1,720,961 research outputs found
Vision-based defect localisation and automated planning for robotic spray coating systems
No full textThis work presents a novel approach to improving the robotic quality inspection of the spray coating process in the aerospace
industry by integrating computer vision with robotic systems. While spray coating is essential for providing protective and
aesthetic coatings, due to the challenges and complexity of the aerospace industry, it frequently encounters issues such as
incomplete coverage, paint defects, and surface imperfections, which can compromise quality and increase the need for
rework. To address this, a methodology that utilises a cutting-edge computer vision technique based on YOLOv10 for realtime defect localisation is proposed, targeting issues such as uneven thickness and missed areas. Once the camera is calibrated,
the results of defect localisation achieve a multi-class mean Average Precision of 99%. Furthermore, this work presents a
framework that demonstrates how positional information and classification results can be utilised to automatically generate
path planning and control actions for an intelligent spray coating system. This innovation advances the state of knowledge
in the field, which has previously relied only on image classification
Frequency informed convolutional autoencoder for in situ anomaly detection in wire arc additive manufacturing
No full textIn the context of Industry 4.0, the importance of anomaly detection is growing, particularly in Additive Manufacturing, as itallows for the detection and localization of defects, thereby reducing waste and costs. However when normal and anomalysignals have similar shapes in time this task is particularly challenging. Despite that, the frequency content of time seriessignals often holds valuable information that, when integrated into the learning process, can greatly improve the recognitionof hidden patterns in the data and enhance feature separability. In this study, we propose an unsupervised anomaly detectiontechnique for Wire Arc Additive Manufacturing (WAAM) based on deep learning, namely 1D-Convolutional AutoEncoder.By integrating frequency-regularization terms based on wavelet analysis of defect-free welding signals during the trainingphase, the results demonstrated a significant 54.8% improvement in anomaly detection performance compared to similarmethods. This improvement enables the effective use of unsupervised learning for anomaly detection in WAAM, minimizingthe need for labeled data and making it suitable for industrial applications, even when dealing with unbalanced datasets
Deep Neural Networks for Defects Detection in Gas Metal Arc Welding
Full text linkWelding is one of the most complex industrial processes because it is challenging to model, control, and inspect. In particular, the quality inspection process is critical because it is a complex and time-consuming activity. This research aims to propose a system of online inspection of the quality of the welded items with gas metal arc welding (GMAW) technology through the use of neural networks to speed up the inspection process. In particular, following experimental tests, the deviations of the welding parameters—such as current, voltage, and welding speed—from the Welding Procedure Specification was used to train a fully connected deep neural network, once labels have been obtained for each weld seam of a multi-pass welding procedure through non-destructive testing, which made it possible to find a correspondence between welding defects (e.g., porosity, lack of penetrations, etc.) and process parameters. The final results have shown an accuracy greater than 93% in defects classification and an inference time of less than 150 ms, which allow us to use this method for real-time purposes. Furthermore in this work networks were trained to reach a smaller false positive rate for the classification task on test data, to reduce the presence of faulty parts among non-defective parts
Towards the application of machine learning in digital twin technology: a multi-scale review
Full text linkThis review article delves into the conceptual framework of digital twins and their diverse applications across research domains, highlighting the pivotal role of machine learning in shaping the development and integration of digital twin technology across multiple disciplines. Emphasising key features like multidisciplinarity and multi-scale aspects, the paper explores how data-driven techniques are employed for modelling, visualisation, monitoring, and optimisation within the digital twin framework, pinpointing the benefits introduced in the current state-of-the-art applications, and elucidates persisting challenges across various research fields, including advanced materials, smart buildings, and manufacturing systems
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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