Inspection Requirements Research Articles

Dental Anesthesia Guidelines and Regulations of US States and Major Professional Organizations: A Review.

This summary reviews guidelines and regulations pertaining to dental anesthesia across the United States, including guidelines of the American Dental Association (ADA), American Association of Oral and Maxillofacial Surgeons (AAOMS), and American Society of Anesthesiologists (ASA). The analysis addresses a range of requirements, including definitions of anesthesia and requirements for training and certification across professional societies and the 50 US states, with a focus on office-based settings. Strikingly, substantial variation exists among state rules and regulations and the ADA, AAOMS, and ASA guidelines with implications for variations of care, outcomes, and patient safety. We examined definitions of sedation and general anesthesia, patient selection and evaluation, fasting requirements, emergency preparedness, drug administration, monitoring, equipment, procedure selection, education and training requirements, permit requirements, life support certifications, reporting of adverse events, and inspection requirements. This comprehensive analysis serves to educate dental practitioners and office staff, patients, primary care providers, and state dental boards about the regulatory landscape of dental anesthesia. Our findings establish a foundation for future research and policy development aimed at improving consistency, best practices, and safety in dental anesthesia practices.

Analysis of a third‐party action incident in a gas pipeline: A case study of the Cuiabá Lateral Pipeline in Brazil

AbstractThe impact of third‐party actions remains a primary cause of induced failures in transportation systems. In response, the operational safety management system needs continual revision and robust implementation to mitigate such risks effectively. This study goes through the analysis of an incident involving the rupture of a gas pipeline, specifically examining the event that happened in January 2021 along the Cuiabá Lateral Pipeline in Mato Grosso, Brazil. A comprehensive comparative evaluation is undertaken, contrasting the investigation taken by the pipeline operator, and the analysis conducted by the regulatory body. Within the Brazilian context, operators are mandated to adhere to the Technical Regulation of Onshore Oil and Gas Pipelines (RTDT), a regulatory framework grounded in process safety, encompassing requirements for inspection, community engagement, and incident investigations. The regulator assumes an important role in evaluating the investigations carried out by operators. This research sheds light on the divergent perspectives surrounding the management system, depending on whether it is viewed from the operator's lens or the regulator's standpoint. operators tend to emphasize community actions, whereas regulators focus on the efficacy of preventive measures against third‐party actions, underscoring the significance of inspection frequency.

Artificial Intelligence in Quality Control Systems: A Cross-Industry Analysis of Applications, Benefits, and Implementation Frameworks

This article presents a comprehensive analysis of artificial intelligence applications in quality control across manufacturing, service, and infrastructure maintenance sectors. The article examines how AI-driven systems are transforming traditional quality control processes through automated defect detection, real-time monitoring, and adaptive testing methodologies. Through systematic review of industry implementations and case studies, we investigate the impact of machine learning algorithms, computer vision systems, and deep learning applications on quality assurance processes. The findings demonstrate significant improvements in inspection accuracy, reduction in manual inspection requirements, and enhanced detection of subtle defects across various industrial applications. The article reveals that AI-driven quality control systems offer substantial benefits in terms of operational efficiency, cost reduction, and quality consistency, while also identifying key implementation challenges such as initial infrastructure requirements, data quality concerns, and workforce adaptation needs. Additionally, the article provides insights into emerging trends and future opportunities for AI integration in quality control systems, contributing to the broader understanding of Industry 4.0 implementation strategies. This work serves as a foundational reference for organizations considering AI implementation in their quality control processes and provides a framework for evaluating the potential benefits and challenges across different industrial contexts.

Improved Algorithm for Vehicle Bottom Safety Detection Based on YOLOv8n: PSP-YOLO

The safety inspection system for the underside of vehicles demands both high detection speed and accuracy, necessitating a model with small parameters and high network efficiency. To address these issues and improve the real-time performance of under-vehicle safety inspection, the YOLOv8n object-detection algorithm was enhanced, resulting in the PSP-YOLO algorithm. Firstly, the improved PSP-YOLO network introduces a P2 small-object detection head, which is particularly effective in detecting tiny objects, such as small dangerous objects under vehicles, thereby significantly enhancing the ability to detect such small targets. Secondly, the Space-to-Depth Conv (SPD-Conv) module was introduced into the backbone network, which improves the detection performance of low-quality samples and small objects while ensuring high precision and accurate localization of small targets in images. Finally, the Partial Convolution (PConv) lightweight module was also added to the feature fusion network, effectively reducing the model’s parameters and computational load, conserving computing resources, and improving detection speed and accuracy. Experimental results demonstrate that the modified model increased mAP@0.5 by 3.9% and mAP@0.5-0.95 by 3.6% on the dataset used, reduced the number of parameters to 2.53 M, and achieved a detection speed of 104.6 f/s. Therefore, the improved YOLOv8n algorithm significantly enhances the detection performance of dangerous objects, meeting the high-precision and real-time requirements for vehicle underside safety inspections.

A novel BP-GA based autofocus method for detection of circuit board components

Optical micro-inspection systems use different focusing methods depending on the inspection requirements of different scenarios. In practical industrial micro-inspection, grid samples have a wide variety of components, for example, electronic components on circuit boards and transistors on electronic chips. Changes in the surrounding environment (e.g., brightness of light, flatness of the platform, and temperature, etc.) during the inspection of these processed parts may lead to out-of-focus of the object under the microscope. Therefore, this paper proposes an autofocus algorithm to cope with the complex environment during inspection. The algorithm is based on feature vectors reflecting the external geometry of the spot and the internal energy distribution, and is combined with a back-propagation neural network with a genetic algorithm (GA) to enhance the focusing capability of the optical microscope. Preliminary numerical test results show that because of the bias problem in the focusing system, the accuracy of the neural network in calculating the defocused amount (DA) is significantly improved, despite the pitfalls of its generalization ability and the possibility of endless loops during the focusing process. In order to further solve the pitfalls of neural networks, this paper introduces a full reference image evaluation model into the optical microscope system and finally develops the autofocus software. Focusing tests using the developed software for the inspection of real components demonstrate that the introduced full reference image evaluation model not only expands the focusing distance of the inspection system, but also prevents the autofocus algorithm from falling into a dead loop.

Study on Factors to Ensure Education Quality at University of Sciences - Thai Nguyen University

Research and collect feedback from staff and students on the quality of educational factors, thereby making necessary adjustments to help improve and meet the requirements of program quality inspection. Training and checking the quality of educational and training facilities in the University of Sciences - Thai Nguyen University (TNUS), The research was conducted through a survey to get feedback from officials and lecturers on quality assurance factors at the school. Data collected from 240 officials and lecturers in the school year 2023 - 2024, using SPSS 22.0 software for analysis. Research results show that there are 7 factors affecting quality assurance at the University of Sciences - Thai Nguyen University, including 7 main contents (with 29 evaluation criteria). The survey results are synthesized and summarized specifically to provide recommendations that contribute to improving quality assurance conditions and enhancing the quality of educational and training activities of the School.

A method for enhancing near-mirror object detection by integrating AWCS and CBAM into ResNet18

ABSTRACT Defects, such as scratches, frequently arise during the manufacturing of near-mirror workpieces, adversely affecting both their longevity and aesthetic appeal. The identification of these near-mirror defects through machine vision is fraught with challenges, including the scarcity of comprehensive datasets, variability in surface curvature, and the diminutive size of the defects. In response to these challenges, this study integrates Visual Parser and deep learning methodologies for the visual detection of near-mirror defects. A dataset conducive to deep learning was developed utilising a sliding window approach. The proposed model incorporates an Adaptive Weighted Convolution Structure (AWCS) and Convolutional Block Attention Module (CBAM) within the ResNet18 architecture. Additionally, we introduce an efficient and precise technique termed Visual Parser, which facilitates the detection and localisation of defect areas in near-mirror workpieces. When combined with a classification model, the Visual Parser allows for the online inference and detection of extensive near-mirror images. Experimental findings indicate that the performance metrics of the proposed ResNet18-AWCS-CBAM (ResNet18-ASCM) model significantly exceed those of established networks. The defect detection and localisation method based on Visual Parser is capable of fulfilling the online inspection requirements pertinent to the production of near-mirror workpieces.

Simulation of fatigue crack growth in aluminium alloy 7075-T7351 under spike overload and aircraft spectrum loading

The trend towards virtual testing and digital-twin assisted management means that the accurate and reliable simulation of fatigue crack propagation behaviour is more important than ever. Reliable but conservative approaches to support this are in widespread use in the aerospace industry. Nevertheless, the conservatism comes at a significant cost in terms of reduced structural life and an increased ongoing inspection requirement and, as such leads to questions about the economic burden of these approaches. Recent comparisons between blind predictions and test results revealed the extent of the issue for cracking in aluminium alloy 7075-T7351 coupons with configuration and loading representative of military transport aircraft wing skins. The current models were generally conservative by a factor of two or more in terms of crack propagation life. This suggested that there was significant scope to improve the modelling to better reflect all the complex contributing factors. The current work has investigated the issue of changes in the crack front constraint as the crack progresses from a state of high constraint (close to plane strain) to a lower constraint (approaching plane stress). This issue was investigated both experimentally and with the development of an improved analytical model. A test program was conducted on several specimens, loaded under constant-amplitude, constant-amplitude with spike-overloads and a variable amplitude spectrum. Crack-opening stress levels were measured at key points in the tests and the results were used to develop and evaluate improved modelling approaches. The improved model was generally able to predict crack growth within about ± 30 % of that demonstrated along with the correct form of the crack growth, which is a significant advance and will lead to reduced costs and increased safety.

A Novel Defect Quantification Method Utilizing Multi-Sensor Magnetic Flux Leakage Signal Fusion

In the assessment of pipeline integrity using magnetic flux leakage (MFL) detection, it is crucial to quantify defects accurately and efficiently using MFL signals. However, in complex detection environments, traditional defect inversion methods exhibit low quantification accuracy and efficiency due to the complexity of their algorithms or excessive reliance on a priori knowledge and expert experience. To address these issues, this study presents a novel defect quantification method based on multi-sensor signal fusion (MSSF). The method employs a multi-sensor probe to fuse the MFL signals under multiple lift-off values, enhancing the diversity of defect information. This enables defect-opening profile recognition using the characteristic approximation approach (CAA). Subsequently, the MSSF method is based on a 3D magnetic dipole model and integrates the structural features of multi-sensor probes to develop an algorithm. This algorithm iteratively determines the defect depth at multiple data acquisition points within the defect region to obtain the maximum defect depth. The feasibility of the MSSF quantification method is validated through finite element simulation and physical experiments. The results demonstrate that the proposed method achieves accurate defect quantification while enhancing efficiency, with the number of iterations for each defect depth calculation point consistently requiring fewer than 15 iterations. For rectangular metal loss, perforation, and conical defects, quantification errors are less than 10%, meeting practical inspection requirements.

Novel intelligent defects detection of boiler water walls in thermal power plants based on OFM_SSD

The boiler is a critical component of conventional thermal power systems, where surface flaws in boiler water walls can significantly compromise safety and availability, potentially leading to substantial loss of life and property. Traditional detection methods, whether manual or based on machine learning, often prove costly, inefficient and time-consuming, failing to meet the stringent requirements for water wall inspection. Therefore, a novel surface defect detection model integrating an improved single shot multibox detector (SSD) with the optical flow method (OFM) (OFM_SSD) is proposed. The OFM enhances data sample diversity by augmenting the dataset derived from thermal power plants, while the incorporation of deconvolution techniques improves the model receptive field, thereby enhancing its ability to detect and classify small defects. Comprehensive experiments demonstrate that the OFM_SSD outperforms several existing models including the SSD model based on traditional expanded datasets (T_SSD), you only look once (YOLO), ordinary SSD, Regions with the CNN(R_CNN), and Deconvolution-only SSD (DSSD) in terms of accuracy in defect localization and classification. This advancement of the OFM_SSD not only reduces operational costs but also enhances detection capabilities, ultimately contributing to safer and more efficient operations within thermal power plants.

A customised ConvNeXt-SCC network: integrating improved principal component analysis with ConvNeXt to enhance tire crown defect detection

ABSTRACT Defects such as bubbles and rubber shortages occasionally occur during vehicle tire production. Tiny defects in the crown gradually expand under load, ultimately causing tire failure. However, machine-vision-based crown defect detection faces challenges like limited datasets, varying groove patterns, low colour contrast, and small defect sizes. Therefore, this article integrates principal component analysis (PCA) and deep learning techniques for the visual detection of tire crown defects. A dataset of tire crown defects suitable for deep learning was constructed using an improved-PCA method and composite sampling. We propose the ConvNeXt-Shallow Conv CBAM (ConvNeXt-SCC) model, which includes an improved convolutional block attention module (CBAM) in a shallow stacked structure. And we propose an efficient and accurate method called Improved Selective Search (ISS) to detect and locate tire crown defective regions. The ISS pre-selected box generation method, enhanced with a priori knowledge filtering module, was used alongside a classification model to achieve online inference detection of large-size tire crown images. The experimental results show that the performance indices of the proposed ConvNeXt-Shallow Conv CBAM (ConvNeXt-SCC) model significantly improve compared to the original ConvNeXt-T. The defect detection and localisation method based on ISS meets the online inspection requirements of tire production.

DESIGN TASK IN THE MODERN CONDITIONS OF INVESTMENT AND CONSTRUCTION PROJECTS

The article is devoted to the organizational aspect of solving the urgent problem of shortening the terms of implementation of investment and construction projects in modern conditions at the stage design The article examines the procedure for drawing up a design task by the customer at the stage of preparing tender documentation for construction objects in Ukraine, the financing of which is carried out with the involvement of budget funds, funds of state and communal enterprises, institutions and organizations, loans provided under state guarantees and defined and disadvantages The provisions of regulatory and technical documents regulating the procedure for drawing up a design assignment and its content have been reviewed. The list of problems that arise at the design and construction stage during the implementation of investment and construction projects due to the uncertainty and variability of data for project development, provided by the customer in the design task, is defined. The analysis of open sources of information regarding the procurement of design works showed that customers fill out the design task form, defined by the regulatory framework, on a formal basis, without actually expressing their real requirements for the future construction inspection. In "design and build" projects, the design task occupies a special place, since the contractor, on the basis of the customer's technical task, can determine not only the cost of the design work, but also the price of the construction work for which he is willing to perform it, while this price depends on technical and economic parameters of the building, which has not yet been designed, from the applied finishing materials, engineering systems, structural solutions, etc. An analysis of completed contracts for the purchase of construction works on "design and build" schemes was carried out, and they were divided into groups depending on the sufficiency of information in the design task. The foreign experience of organizing design works was analyzed, in particular, when implementing the "design and build" approach. It is proposed to improve the organization of the design process by separating the process of drawing up the design task into a separate procedure with a toolkit based on digital technologies, formulating the customer's requirements for design solutions and checking the validity of the customer's requirements. The feasibility of developing a toolkit based on digital technologies, which can be applied by the customer for drawing up a design task in investment and construction projects.

Chip Surface Character Recognition Based on OpenCV

Chip surface character recognition is an important part of quality inspection in the field of microelectronics manufacturing. By recognizing the character information on the chip, automated production, quality control, and data collection and analysis can be achieved. This article studies a chip surface character recognition method based on the OpenCV vision library. Firstly, the obtained chip images are preprocessed. Secondly, the template matching method is used to locate the chip position. In addition, the surface characters on the chip are individually segmented, and each character image is extracted separately. Finally, a Support Vector Machine (SVM) is used to classify and recognize characters. The results show that this method can accurately recognize the surface characters of chips and meet the requirements of chip quality inspection.

An automatic robot for ultrasonic partial discharge detection of gas-insulated switchgear

PurposeGas-insulated switchgear (GIS) stands as a pivotal component in power systems, susceptible to partial discharge occurrences. Nevertheless, manual inspection proves labor-intensive, exhibits a low defect detection rate. Conventional inspection robots face limitations, unable to perform live line measurements or adapt effectively to diverse environmental conditions. This paper aims to introduce a novel solution: the GIS ultrasonic partial discharge detection robot (GBOT), designed to assume the role of substation personnel in inspection tasks.Design/methodology/approachGBOT is a mobile manipulator system divided into three subsystems: autonomous location and navigation, vision-guided and force-controlled manipulator and data detection and analysis. These subsystems collaborate, incorporating simultaneous localization and mapping, path planning, target recognition and signal processing, admittance control. This paper also introduces a path planning method designed to adapt to the substation environment. In addition, a flexible end effector is designed for full contact between the probe and the device.FindingsThe robot fulfills the requirements for substation GIS inspections. It can conduct efficient and low-cost path planning with narrow passages in the constructed substation map, realizes a sufficiently stable detection contact and perform high defect detection rate.Practical implicationsThe robot mitigates the labor intensity of grid maintenance personnel, enhances inspection efficiency and safety and advances the intelligence and digitization of power equipment maintenance and monitoring. This research also provides valuable insights for the broader application of mobile manipulators in diverse fields.Originality/valueThe robot is a mobile manipulator system used in GIS detection, offering a viable alternative to grid personnel for equipment inspections. Comparing with the previous robotic systems, this system can work in live electrical detection, demonstrating robust environmental adaptability and superior efficiency.

Fast inspection of flange faces of welded tubes using photogrammetry and auxiliary measuring tools

The attitudes (including positions and orientations) of flange faces on a multi-branch welded tube are critical to tube installation quality. However, it lacks appropriate approach for flange attitudes inspection. Given the inspection requirements, a novel Auxiliary Measuring Tool (AMT) is designed. A photogrammetry-based approach is put forward to inspect the attitude errors of the flange faces with the aid of the AMT. The specifically designed AMT can achieve flange face centering through a three-jaw chuck structure and establish in-plane rotation by employing a slider to locate the reference bolt hole. An ingenious calibration method is proposed to establish the AMT coordinate frame (ACF) and obtain the marker points’ coordinates in ACF. With the calibrated AMTs attached to each flange, the flange attitude error can be efficiently evaluated by simply capturing a set of images of the tube. Experiments on two welded tubes demonstrate the high performance of the proposed method.

An Improved YOLOv5x-Based Algorithm for IC Pin Welding Defects Detection

This study suggests an integrated circuit (IC) pin welding defect detection algorithm based on improved YOLOv5x to address the issues of low detection accuracy caused by small target size and dense pin arrangement in IC pin welding defects identification. The ability of the network to extract features is improved by effective fusing of features with various receptive fields through the inclusion of the D-SPP module to merge different channel information. The introduction of the mask self-attention mechanism module increases the network’s capacity to recognize global feature correlations and raises the algorithm’s detection precision. In order to speed up the model’s convergence and tackle the issue of sample imbalance in BBox regression, the Focal-EIoU loss function is applied. The detection accuracy and speed are increased by using the k-means++ clustering algorithm to create nine clustering centers to figure out the size of the prior box. According to the results of the experiment, the new method achieves average precisions for short-circuit, missing pin, pin-cocked, and little tin faults in IC pin welding of 96.7%, 94.5%, 95.6%, and 93.3%, respectively. The mean average precision increases to 95.0% at a threshold of 0.5, which is 13.3% and 8.9% greater than YOLOv3 and YOLOv5x, respectively. A reference value for IC pin welding defect identification is provided by the improved algorithm, which has a detection time of 0.142 seconds per image. This meets the speed requirements of IC quality inspection.

A high-accuracy single-frame 3D reconstruction method with color speckle projection for pantograph sliders

The carbon slider of the pantograph is a crucial component in the high-speed rail power system, and regular maintenance is of great significance. 2D-based approach is susceptible to inaccuracies caused by defects, rainwater, stains, and other factors. Due to the weak texture and uneven reflectance of the slider, it is difficult to obtain accurate 3D information through a single-frame approach. To address this issue, this paper proposes a high-accuracy 3D imaging method for sliders, successfully resolving the long-standing problem of low reconstruction accuracy and limited generalization in pantograph reconstruction. Firstly, high-precision disparity maps of industrial component are generated using the binocular Gray Code phase-shifting method as the Ground Truth, and the color speckle pattern is subsequently utilized as the input for the model. Secondly, we propose a split-cost volume approach that achieves cost aggregation using only 2D decoding-encoding operations. Furthermore, disparity regression is utilized to provide the initial disparity for the GRU(Gated Recurrent Unit), thereby accelerating the convergence speed of the model. Quantitative experiments of optical standard sphere validated that the proposed method could achieve 3D reconstruction with a radius error of no more than 0.07 mm. The pantograph reconstruction results showed that the average absolute error of the carbon slider did not exceed 0.21 mm under reasonable exposure time and the working distance at which focus has been achieved, which meets the requirements of industrial inspection.

DEW-YOLO: An Efficient Algorithm for Steel Surface Defect Detection

To address the current steel surface defect detection algorithms in practical applications involving low detection accuracy, an efficient and highly accurate strip steel surface defect detection algorithm, DEW-YOLO, is proposed in this paper. Firstly, by combining the advantages of deformable convolutional networks (DCNs), this paper innovates the C2F module in YOLOv8 and proposes a C2f_DCN module that can flexibly sample features to enhance the abilities of learning and expressing defect features of different sizes and shapes. Secondly, the explicit visual center (EVC) is introduced into the backbone network, which enhances feature extraction capabilities and adaptability and enables the model to better adjust features at different levels and scales. Finally, the original loss function is replaced with the Wise-IoU (WIoU) loss function to accurately measure the similarity between the target frames and improve the defect detection performance of the model. The experimental results on the NEU-DET dataset demonstrate that the algorithms proposed in this paper achieved a mean average precision (mAP) of 80.3% in steel surface defect detection tasks, which was a 3.9% improvement over the original YOLOv8 model. The model’s inference speed reached 91 frames per second (FPS). DEW-YOLO effectively enhances the accuracy of steel defect detection and better satisfies industrial inspection requirements.

Epizootological and epidemiological situation of anthrax in Siberia and the Far East (1985–2023)

Anthrax remains relevant in the world and in the Russian Federation. In Siberia and the Far East, epizootological, epidemiological, environmental and microbiological monitoring of anthrax is constantly carried out. To analyze the epizootological and epidemiological situation of anthrax, we used the information from veterinary institutions and Rospotrebnadzor for 24 regions, as well as reference and archive materials. On the territory of Siberia and the Far East for the period from 1985 to 2023, pronounced epizootological and epidemiological problems of anthrax are observed in the Yamalo-Nenets Autonomous Okrug, the Republic of Buryatia, Omsk Region and the Altai Territory. Over the last five-year observation period, high epizootic activity has been observed in the west of the Republic of Tyva. A total of 3,375 livestock animals and 128 people fell ill. The anthrax database contains information on 7,491 stationary anthrax-infected points in 22 subjects and 562 anthrax burials and cattle burials in 17 subjects of Siberia and the Far East. B. anthracis strains have slight variability in biological properties and belong to two global genetic lines – A and B. In order to prevent anthrax, it is necessary to ensure surveillance of soil foci of anthrax (anthrax burials and cattle burials, “plague fields”), a high level of livestock animals registration and their specific vaccination coverage; to control the implementation of veterinary and sanitary requirements for pre-mortem inspection and emergency slaughter of livestock, the sale of meat and meat products, and biological waste disposal; to carry out explanatory work among the population.

Prediction of water absorption of recycled coarse aggregate based on deep learning image segmentation

Abstract To quickly measure the water absorption (WA) of Recycled Coarse Aggregates (RCA), we utilize a detection platform designed for RCA to collect two-dimensional images. Utilizing the RCA-net network, we segment the areas of the mortar and aggregate on the RCA surface. Segmentations allow us to extract critical parameters for characterizing the quality of RCA, the proportion of mortar area (PMA). Subsequently, we construct three regression functions between PMA and WA. The experimental results demonstrate that our proposed segmentation method effectively separates both adhered particles of RCA and distinct areas of mortar and aggregate on RCA surfaces. Next, sprinkling water on RCA surfaces can enhance the accuracy of the segmentation. Notably, within particle size ranges of 5–10 mm, 10–20 mm, and 20–31.5 mm, we all observed a significant linear relationship between PMA and WA. We used those linear relationships and the equivalent mass of RCA detected by the image method in each particle size range to construct the prediction model of water absorption. According to the validation result of 24 groups RCA, this model’s maximum relative error of RCA water absorption predicted value was 10.6 %. The detection time of this method is short, and the detection time of 2 kg RCA is 3.8 min, with an average computation time per image of merely 0.659 s. This efficiency fulfills the requirements for real-time industrial inspection.