Precise Inspection Research Articles

Long‐Term Monitoring of ApoE4 within Aqueous Humor Using Intraocular Lenses Containing Target‐Responsive Inverse Opal Hydrogel

Neurodegenerative diseases, including Alzheimer's disease (AD), pose significant challenges to global healthcare because of their irreversible postsymptomatic progression. Conventional radiographic techniques have limitations in early detection, owing to the time lapse between symptom recognition by individuals and precise inspection using medical instruments. Current research explores ophthalmic approaches to address this gap, investigating the physiological link between ocular health and brain diseases. Intraocular lenses (IOLs), widely used in cataract and refractive surgeries, provide a safe and minimally invasive platform for the continuous monitoring of neurodegenerative disease biomarkers, including those associated with AD. By integrating biomolecule‐responsive sensors with IOLs, it becomes feasible to achieve early diagnosis and long‐term, noninvasive monitoring postimplantation. Herein, a fluorescently labeled inverse opal hydrogel (FIOH) is merged with IOLs for ApoE4 detection, a key risk factor for AD. The permeability and inherent photonic properties of FIOH facilitate the cumulative enhancement of the fluorescence signal. In a simulation of the aqueous humor circulation, the detection limit for ApoE4 is determined to be 0.1 nM. Therefore, FIOH‐integrated IOLs hold promise not only for early detection but also for providing a reliable platform for the continuous, noninvasive monitoring of neurodegenerative diseases after the initial, minimally invasive implantation.

Emerging Nano/Microporous Architectures for Food Hazards: New Strategies for Precise Inspection and New Principles for Controllable Regulation.

The big progress of materials science along with chemical engineering and biotechnology has significantly promoted interdisciplinary development, achieving advanced analytical methodologies, improved inspection performance, as well as promising regulation principles for food safety. The very recent progress on nano/microporous architectures for agri-food science, including new strategies for precise inspection and new principles for controllable regulation of food hazards, are summarized and discussed. Major attention is paid to the newly emerged porous architectures with their derivative nano/microstructures contributing to food safety through their instinctive advantages including special material surface, extraordinary porous structure, ease-of-modification, and excellent diversity and variability. This review clearly and logically displays the research road maps and development trends for current food safety issues and give suggestive directions for future outlook as well as the bottleneck problems to be solved, not only smart inspection and analysis but also elimination and control of ever-emerging food hazards.

Mitigation of Parasitic and Drift Capacitance-Induced Nonlinear Current Responses for Stable and Sensitive Perovskite X-ray Detectors.

The development of perovskite direct X-ray detectors shows potential for advancing medical imaging and industrial inspection precision. To ensure the optimal energy conversion efficiency of X-rays for reducing radiation doses, it is necessary for perovskites with thicknesses reaching hundreds of micrometers or even several millimeters to be utilized. However, the nonlinear current response becomes uncertain with such high thicknesses. For instance, the prevailing theory regarding the rapid trapping and release of charges by shallow-level defects falls short in explaining the nonlinear current response observed in high-quality single-crystal samples. Moreover, a significant nonlinear current response can degrade the detection performance. Here, we elucidate peculiar parasitic and drift capacitance-induced nonlinear current responses in perovskites, which arise from bulk structural deficiencies and interface junction width variation in addition to shallow-level defects. Both theoretical analysis and experimental findings demonstrate the effective suppression of nonlinear current responses by establishing bulk heterojunctions and refining interface junctions. Consequently, we have successfully developed highly linear current-responsive detectors based on polycrystalline MAPbI3 thick films. Notably, these detectors achieve a record sensitivity of 2.3 × 104 μC·Gyair-1·cm-2 under 100 kVp X-ray irradiation with a low bias of 0.1 V/μm, enabling enduring and high-resolution X-ray imaging for high-density objects. Successful fabrication and testing of a 64 × 64-pixel flat-panel prototype detector affirm the widespread applicability of these strategies in rectifying nonlinear current responses in perovskite-based X-ray detectors.

Navigating the path from Casing Design to Precision Inspection.

Navigating the path from Casing Design to Precision Inspection.

A novel deep learning algorithm applied to machine vision inspection for surface defects of injection moulded products

In modern industry, the surface defect inspection of injection moulded products is crucial for controlling product quality and optimising the manufacturing process. With the development of optical measurement and computer technology, machine vision inspection methods have been widely adopted instead of manual inspection. However, current machine vision inspection methods are difficult to simultaneously ensure the accuracy and efficiency of surface defect inspection of injection moulded products. Considering this problem, a novel deep learning algorithm applied to machine vision inspection for surface defects of injection moulded products is proposed. To train and evaluate the proposed deep learning algorithm, an image acquisition platform is established and the dataset of surface defects in moulded products is obtained. In the proposed deep learning algorithm, reparameterisation-based convolution modules are employed for feature extraction and feature fusion. A median iterative clustering algorithm based on hierarchical clustering initialisation is proposed to obtain prior anchors that are highly matched with the actual distribution of defect sizes. A novel focus-entire union over covering loss function is utilised for bounding box regression. On these bases, the proposed deep learning algorithm applied to machine vision inspection is evaluated on the dataset of surface defects in moulded products. The experimental results indicate that compared to the traditional inspection algorithms and other deep learning algorithms currently used in machine vision inspection, the proposed deep learning algorithm exhibits superior inspection accuracy and inspection efficiency on the acquired dataset. The inspection precision reaches 0.964, the inspection recall reaches 0.955, and the inference time for each subgraph is only 6.1 ms, confirming its effectiveness.

Back to Michelson interferometer: a precise inspection system for industrial intricate structures defect detection

Quality inspection of injection molding products with intricate three-dimensional structures and diffuse reflection characteristics is a very important procedure in industrial production. However, the current inspection process for these products still heavily relies on visual inspection, which introduces various issues including low efficiency, and missing or false detection. While previous studies have utilized deep-learning methods in conjunction with specific optical sensors and imaging systems to detect defects, the intricate structure of injection molding products and the small magnitude of defects pose significant challenges in defect detection. To address these challenges, this paper proposes an inspection system based on Michelson interferometer capable of detecting and characterizing defects of injection molding products. Notably, by utilizing the light intensity modulation and an improved image differencing approach, this inspection system is capable of detecting defects with a magnitude as small as 0.1 mm and achieving a remarkable detection accuracy exceeding 93% on self-made datasets without utilizing phase information. The effectiveness of our method is validated by comparison with mainstream deep-learning-based defect detection methods and visual inspection methods.

An inspection network with dynamic feature extractor and task alignment head for steel surface defect

High-precision identification and real-time localization for irregular-shaped steel surface defects are crucial for shipbuilding quality control. Although traditional lightweight networks enable real-time defect inspection, the incurred model cannot achieve precise inspection for the defects with large variations in aspect ratios of ship plates. This paper proposes a lightweight inspection network with a dynamic feature extractor and task alignment detection head (INDT) for multi-class steel plate surface defects to address this obstacle. A dynamic structure expansion training strategy based on a re-parameterization multi-branch block is constructed to achieve real-time inspection containing multi-scale information. Furthermore, fed with multi-scale information, the task alignment head with a preprocess for multi-task to concentrate task-oriented features into specific channels. Besides, a soft-weighted sample assignment algorithm with dynamic priors to irregular defects is developed to supervise high-precision model training. The experiments show that the INDT achieves higher precision among all the benchmark methods with lossless accelerating inference.

Multi-view stitching phase measuring deflectometry for freeform specular surface metrology.

Phase measuring deflectometry (PMD) offers notable advantages for precision inspection of specular elements. Nevertheless, if confronts challenges when measuring freeform specular surfaces due to the dispersion of reflection rays from surfaces with high local slopes. Here, we propose a multi-view stitching PMD. It utilizes distinct sensors combining with a screen to capture the appearance of each region. After precisely calibrating the entire system to correct the absolute depth of each region, the appearances of all regions are precisely stitched together, reconstructing the comprehensive appearance of the surface. Through experimental setup, we measured the 3D morphology of a spherical lens with a curvature radius of 155.04 mm and a peak-to-valley (PV) value of 2.9 mm, which yielded a measurement accuracy of 5.3 µm (relative error: 0.18 %). Furthermore, we successfully measured the appearance of a curved mobile phone screen with local slopes ranging from -46.1° to 51.3°, and freeform acrylic sheet with local slopes ranging from -6.7° to 7.7° and a PV value of 5.3 mm.

Response Mechanism of Leaf Area Index and Main Nutrient Content in Mangrove Supported by Hyperspectral Data

Mangrove is the key vegetation in the transitional zone between land and sea, and its health assessment can indicate the deep-level ecological information. The LAI and six key nutrients of mangrove were selected as quantitative evaluation indicators, and the decisive evaluation method of mangrove growth was expected. The mangrove reserve of Dongzhai Port National Nature Reserve in Hainan Province, China, was selected as the study area, with an area of 17.71 km2. The study area was divided into adjacent urban areas, aquaculture areas, and agricultural production areas, and key indicators are extracted from satellite hyperspectral data. The extraction process includes spectral data preprocessing, spectral transformation, spectral combination, spectral modeling, and precision inspection. The spatial distribution of LAI and six key nutrient components of mangrove in the study area were obtained. LAI and Chla need to calculate the index after high-order differentiation of the spectrum; MSTR and Chlb need to calculate the envelope after the second-order differential of the spectrum; TN and TP are directly changed by original or exponential spectrum; the spectral transformation method adopted by TK was homogenization after first-order differential. The results of the strength of nutrient content along the three regions show that there was no significant difference in the retrieval index of mangroves in the three regions, and the overall health level of mangroves was consistent. Chla was the key identification component of mangrove growth and health. The contents of nutrient elements with correlation coefficient exceeding 0.80 include MSTR and TK (0.98), Chla and TP (0.96), Chla and TK (0.87), MSTR and Chla (0.86), MSTR and TK (0.83), and MSTR and TP (0.81). The study quantifies the relationship between different LAI and nutrient content of mangrove leaves from the perspectives of water, leaf biology, and chemical elements, which improved our understanding of the relationship between key components during mangrove growth for the first time.

Precision inspection of transparent component quality

Precision inspection of transparent component quality

Functional Micro-/Nanostructures in Agrofood Science: Precise Inspection, Hazard Elimination, and Potential Health Risks.

Nanotechnology, biotechniques, and chemical engineering have arisen as new trends with significant impacts on agrofood science development. Advanced analytical techniques with high sensitivity, specificity, and automation based on micro-/nanomaterials for food hazard elimination have become leading research hotspots in agrofood science. Research progress in micro-/nanomaterials has provided a solid theoretical basis and technical support to solve problems in the industry. However, the rapid development of micro-/nanostructures has also raised concerns regarding potential risks to human health. This review presents the latest advances in the precise inspection and elimination of food hazards from micro-/nanomaterials and discusses the potential threats to human health posed by nanomaterials. The theoretical reference was provided for the application trend of micro-/nanomaterials in the field of agrofood science in the future.

ANSYS-based three-degree-of-freedom precision inspection platform response surface optimization analysis

ANSYS-based three-degree-of-freedom precision inspection platform response surface optimization analysis

Prediction of Suzhou’s Industrial Power Consumption Based on Grey Model with Seasonal Index Adjustment

The accurate prediction of industrial power consumption is conducive to the effective allocation of power resources by power and energy institutions, and it is also of great significance for the construction and planning of the national grid. By analyzing the characteristics of the data of Suzhou’s industrial power consumption between 2003 and 2005, this paper proposes a grey model with a seasonal index adjustment to predict industrial power consumption. The model results are compared with the traditional grey model, as well as the real value of Suzhou’s industrial power consumption, which shows that our model is more suitable for the prediction of industrial power consumption. The lasted Suzhou’s industrial power consumption data, from 2019–2021, are also investigated, and the results show that the prediction results are in very good agreement with the real data. The highlights of the paper are that all precision inspection indexes are excellent and the seasonal fluctuations in the data changes can be reflected in the present model.

Studies on the Metrological Need and Capabilities of 3D Scanning Technologies

3D scanning technologies are deployed toward developing a digital 3D model for Additive Manufacturing (AM) applications. It collects data, turning it into a 3D model that uses designated 3D printing processes. Many scanners, ranging from low-cost alternatives to professional series that are far more accurate and reliable, are now available to assist in bringing designs to reality. 3D scanning solutions enable the appropriate measurement of 3D physical parts into the virtual world, allowing factory production teams and corporate offices to share critical design information. These techniques are utilized everywhere in the design process, including product design and development, reverse engineering, quality control and quality assurance. The manufacturing sector can decrease costs while accelerating time to market and resolving quality issues. This study investigates the metrological need as per the advancements of 3D scanners. The procedural steps of the 3D scanners, along with specific metrological components and soft tools for 3D scanning, are discussed briefly. Finally, various 3D scanning applications are identified and discussed in detail. Because of the overall relative advantages of these non-contact measurement techniques, 3D metrological tools are crucial for modern production. Almost every sector aims for smaller, more complex components, which are more vulnerable to contamination or injury from even the slightest touch with a probe. The market is driven by global Research and Development (R&D) investment to develop game-changing technologies and solutions. Precision inspection and quality control are significant market drivers for industry progress. Smart factories will have lifetime access to 3D metrological data, allowing them to enhance quality and gain a competitive advantage in the marketplace.

Defect Detection of Metro Wheel Set Tread Based on Image Recognition

In the operation of railway vehicles, the quality of bogies directly affects the operation quality and driving safety. Wheel set is one of the most important components in bogie, so the maintenance of wheel set is very important. For a long time, the detection of train wheel sets in China is still in the stage of manual measurement with backward technology and low efficiency. A new automatic detection method of wheel flange tread based on fuzzy neural network image processing technology is proposed in this paper. This method can accurately detect the defects of wheel flange tread. It collects the original image of the tested wheel set through the digital camera, inputs it into the computer, through certain calculation and processing, and compares it with the model established based on fuzzy neural network, so as to detect the defects of wheel flange and tread. First, the research status of wheel tread defect detection is summarized. Second, the basic principles of digital image technology are studied, the image processing models are confirmed, and the image processing method based on fuzzy neural network is established. Finally, eight wheel set treads are selected to carry out defect detection, and the analysis results show that the proposed method can obtain the better inspection precision.

Optimization Design of Multi-UAV Communication Network Based on Reinforcement Learning

In recent years, due to the application of high-definition video codec technology, high-precision satellite navigation technology, mobile base station positioning technology, and broadband technology, the performance of UAVs has been greatly improved. In the military field, drones have become an important weapon alongside missiles on the battlefield. In the future, military drones will perform strategic missions such as battlefield reconnaissance and long-range destruction. Outside the military field, DJI’s Zenmuse series drones are used for filming, MG series drones are used for pesticide spraying, and Beijing Zhonghangzhi unmanned helicopters are used for geological surveys, precise inspection of power lines, and maritime law enforcement. With the continuous improvement of technical specifications, UAV communication technology requires further research and development. This paper has conducted research experiments on the optimization of multi-UAV communication network based on reinforcement learning. The experimental data show that it is marked as the AoI value corresponding to the completion of a certain self-task. It can be seen that the final AoI of the communication trajectory of reinforcement learning is 115, and the AoI greedy strategy finally obtains AoI of 140 seconds, achieving about 18% of the total AoI reduce, which effectively improve the performance of the system. From the above data, the research of reinforcement learning method has great benefits for the development of UAV communication.

Skeleton Curve-Guided Five-Axis Sweep Scanning for Surface With Multiple Holes

Although surface with multiple holes (SMHs) is widely used in industrial components, the precise inspection of SMH is a knotty task due to the inefficiency of traditional surface inspection technology. Recently, a five-axis surface sweep scanning approach is emerging and shows great potential to boost the surface inspection efficiency by sensing surface in a continuous sweep scanning way. However, an efficient sweep scanning path should cater to the unique kinematic characteristics of the five-axis inspection system. In this article, we present an approach to automatically generate efficient five-axis sweep scanning paths for inspecting SMH. First, the skeleton of SMH is extracted along with the surface partitioned into several patches, based on which a guiding curve-based sweep scanning path can be defined for each surface patch. By modeling the skeleton of SMH as a directed Euler graph and finding a proper sequence to traverse the graph, a continuous five-axis sweep scanning path is then generated to sweep all patches without any transition among them. The nonsweeping time could be eliminated so that the sweep scanning efficiency is drastically improved in this way. Simulation and physical inspection experiments are conducted on two SMHs, showing that our method significantly outperforms existing approaches, such as the popular zigzag method and the method from the leading commercial software of RENISHAW. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article aims to generate an efficient sweep scanning path of a five-axis coordinate measuring machine (CMM) for inspecting the SMHs of a precision component, such as an engine block or a gearbox cover. Although traditional five-axis CMM path generation methods can be applied to SMH inspection, most of them are designed for the point-by-point inspection mode, which intrinsically suffers from extremely low efficiency. Recently, an emerging five-axis surface sweep scanning technology shows great potential for boosting the efficiency of surface inspection by using a continuous surface sensing stylus mounted on a rapid rotary probe head. The realization of high-speed five-axis surface scanning depends on a proper sweep scanning path which takes advantage of the superior kinematic performance of the rotary probe head as much as possible and accordingly avoids assigning heavy workload to the three linear axes with weaker kinematic performance. However, up to date, only a handful of five-axis sweep scanning path planning methods are reported and mainly focus on the simple and compact surface. Therefore, existing methods are incompetent to generate a suitable sweep scanning path for high-speed inspection of SMH. For these reasons, we present a novel and practical approach to automatically generate a continuous five-axis sweep scanning path for complex SMH. The generated path exactly meets the unique kinematic requirements of high-speed five-axis inspection and eliminates all noninspection operations. In this way, the SMH inspection can be accomplished with high efficiency.

Path Planning and Control of a UAV Fleet in Bridge Management Systems

Traditional methodologies for precise inspection of bridges (pavement, beams, column cap, column, joints and inside box girder, etc.) with By-bridge equipment, Aerial Work Platform (AWP) or via ropes have several limits that can be overcome by using Unmanned Aerial Vehicles (UAVs). The constant development in this field allows us to go beyond the manual control and the use of a single UAV. In the context of inspection rules, this research provides new inputs to the multilevel approach used today and to the methods of structural inspection with drones. Today, UAV-based inspections are limited by manual and/or semi-automatic control with many restrictions on trajectory settings, especially for areas of difficult access with Global Navigation Satellite Systems (GNSS) denied that still require the intervention of a human operator. This work proposes the use of autonomous navigation with a fleet of UAVs for infrastructural inspections. Starting from a digital twin, a solution is provided to problems such as the definition of a set of reference trajectories and the design of a position controller. A workflow to integrate a generic Bridge Management System (BMS) with this type of approach is provided.

Rapid Inspection of Large Concrete Floor Flatness Using Wheeled Robot with Aided-INS

Flatness is an important parameter for the quality assessment of concrete floors. Traditional flatness inspection methods have problems with sparse sampling and low efficiency for large concrete floors. In this paper, a rapid flatness inspection method for large concrete floors based on a wheeled robot with an aided inertial navigation system (INS) is proposed. The robot realizes high precision relative to the three-dimensional profile measurement of concrete floors through fusion of INS, odometers and total station. The overall measurement of concrete floor flatness is realized through a certain density of profiles. The measurement performance of the proposed method has been tested in laboratory, and the effectivity is tested in the flatness inspection of the concrete base of an ice floor in the National Speed Skating Oval of 2022 Beijing Winter Olympic Games. The results demonstrate that the floor flatness inspection accuracy can meet the requirement of ±0.5 mm over 5 m and the efficiency is several times that of the traditional method. This technology is promising for high precision and rapid flatness inspection of large floors.

Detection Method of Photovoltaic Panel Defect Based on Improved Mask R-CNN

&lt;p&gt;To solve the low efficiency and precision of uncrewed inspection in photovoltaic power stations, a segmentation method of improving the defective photovoltaic panels based on improved Mask R-CNN is proposed. The atrous spatial pyramid pooling and spatial attention mechanism were introduced into the extraction network to improve detection accuracy. Uncrewed aerial vehicle infrared video of the panels is used to input the network model for defect detection. As a result, the automatic annotation of the defect position is achieved, significantly improving the efficiency and precision of uncrewed inspection. The precision, recall and FPS are used as performance metrics to evaluate U-Net, PSPNet, Mask R-CNN and the algorithm in this paper. Experiments show that the detection precision of the four models are 77.3%, 82.2%, 84.0% and 89.8% respectively, the recall are 79.4%, 79.0%, 81.6% and 84.4% respectively, and the FPS are 12.5, 9.6, 8.6 and 8.2 respectively. Although the FPS of this algorithm is slightly reduced, the precision and recall have been greatly improved, and can be applied to industry.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;