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"quality inspection"
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Exploiting image quality measure for automatic trajectory generation in robot-aided visual quality inspection
Currently, the standard method of programming industrial robots is to perform it manually, which is cumbersome and time-consuming. Thus, it can be a burden for the flexibility of inspection systems when a new component with a different design needs to be inspected. Therefore, developing a way to automate the task of generating a robotic trajectory offers a substantial improvement in the field of automated manufacturing and quality inspection. This paper proposes and evaluates a methodology for automatizing the process of scanning a 3D surface for the purpose of quality inspection using only visual feedback. The paper is divided into three sub-tasks in the same general setting: (1) autonomously finding the optimal distance of the camera on the robot’s end-effector from the surface, (2) autonomously generating a trajectory to scan an unknown surface, and (3) autonomous localization and scan of a surface with a known shape, but with an unknown position. The novelty of this work lies in the application that only uses visual feedback, through the image focus measure, for determination and optimization of the motion. This reduces the complexity and the cost of such a setup. The methods developed have been tested in simulation and in real-world experiments and it was possible to obtain a precision in the optimal pose of the robot under 1 mm in translational, and 0.1
∘
in angular directions. It took less than 50 iterations to generate a trajectory for scanning an unknown free-form surface. Finally, with less than 30 iterations during the experiments it was possible to localize the position of the surface. Overall, the results of the proposed methodologies show that they can bring substantial improvement to the task of automatic motion generation for visual quality inspection.
Journal Article
Effect of the manufacturer quality inspection policy on the supply chain decision-making and profits
Due to competitive pressure and information asymmetry, manufacturers will produce quality inspection avoidance behaviour to gain short-term economic benefits, but this behaviour affects the ultimate quality and safety of the product. This paper studies the two-echelon supply chain consisting of a manufacturer and a retailer, and analyses whether the manufacturer's quality inspection avoidance behaviour model is considered or not. This paper discusses the impact of quality inspection level, quality loss cost, product repair cost, product return rate on the profit and optimal decision-making behaviour of both actors of the supply chain. It is found that when the manufacturer's quality inspection avoidance level is high, the increase of retailer' quality inspection effort level, manufacturer's internal failure cost, consumer product return rate and retailer' external quality loss cost will lead to the decrease of manufacturer's quality effort level instead of increasing. Finally, the numerical study is given to verify the above conclusion, and analysed the influence of different parameters on the optimal decision and supply chain actors profits.
Journal Article
YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection
Since its inception in 2015, the YOLO (You Only Look Once) variant of object detectors has rapidly grown, with the latest release of YOLO-v8 in January 2023. YOLO variants are underpinned by the principle of real-time and high-classification performance, based on limited but efficient computational parameters. This principle has been found within the DNA of all YOLO variants with increasing intensity, as the variants evolve addressing the requirements of automated quality inspection within the industrial surface defect detection domain, such as the need for fast detection, high accuracy, and deployment onto constrained edge devices. This paper is the first to provide an in-depth review of the YOLO evolution from the original YOLO to the recent release (YOLO-v8) from the perspective of industrial manufacturing. The review explores the key architectural advancements proposed at each iteration, followed by examples of industrial deployment for surface defect detection endorsing its compatibility with industrial requirements.
Journal Article
Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity
Automatic detection and localization of anomalies in nanofibrous materials help to reduce the cost of the production process and the time of the post-production visual inspection process. Amongst all the monitoring methods, those exploiting Scanning Electron Microscope (SEM) imaging are the most effective. In this paper, we propose a region-based method for the detection and localization of anomalies in SEM images, based on Convolutional Neural Networks (CNNs) and self-similarity. The method evaluates the degree of abnormality of each subregion of an image under consideration by computing a CNN-based visual similarity with respect to a dictionary of anomaly-free subregions belonging to a training set. The proposed method outperforms the state of the art.
Journal Article
A Decade of Modern Bridge Monitoring Using Terrestrial Laser Scanning: Review and Future Directions
Over the last decade, particular interest in using state-of-the-art emerging technologies for inspection, assessment, and management of civil infrastructures has remarkably increased. Advanced technologies, such as laser scanners, have become a suitable alternative for labor intensive, expensive, and unsafe traditional inspection and maintenance methods, which encourage the increasing use of this technology in construction industry, especially in bridges. This paper aims to provide a thorough mixed scientometric and state-of-the-art review on the application of terrestrial laser scanners (TLS) in bridge engineering and explore investigations and recommendations of researchers in this area. Following the review, more than 1500 research publications were collected, investigated and analyzed through a two-fold literature search published within the last decade from 2010 to 2020. Research trends, consisting of dominated sub-fields, co-occurrence of keywords, network of researchers and their institutions, along with the interaction of research networks, were quantitatively analyzed. Moreover, based on the collected papers, application of TLS in bridge engineering and asset management was reviewed according to four categories including (1) generation of 3D model, (2) quality inspection, (3) structural assessment, and (4) bridge information modeling (BrIM). Finally, the paper identifies the current research gaps, future directions obtained from the quantitative analysis, and in-depth discussions of the collected papers in this area.
Journal Article
Deep Learning for Industrial Computer Vision Quality Control in the Printing Industry 4.0
Rapid and accurate industrial inspection to ensure the highest quality standards at a competitive price is one of the biggest challenges in the manufacturing industry. This paper shows an application of how a Deep Learning soft sensor application can be combined with a high-resolution optical quality control camera to increase the accuracy and reduce the cost of an industrial visual inspection process in the Printing Industry 4.0. During the process of producing gravure cylinders, mistakes like holes in the printing cylinder are inevitable. In order to improve the defect detection performance and reduce quality inspection costs by process automation, this paper proposes a deep neural network (DNN) soft sensor that compares the scanned surface to the used engraving file and performs an automatic quality control process by learning features through exposure to training data. The DNN sensor developed achieved a fully automated classification accuracy rate of 98.4%. Further research aims to use these results to three ends. Firstly, to predict the amount of errors a cylinder has, to further support the human operation by showing the error probability to the operator, and finally to decide autonomously about product quality without human involvement.
Journal Article
Terahertz Quality Inspection for Automotive and Aviation Industries
Nondestructive quality inspection with terahertz waves has become an emerging technology, especially in the automotive and aviation industries. Depending on the specific application, different terahertz systems—either fully electronic or based on optical laser pulses—cover the terahertz frequency region from 0.1 THz up to nearly 10 THz and provide high-speed volume inspections on the one hand and high-resolution thickness determination on the other hand. In this paper, we present different industrial applications, which we have addressed with our terahertz systems within the last couple of years. First, we show three-dimensional imaging of glass fiber–reinforced composites and foam structures, and demonstrate thickness determination of multilayer plastic tube walls. Then, we present the characterization of known and unknown multilayer systems down to some microns and the possibility of measuring the thickness of wet paints. The challenges of system reliability in industrial environments, e.g., under the impact of vibrations, and effective solutions are discussed. This paper gives an overview of state-of-the-art terahertz technology for industrial quality inspection. The presented principles are not limited to the automotive and aviation industries but can also be adapted to many other industrial fields.
Journal Article
Smartphone Spectrometers
Smartphones are playing an increasing role in the sciences, owing to the ubiquitous proliferation of these devices, their relatively low cost, increasing processing power and their suitability for integrated data acquisition and processing in a ‘lab in a phone’ capacity. There is furthermore the potential to deploy these units as nodes within Internet of Things architectures, enabling massive networked data capture. Hitherto, considerable attention has been focused on imaging applications of these devices. However, within just the last few years, another possibility has emerged: to use smartphones as a means of capturing spectra, mostly by coupling various classes of fore-optics to these units with data capture achieved using the smartphone camera. These highly novel approaches have the potential to become widely adopted across a broad range of scientific e.g., biomedical, chemical and agricultural application areas. In this review, we detail the exciting recent development of smartphone spectrometer hardware, in addition to covering applications to which these units have been deployed, hitherto. The paper also points forward to the potentially highly influential impacts that such units could have on the sciences in the coming decades.
Journal Article