1,720,973 research outputs found
A Time-Frequency Domain Feature Extraction Approach Enhanced by Computer Vision for Wire Arc Additive Manufacturing Monitoring Using Fourier and Wavelet Transform
No full textWire arc additive manufacturing (WAAM) is a rapidly growing technology that offers several advantages over traditional manufacturing methods, such as high deposition rates and the ability to build large components in a cost-effective manner. However, WAAM is also prone to the generation of defects, so the timely identification of anomalies is important to reduce the waste and get components of high quality. To develop anomaly detection application, the feature extraction process represents a key ingredient which allows machine learning systems to analyze big data. Waveform GMAW welding processes are typically used in WAAM to reduce the heat input supplied to the material and avoid defects such as excessive bending of parts and residual stress. These processes are based on the controlled dip transfer principle, so the waveforms should repeat themselves during deposition. This suggests that the frequency content of the voltage and current welding signals acquired during the process can provide important information about the process state. In this research, an experimental campaign was conducted to collect data for pulsed welding and surface tension transfer (STT) processes during the deposition of mild steel ER70S6, stainless steel 316L, Aluminum 4043, and Inconel 718 alloys. Welding voltage and current signals were acquired during the building processes, and a frequency domain analysis was conducted using the Fast Fourier transform (FFT) and discrete wavelet transform (DWT) with the aim to extract features from signals aiming to better separate the feature space, which means improve anomaly detection performance in detecting defects like arc instability, porosity, geometrical defect due to arc blow and humping. Furthermore, a methodology based on time-frequency analysis enhanced by Gabor filter for texture anomaly detection of scalograms obtained by Morlet Continuous Wavelet Transform is proposed, which showed an improvement of performance in separation between normal and anomalous deposition of several materials under different welding technologies
Monitoring and control the Wire Arc Additive Manufacturing process using artificial intelligence techniques: a review
No full textMonitoring the gas metal arc additive manufacturing process using unsupervised machine learning
No full textHybrid Statistical Process Monitoring of Wire Arc Additive Manufacturing With Frequency-Informed Deep Learning
No full textArc welding is classified as a special process under ISO standards, making process monitoring a critical component of the welding and additive manufacturing (AM) certification procedure. Nowadays, the advancements in data analysis have led to the growing use of Machine Learning (ML) techniques for real-time weld quality assessment. However, due to their simple design and minimal data requirements, traditional statistical process monitoring (SPM) methods, such as control charts, remain widely used for evaluating process quality and detecting anomalies. Despite their significance, traditional SPM techniques struggle when dealing with multivariate and high-frequency data typical of Industry 4.0 contexts, making their application challenging and highlighting the need for new approaches to data analysis. Therefore, in this study, we propose an innovative hybrid deep learning–based SPM technique for in situ monitoring of the wire arc additive manufacturing (WAAM) process, with the aim of making SPM more effective in this setting. In particular, an experimental campaign was conducted using the Invar36 alloy, and an online anomaly detection application was developed using ML methods to improve the performance of SPM. Specifically, a frequency-informed convolutional auto-encoder (FICA) is used as a sensor fusion technique for welding current and welding voltage data. The obtained latent space across additional temporal dimensions—which fuse the high-frequency information in a low dimensional space—is then analysed using an exponentially weighted moving average (EWMA) chart to detect anomalies during production. The results demonstrate that the proposed methodology improves anomaly detection performance compared to conventional SPM techniques, with the F2-score improving from 71.1% to 81.3%
Shrinkage estimation with reinforcement learning of large variance matrices for portfolio selection
No full textA large amount of assets characterizes high-dimensional portfolio selection problems compared to temporal observation. In such a high-dimensional framework, the asset allocation is unfeasible because the covariance matrix obtained with the usual sample estimators cannot be inverted. This paper proposes a new shrinkage estimator based on reinforcement learning for large covariance matrices that is optimal in the context of portfolio selection. The resulting estimator is entirely data-driven since the optimal shrinkage intensity is given by optimizing neural network weights. This paper presents two different architectures: a standard fully connected network for a classical Policy Gradient Agent (PGA) and a Gated Recurrent Unit for a Recurrent Policy Gradient Agent (RPGA). To show the validity of the proposal, an application to asset allocation with Industry portfolios is provided. The results indicate that the RPGA-based approach in shrinkage estimation provides the best performance in out-of-sample comparison
Barriers towards foreign firms in international public procurement markets: a review
No full texthe international dimension of public procurement has gained inimportance in the last decade and has attracted the attention of economist and policymakers. A number of trade agreements were signed with the intention to removebarriers to procurement markets and favour entry of foreign firms and products.However, empirical evidence shows that, despite the existence of trade agreements,discrimination towards foreign firms still applies in a number of countries aroundthe world. In this paper, we present the methodologies used in the economic lit-erature for the identification of overt and covert barriers to public tenders anddiscuss the importance of collecting high quality data for meliorating the ability ofinternational traders to detect procurement barriers
Anomaly detection in manufacturing systems with temporal networks and unsupervised machine learning
Full text linkTraditional manufacturing systems face significant challenges in detecting operational anomalies due to the absence of advanced sensor networks and intelligent machinery commonly associated with Industry 4.0. Existing solutions often rely on sophisticated, interconnected infrastructures, which are not feasible in conventional settings. This paper introduces a novel methodology for anomaly detection tailored specifically for traditional manufacturing environments, addressing the gap in cost-effective monitoring solutions. The proposed approach models manufacturing systems as complex temporal networks, where each machine or process is represented as a node and job flows between machines form the network edges over time. The novelty of this method lies in the combination of dynamic network theory with unsupervised machine learning. Statistical features extracted from the temporal networks are processed through dimensionality reduction techniques, specifically Principal Component Analysis (PCA) and Deep Neural Autoencoders, to reduce feature complexity while preserving essential information. The reduced feature sets are then analysed using multiple unsupervised anomaly detection algorithms, including Isolation Forest, One-Class Support Vector Machine (OC-SVM), and Local Outlier Factor (LOF). This approach does not require significant infrastructure upgrades, making it suitable for traditional manufacturing plants while still aligning with Industry 4.0 paradigms. By using only normal job flow data, it provides a cost-effective solution where anomalous data is scarce. The results demonstrate that Local Outlier Factor and Isolation Forest, when combined with Autoencoder-based feature reduction, achieved an F1-score exceeding 84%, with precision close to 99% and recall at 74%. This strong performance underscores the methodology's potential for real-world manufacturing environments, bridging the gap between traditional settings and modern Industry 4.0 paradigms
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
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