerous experiments were conducted, and when using all three stages for detection, the accuracy rate reaches 99.4%, with a parameter count of 16.8M and Frames Per Second (FPS) of 186. The cascade network has been successfully applied in the industrial field of fiberglass fabric and meets industrial production requirements.

Sparse deep encoded features with enhanced sinogramic red deer optimization for fault inspection in wafer maps

AbstractDue to the complexity and dynamics of the semiconductor manufacturing processes, wafer bin maps (WBM) present various defect patterns caused by various process faults. The defect type detection on wafer maps provides information about the process and equipment in which the defect occurred. Recently, automatic inspection has played a vital role in meeting the high-throughput demand, especially with deep convolutional neural networks (DCNN) which shows promising efficiency. At the same time, the need for a large amount of labeled and balanced datasets limits the performance of such approaches. In addition, complex DCNN in recognition tasks can provide redundant features that cause overfitting and reduce interpretability. In this paper, a new hybrid deep model for wafer map fault detection to get over these challenges is proposed. Firstly, a new convolutional autoencoder (CAE) is employed as a synthetization model to fix the high imbalance problem of the dataset. Secondly, for efficient dimensionality reduction, an embedding procedure is applied to the synthesized maps to get sparse encoded wafer maps by reinforcing a sparsity regularization in an encoder-decoder network to form a sparsity-boosted autoencoder (SBAE). The sparse embedding of wafer maps guarantees more discriminative features with 50% reduction in spatial size compared to the original wafer maps. Then, the 2D encoded sparse maps are converted to 1D sinograms to be fed later into another aggressive feature reduction stage using a new modified red deer algorithm with a new tinkering strategy. The resultant feature pool is reduced to ~ 25 1D feature bases, i.e., ~ 1.5% of the initial size of the 2D wafer maps. Finally, for the prediction stage, a simple 1DCNN model is introduced. The proposed inspection model is tested via different experiments on real-world wafer map dataset (WM-811K). Compared to state-of-the-art techniques, the proposed model outperforms their performance even with small-sized 1D feature pool. The average testing accuracy are 98.77% and 98.8% for 9 and 8 types of faults, respectively.

Defect Detection in Civil Structure Using Deep Learning Method

With the usage time, undesirable defects like crack and spalling appeared in various types of civil structures including beam; wall and column. These defects have a great influence on the usage life of the structures and hence need attention and maintenance. Normally, defect detection in civil structures is the human visual inspection. The manual approach is time-consuming and has a low accuracy. In this paper, we use deep learning method to predict multiple defects in various types of civil structures. Crack and spalling are recognized using the convolutional neural network model in a pixel level. To build the inspection model, the U-net Plus Plus architecture is used for segmentation. In the dataset, 1300 images are taken for training and 600 images for validation. The obtained computer vison model is tested on a dataset of about 491 images. The recall for crack and spalling reach 0.89 and 0.85 respectively. This research work develops an automated approach for defects detection in civil structures. The results can provide helpful guidance for structure health monitoring and maintenance in civil engineering like buildings and bridges.

Spot scanning imaging calibration method based on deviation model for wafer inspection

The spot scanning defect detection system has good detection performance for wafer defects. However, due to the existence of system assembly errors, the wafer defect inspection system could not scan following the planned trace, which resulted in distortion of the reconstructed image and had a greater impact on defect feature extraction and accurate defect location. The system deviation calibration and adjustment can further facilitate the accuracy of the system position and image reconstruction. To address this issue, the causes and shapes of imaging distortion were analyzed and simulated, and a simple and effective calibration method based on a scanning deviation model for the spot scanning defect detection system was proposed. First, a scanning deviation model was established following the characteristics of the spot scanning system, and different degrees of reconstructed image distortion were simulated and analyzed as the system deviation changes. Then, an 8-inch wafer with a grid was used as the calibrator and scanned using the developed spot scanning system. Serveral grid corners in the reconstructed image are selected and used as the calibration feature points. The constraint function is constructed using the equidistant features between adjacent grid points, and then the quasi-Newton method is used to optimize the parameters of the function to obtain the best deviation estimation of the spot scanning system. To prove the validity of the estimated deviation parameters, another patterned wafer was scanned and the distortion image was used for reconstruction. The experiment demonstrated that an accurate and distortion-free wafer image can be reconstructed using the calibrated deviation parameters, and the root mean square error of the measured distance between two points of interest was only 0.61 pixels, which proved the effectiveness of the proposed calibration method.

A High-Quality Sample Generation Method for Improving Steel Surface Defect Inspection.

Defect inspection is a critical task in ensuring the surface quality of steel plates. Deep neural networks have the potential to achieve excellent inspection accuracy if defect samples are sufficient. Nevertheless, it is very different to collect enough samples using cameras alone. To a certain extent, generative models can alleviate this problem but poor sample quality can greatly affect the final inspection performance. A sample generation method, which employs a generative adversarial network (GAN), is proposed to generate high-quality defect samples for training accurate inspection models. To improve generation quality, we propose a production-and-elimination, two-stage sample generation process by simulating the formation of defects on the surface of steel plates. The production stage learns to generate defects on defect-free background samples, and the elimination stage learns to erase defects on defective samples. By minimizing the differences between the samples at both stages, the proposed model can make generated background samples close to real ones while guiding the generated defect samples to be more realistic. Experimental results show that the proposed method has the ability to generate high-quality samples that can help train powerful inspection models and thereby improve inspection performance.

Development and application of a substation work ticket inspection tool based on big data cloud platform

In the context of the digital transformation of the power grid, based on information data mining and information fusion technology, fully utilizing big data cloud platforms to develop and apply substation work ticket inspection tools, innovating application data scenarios in the deep implementation of safety production, solving the problems of traditional work ticket inspection content and process timeliness. Through the application of substation work ticket inspection models, it is fully verified that substation work ticket inspection tools improve inspection efficiency and quality, and play a positive role in reducing personal and equipment risks during on-site operations.

Psychometric properties of a modified health belief model for cervical cancer and visual inspection with acetic acid among healthcare professionals in Ethiopia.

Evidence supports that the Health Belief Model (HBM) can explain and predicts certain health behaviors, including participation in cervical cancer (CC) screening. The purpose of this study was to evaluate the psychometric properties of a modified HBM for CC and visual inspection with acetic acid (VIA) in female healthcare professionals in Addis Ababa, Ethiopia, 2020. Psychometric properties related to CC and VIA were tested using 42-item modified HBM self-administered questionnaire and a cross-sectional study design with simple random sampling. Kaiser-Meyer-Olkin and Bartlett's sphericity test indicated that data sampling adequacy for exploratory factor analysis was 0.792 (χ2 = 3189.95, df = 351, p < .001). Items with cross-loading and factor loadings ≥ 0.5 were retained. Confirmatory factor analysis (CFA) was conducted to determine model fit. The final analysis included 194 women, (mean age 30±4.34). Twelve items with ≤ 0.5 were removed and 30 retained items loaded into 6 factors; (benefits of VIA, perceived seriousness of CC, barrier (fear of negative outcome), self-efficacy, susceptibility to CC, and barriers (health system delivery)) explained 65% of the total variance. Cronbach's alpha for the total instrument was 0.8 and reliability for the 6 subscales was 0.76-0.92. Composite reliability and average variance extracted indicated good internal consistency and convergent validity. CFA identified 6 additional items to be removed with high residual covariance. The final 24 items of the modified HBM had an acceptable model fit (goodness-of-fit index (GFI) = 0.861, adjusted GFI = 0.823, comparative fit index = 0.937, root mean square error of approximation = 0.059). The modified HBM for CC and VIA with 24 items had adequate psychometric properties and may be used by Ethiopian healthcare professionals for research or clinical purposes. To support external validity the updated 24 items tool is suggested for application in further study in different populations in Ethiopia.

Research Progress on Deep Learning Based Defect Detection Technology for Solar Panels

INTRODUCTION: Based on machine vision technology to carry out photovoltaic panel defect detection technology research to solve the photovoltaic panel production line automation online defect detection and localization problems.&#x0D; OBJECTIVES: The goal is to improve the accuracy of defect detection on PV cell production lines, increase the speed of defect detection to meet real-time monitoring needs, and improve production efficiency.&#x0D; METHODS: In this paper, three detection methods such as image processing based detection, traditional machine learning based detection and deep learning algorithm based detection are discussed and compared and analyzed respectively. Finally, it is concluded that deep learning based detection methods are more effective in comparison. Then, further analysis and simulation experiments are done by several deep learning based detection algorithms.&#x0D; RESULTS: The experimental results show that the YOLOv8 algorithm has the highest precision rate and maintains good results in terms of recall and mAP values. The detection speed is all less than other algorithms, 10.6ms.&#x0D; CONCLUSION: The inspection model based on yolov8 algorithm has the highest comprehensive performance and is the most suitable algorithmic model for detecting defects in solar panels in production lines.

Combined Anomaly Aware Weakly Supervised Lightweight Model for Surface Defect Inspection

Combined Anomaly Aware Weakly Supervised Lightweight Model for Surface Defect Inspection

Grid Operation and Inspection Resource Scheduling Based on an Adaptive Genetic Algorithm

Grid operation and inspection a key links to ensure the safe operation of the power system, which requires efficient task allocation and resource scheduling. To address this problem, this paper proposes a resource scheduling model for grid operation and inspection based on bi-level programming. Firstly, the O&amp;I process is analyzed and defined as a combined optimization problem of the multiple traveling salesman problem (MTSP) and the job-shop scheduling problem (JSP). Secondly, a bi-level programming model of MTSP and JSP is established according to the characteristics of the problem. Finally, an adaptive genetic algorithm is used to solve the problem. The feasibility of the model and the advancement of the algorithm are verified through the simulation of real scenarios and a large number of tests, which provide strong support for the sustainable development of the power system.

Study of mass and heat transfer for peristaltic transport of cross nanofluid through a curved channel

AbstractNanoparticles have significant applications in drug delivery systems, heat exchanges, treatment of several diseases and chemical reactions. The Cross non‐Newtonian nanofluid model for heat and mass transfer inspection with diethylene glycol‐based fluid is highlighted in this study. The magnesium aluminate nanoparticles are used to evaluate nanofluid characteristics. The current investigation explores the numerical results for magnetohydrodynamics (MHD) peristaltic movement of Cross‐nanofluid via a curved channel under the influences of thermal radiation, heat sink/source and Joule heating. The effects of viscous dissipation, thermophoretic, variable thermal conductivity and Brownian diffusion coefficients in the presence of mass and thermal convection are also taken into consideration. Flow problem is modeled using fundamental conservation laws, and the resulting nonlinear partial differential equations (PDEs) are reduced to a system of nonlinear ordinary differential equations (ODEs) by employing long wavelength and a small Reynolds number approximations. Simplified systems of ODEs are addressed by adopting numerical technique via the NDSolve built‐in command in Mathematica. Furthermore, graphical presentations explain the behavior of relevant flow parameters. Results indicate that better values of the magnetic number and the thermophoresis parameter increase the rate of mass transfer. Results indicate that the nanofluid's temperature increases by improving values of the Brinkman number and falls with the thermal radiation parameter. It is also stated that the nanofluid's velocity reduces near the lower wall for greater values of Hartman number. Moreover, heat transfer rate improves for larger values of the heat generation parameter and curvature parameter.