Inspection Platform Research Articles

Design of servo drive system for machine vision inspection platform

With the more application of machine vision technology in production practice, most machine vision systems are based on passive vision to measure the target, which has some limitations. Based on the requirements of machine vision platform, a three-dimensional servo movement scheme based on active positioning vision is proposed in this paper. In this paper, the parts of the servo drive system of the platform are selected, calculated and checked, the three-dimensional modeling of the machine vision platform is completed in SolidWorks, and the motion simulation of the servo control system is carried out.

An Optical Smartphone-Based Inspection Platform for Identification of Diseased Orchids.

Infections of orchids by the Odontoglossum ringspot virus or Cymbidium mosaic virus cause orchid disfiguration and are a substantial source of economic loss for orchid farms. Although immunoassays can identify these infections, immunoassays are expensive, time consuming, and labor consuming and limited to sampling-based testing methods. This study proposes a noncontact inspection platform that uses a spectrometer and Android smartphone. When orchid leaves are illuminated with a handheld optical probe, the Android app based on the Internet of Things and artificial intelligence can display the measured florescence spectrum and determine the infection status within 3 s by using an algorithm hosted on a remote server. The algorithm was trained on optical data and the results of polymerase chain reaction assays. The testing accuracy of the algorithm was 89%. The area under the receiver operating characteristic curve was 91%; thus, the platform with the algorithm was accurate and convenient for infection screening in orchids.

Enhancing a flexible robotic spraying platform for distant plant inspection via high-quality thermal imagery data

The recent developments in electronics have made available a variety of systems at very appealing quality, cost and size levels. These conditions are not left unexploited by modern agriculture that seeks to tackle the increased global food demand and the degradation of available resources. Indeed, the precision agriculture sector is flourishing assisted by outstanding innovations, like the UAVs, that can carry a plethora of sensors. It must be noted though that, despite the progress being achieved, several high-quality measurements still demand expensive, often pre-existing but heavy instruments, and meticulous time-consuming operations. The latter requirements are not always in-line with the flight duration and cargo weight limitations of a typical UAV. This gap is apparent when scientists have to apply precise thermal imagery methods to support actions like accurate plant inspection or early disease detection. In order to provide a satisfactory solution, an experimental electric robotic platform, mainly destined for spraying operations, was enhanced so as to improve its functions and to host a high-quality thermal/optical camera. This platform has been equipped with a set of complementary electronic systems so as to provide fluent and robust semi-autonomous remote monitoring operation. The overall layout is lightweight, eliminating soil compaction, reducing vibration and thermal footprint, guarantying almost non-invasive operation and image capturing at very low noise levels. The whole approach is targeting to combine innovative but easy-to-find assistive components of affordable cost in local markets, emphasizing the challenges towards a really functioning solution accompanied by detailed performance data. The overall study is targeting the rapidly growing robotic vehicles market. The proposed platform is following the modern sustainability objectives as it is fully electric and can be assisted by solar panels, for increased autonomy. Finally, insight is given for future improvements and exploitation.

Aerial Robotic Solution for Detailed Inspection of Viaducts

The inspection of public infrastructure, such as viaducts and bridges, is crucial for their proper maintenance given the heavy use of many of them. Current inspection techniques are very costly and manual, requiring highly qualified personnel and involving many risks. This article presents a novel solution for the detailed inspection of viaducts using aerial robotic platforms. The system provides a highly automated visual inspection platform that does not rely on GPS and could even fly underneath the infrastructure. Unlike commercially available solutions, our system automatically references the inspection to a global coordinate system usable throughout the lifespan of the infrastructure. In addition, the system includes another aerial platform with a robotic arm to make contact inspections of detected defects, thus providing information that cannot be obtained only with images. Both aerial robotic platforms feature flexibility in the choice of camera or contact measurement sensors as the situation requires. The system was validated by performing inspection flights on real viaducts.

Extreme Ultraviolet Photoresist Inspection Platform in Shanghai Synchrotron Radiation Facility

Extreme Ultraviolet Photoresist Inspection Platform in Shanghai Synchrotron Radiation Facility

Quantitative phase imaging in digital holographic microscopy based on image inpainting using a two-stage generative adversarial network

Based on the hologram inpainting via a two-stage Generative Adversarial Network (GAN), we present a precise phase aberration compensation method in digital holographic microscopy (DHM). In the proposed methodology, the interference fringes of the sample area in the hologram are firstly removed by the background segmentation via edge detection and morphological image processing. The vacancy area is then inpainted with the fringes generated by a deep learning algorithm. The image inpainting finally results in a sample-free reference hologram containing the total aberration of the system. The phase aberrations could be deleted by subtracting the unwrapped phase of the sample-free hologram from our inpainting network results, in no need of any complex spectrum centering procedure, prior knowledge of the system, or manual intervention. With a full and proper training of the two-stage GAN, our approach can robustly realize a distinct phase mapping, which overcomes the drawbacks of multiple iterations, noise interference or limited field of view in the recent methods using self-extension, Zernike polynomials fitting (ZPF) or geometrical transformations. The validity of the proposed procedure is confirmed by measuring the surface of preprocessed silicon wafer with a Michelson interferometer digital holographic inspection platform. The results of our experiment indicate the viability and accuracy of the presented method. Additionally, this work can pave the way for the evaluation of new applications of GAN in DHM.

Omnidirectional Robotic Platform for Surveillance of Particle Accelerator Environments with Limited Space Areas

Intelligent robotic systems are becoming essential for inspections and measurements in harsh environments. This article presents the design of an omnidirectional robotic platform for tunnel inspection with spatial limitations. This robot was born from the need to automate the surveillance process of the Super Proton Synchrotron (SPS) accelerator of the European Organization for Nuclear Research (CERN), where there is remaining radiation. The accelerator is located within a tunnel that is divided by small doors of 400 × 200 mm dimensions, through which the robot has to cross. The designed robot brings a robotic arm, and the needed devices to carry out the inspection. Thanks to this design, the robot application may vary by replacing certain devices and tools. In addition, this paper presents the kinematic and dynamic control models for the robotic platform.

A rapid quality grade discrimination method for Gastrodia elata powderusing ATR-FTIR and chemometrics

Gastrodia elata is an obligate fungal symbiont used in traditional Chinese medicine. There are currently 4 grades of the plant based on the “Commodity Specification Standard of 76 Kinds of Medicinal Materials”. The traditional discrimination methods for determining the medicinal grade of G. elata powders are complex and time-consuming which are not suitable for rapid analysis. We developed a rapid analysis method for this plant using attenuated total reflection and Fourier-transform infrared spectroscopy (ATR-FTIR) together with machine learning algorithms. The original spectroscopic data was first pre-treated using the multiplicative scatter correction (MSC) method and 4 principal components were extracted using extremely randomized trees (Extra-trees) and principal component analysis (PCA) algorithms, and different kinds of classification models were established. We found that multilayer perceptron classifier (MLPC) modeling was superior to support vector machine (SVM) and resulted in validation and prediction accuracies of 99.17% and 100%, respectively and a modeling time of 2.48 s. The methods established from the current study can rapidly and effectively distinguish the 4 different types of G. elata powders and thus provides a platform for rapid quality inspection.

Automatic optical inspection platform for real-time surface defects detection on plane optical components based on semantic segmentation.

The tendency to increase the accuracy and quality of optical parts inspection can be observed all over the world. The imperfection of manufacturing techniques can cause different defects on the optical component surface, making surface defects inspection a crucial part of the manufacturing of optical components. Currently, the inspection of lenses, filters, mirrors, and other optical components is performed by human inspectors. However, human-based inspections are time-consuming, subjective, and incompatible with a highly efficient high-quality digital workflow. Moreover, they cannot meet the complex criteria of ISO 10110-7 for the quality pass and fail optical element samples. To meet the high demand for high-quality products, intelligent visual inspection systems are being used in many manufacturing processes. Automated surface imperfection detection based on machine learning has become a fascinating and promising area of research, with a great direct impact on different visual inspection applications. In this paper, an optical inspection platform combining parallel deep learning-based image-processing approaches with a high-resolution optomechanical module was developed to detect surface defects on optical plane components. The system involves the mechanical modules, the illumination and imaging modules, and the machine vision algorithm. Dark-field images were acquired using a 2448×2048-pixel line-scanning CMOS camera with 3.45 µm per-pixel resolution. Convolutional neural networks and semantic segmentation were used for a machine vision algorithm to detect and classify defects on captured images of optical bandpass filters. The experimental results on different bandpass filter samples have shown the best performance compared to traditional methods by reaching an impressive detection speed of 0.07 s per image and an overall detection pixel accuracy of 0.923.

A Grey Wolf Optimization-Based Method for Segmentation and Evaluation of Scaling in Reinforced Concrete Bridges

Bridges are prone to severe deterioration agents which promote their degradation over the course of their lifetime. Furthermore, maintenance budgets are being trimmed. This state of circumstances entails the development of a computer vision-based method for the condition assessment of bridge elements in an attempt to circumvent the drawbacks of visual inspection-based models. Scaling is progressive local flaking or loss in the surface portion of concrete that affects the functional and structural integrity of reinforced concrete bridges. As such, this research study proposes a self-adaptive three-tier method for the automated detection and assessment of scaling severity levels in reinforced concrete bridges. The first tier relies on the integration of cross entropy function and grey wolf optimization (GWO) algorithm for the segmentation of scaling pixels. The second tier is designated for the autonomous interpretation of scaling area. In this model, a hybrid feature extraction algorithm is proposed based on the fusion of singular value decomposition and discrete wavelet transform for the efficient and robust extraction of the most dominant features in scaling images. Then an integration of Elman neural network and GWO algorithm is proposed for the sake of improving the prediction accuracies of scaling area though optimization of both structure and parameters of Elman neural network. The third tier aims at establishing a unified scaling severity index to assess the extent of severities of scaling according to its area and depth. The developed method is validated through multi-layered comparative analysis that involved performance evaluation comparisons, statistical comparisons and box plots. Results demonstrated that the developed scaling detection model significantly outperformed a set of widely-utilized classical segmentation models achieving mean squared error, mean absolute error, peak signal to noise ratio and cross entropy of 0.175, 0.407, 55.754 and 26011.019, respectively. With regards to the developed scaling evaluation model, it accomplished remarkable better and more robust performance that other meta-heuristic-based Elman neural network models and conventional prediction models. In this context, it obtained mean absolute percentage error, root-mean squared error and mean absolute error 1.513%, 29.836 and 12.066, respectively, as per split validation. It is anticipated that the developed integrated computer vision-based method could serve as the basis of automated, reliable and cost-effective inspection platform of reinforced concrete bridges which can assist departments of transportation in taking effective preventive maintenance and rehabilitation actions.

Autonomous robotic platform for inspection and repairing operations in harsh environments

ABSTRACT The MRO (Maintenance, Repair and Overhaul) industry expected economic growth is severely limited by the high human injury and death toll rates, mainly associated to harsh working environment and scarcity of unmanned support solutions. The present work introduces an advanced mobile robotic platform that is capable of providing a clear response to key MRO barriers being designed to climb vertical surfaces and operating in harsh environment while executing inspection and repairing tasks. The paper outlines the integrated design procedure supporting the engineering of the mobile platform; the benefits of the solution have been addressed and validated with regard to real world operating scenarios.

Visual detection of viscosity through activatable molecular rotor with aggregation-induced emission

Food safety has become one of the urgent affairs in the global public health studies, and irregular viscosity is closely associated with the food spoilage extent. In this study, one kind of activatable molecular rotor (TPA-PBZ) based on triphenylamine derivates has been synthesized via the Schiff base condensation reaction. This rotor is comprised by donor-accepter conjugated structure, with aggregation induced-emission feature and a large Stokes shift of 160 nm in water. The rotation of aromatic rings in TPA-PBZ is restricted in high-viscosity microenvironment, with the gradually increasing fluorescence emission signal at 568 nm. Significantly, this rotor TPA-PBZ has successfully been applied not only in the determination of thickening effects of food gum, but also in the detection of viscosity enhancement during the liquid food spoilage process. This molecular rotor can be utilized as an intelligent monitor platform for food quality and safety inspection in viscosity-related conditions.

A semi-supervised self-training method to develop assistive intelligence for segmenting multiclass bridge elements from inspection videos

Bridge inspection is an important step in preserving and rehabilitating transportation infrastructure for extending their service lives. The advancement of mobile robotic technology allows the rapid collection of a large amount of inspection video data. However, the data are mainly the images of complex scenes, wherein a bridge of various structural elements mix with a cluttered background. Assisting bridge inspectors in extracting structural elements of bridges from the big complex video data, and sorting them out by classes, will prepare inspectors for the element-wise inspection to determine the condition of bridges. This article is motivated to develop an assistive intelligence model for segmenting multiclass bridge elements from the inspection videos captured by an aerial inspection platform. With a small initial training dataset labeled by inspectors, a Mask Region-based Convolutional Neural Network pre-trained on a large public dataset was transferred to the new task of multiclass bridge element segmentation. Besides, the temporal coherence analysis attempts to recover false negatives and identify the weakness that the neural network can learn to improve. Furthermore, a semi-supervised self-training method was developed to engage experienced inspectors in refining the network iteratively. Quantitative and qualitative results from evaluating the developed deep neural network demonstrate that the proposed method can utilize a small amount of time and guidance from experienced inspectors (3.58 h for labeling 66 images) to build the network of excellent performance (91.8% precision, 93.6% recall, and 92.7% f1-score). Importantly, the article illustrates an approach to leveraging the domain knowledge and experiences of bridge professionals into computational intelligence models to efficiently adapt the models to varied bridges in the National Bridge Inventory.

Valve Detection for Autonomous Water Pipeline Inspection Platform

Water distribution and transmission lines are indispensable to urban infrastructure. The water pipelines are subject to both structural and functional deterioration due to various reasons including aging, negligence, and high demand for water supply. Hence, to ensure a safe and reliable water supply, the water utilities need to perform routine pipe condition assessment. The condition assessment is usually carried out by visual inspection with the machine vision system carried by a robotic platform. The inspection platforms will capture the internal condition of the water pipelines in a video stream. However, the robotic platform frequently experiences difficulties while traversing through the valves installed along the pipeline. This inhibits and disrupts the inspection process of the water pipelines. Therefore, this article proposes a deep learning-based automatic valve detection framework to facilitate the robot’s navigation and ensure continuous inspection without any interruptions. The valve detection model is developed by combining MobileNet-160 and Feature Pyramid Network and is named as MFPN. The developed framework also employs a generative adversarial network to solve the sparse dataset issues and improve the generality of the framework. The comparative study and ablation analyses demonstrate that the proposed framework can achieve a higher mAP value of 89.11% in comparison with the state of the art. Hence, this light weight and efficient solution can be deployed to the robotic platform for real-time valve detection and enable autonomous navigation of the robotic platform for condition assessment of water pipelines.

InDrone: a 2D-based drone flight behavior visualization platform for indoor building inspection

PurposeThe purpose of this research is to explore how to visually represent human decision-making processes during the performance of indoor building inspection flight operations using drones.Design/methodology/approachData from expert pilots were collected using a virtual reality drone flight simulator. The expert pilot data were studied to inform the development of an interactive 2D representation of drone flight spatial and temporal data – InDrone. Within the InDrone platform, expert pilot data were visually encoded to characterize key pilot behaviors in terms of pilots' approaches to view and difficulties encountered while detecting the inspection markers. The InDrone platform was evaluated using a user-center experimental methodology focusing on two metrics: (1) how novice pilots understood the flight approaches and difficulties contained within InDrone and (2) the perceived usability of the InDrone platform.FindingsThe results of the study indicated that novice pilots recognized inspection markers and difficult-to-inspect building areas in 63% (STD = 48%) and 75% (STD = 35%) of the time on average, respectively. Overall, the usability of InDrone presented high scores as demonstrated by the novice pilots during the flight pattern recognition tasks with a mean score of 77% (STD = 15%).Originality/valueThis research contributes to the definition of visual affordances that support the communication of human decision-making during drone indoor building inspection flight operations. The developed InDrone platform highlights the necessity of defining visual affordances to explore drone flight spatial and temporal data for indoor building inspections.

Research and analysis of fluorescent magnetic particle inspection of steam turbine rotor blade

The operating conditions of turbine rotor blades are complex and changeable, and the probability of defects is high, which puts forward higher requirements for the professionalism, safety, stability and convenience of magnetic particle inspection personnel. How to find the deficiencies in the magnetic particle inspection of blades and improve it to better adapt to the field inspection has become the focus of attention. This paper mainly describes the process of fluorescent magnetic particle inspection, compares the similarities and differences of magnetization specifications in three domestic standards, analyzes the problems found in the process of fluorescent magnetic particle inspection and makes corresponding improvement, and adopts reasonable optimization scheme to improve the reliability of fluorescent magnetic particle inspection. After demonstration test and field application, the improved fluorescent magnetic particle inspection can give full play to its advantages, achieve the purpose of rapid, accurate inspection and continuous improvement; on this basis, different inspection platforms can be established to inspect different parts, improve the universality and inspection efficiency of magnetic particle inspection, which is conducive to the promotion and application of magnetic particle inspection in the field of nuclear power.

Online teaching research on college physics by using different teaching tools

In the epidemic period, online teaching has become the most important teaching form. On the basis of questions-centred massive open online courses (MOOCs) teaching, the online teaching mode of college physics was studied and discussed using the Tencent classroom, WeChat/QQ and Wisdom Tree Platform. MOOCs before class, Tencent classroom during class, WeChat/QQ in the whole process including solving problems after class, and Wisdom Tree Platform for quality inspection of online teaching were practiced in the teaching of each chapter including mass point kinematics. Due to the problem-oriented implementation of the three stages, the results of the online mock test were better than expected. In other words, the online teaching supported by various online tools could effectively guarantee the quality of teaching.

A high performance real-time vision system for curved surface inspection

Surface quality plays an important role in inspection lines. In this paper, a novel imaging device combined with FPGA (Field Programmable Gate Array) based processing platform had been designed to detect and analyse curved surface defects for vision inspection. The optical imaging part was made by an optical device which can be used to collect curved surface features without anamorphose and a camera with 70k Hz linear CMOS was used to capture surface information. The FPGA based inspection platform had been developed for camera control and image processing. Inspecting experiments had been tested with an inspection accuracy of 0.2 mm x 0.2 mm which satisfied a 12 m/s real-time vision inspection line. This research result can be subsequently applied to various surface inspection scenarios.

Research on pulsed eddy current automatic detection technology for weld defects of pressure vessel cylinder

In order to solve the problems of low detection efficiency, high labor intensity and poor working environment of large pressure vessel cylinder, the pulsed eddy current (PEC) nondestructive testing technology is used to detect the defects in the welding seam of the cylinder. The PEC sensor composed of rectangular probe coil and Hall sensor is installed on the mobile inspection platform to test two simulated cylinder weld samples. Results show that the signal curve of sample 1 without defect is gentle and without mutation, while the signal of sample 2 with defect has three mutation signals. Then, the X-ray testing method is used to detect the sample, radiograph confirmed that there are no defects on sample 1 and three porosity defects in sample 2. Through the comparison and analysis of the above two kinds of detection results, the feasibility of the detection method for weld subsurface defects is verified.

A Framework for Cloud Inspection of Complex Mechanical Parts

Inspection plays an important role in quality control and trust of manufacturing service in the cloud manufacturing (CMfg) paradigm. To provide an inspection service for complex mechanical parts in CMfg, the concept of cloud inspection was defined as a multi-dimension extension of CMfg. A framework of cloud inspection consisting of the physical resource layer, the cloud inspection platform, and the application layer was constructed. The data model of cloud inspection, including the role model, resource model, and business model was constructed. Besides, a 4-phrase process model was built by integrating digital inspection technology into the cloud manufacturing environment. Inspection resources including hardware resources, software resources, and knowledge resources were connected to the cloud inspection platform. A four-level model was proposed for the encapsulation of inspection services. Enable toolsets for cloud inspection of complex mechanical parts, including the inspection planning toolset, measurement programming toolset, inspection process monitor toolset, and data analysis toolset were encapsulated. A prototype system was developed. A case study was given to verify the effectiveness and feasibility of the proposed framework. The framework for cloud inspection provides a solution for the inspection business of mechanical parts in CMfg.