Search Results - data processing for defect detection

Multi-Modal Dynamic Fusion for Defect Detection in Electronic Products: A Novel Approach Based on Energy and Deep Learning by Yulin Liu, Yang Gao

“…Specifically, Transformer architectures are employed for sensor data analysis, Convolutional Neural Networks (CNNs) are applied to process image data, and Multi-Layer Perceptrons (MLPs) are used to represent part-level features. …”
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Variational autoencoders for at-source data reduction and anomaly detection in high energy particle detectors by Alexander Yue, Haoyi Jia, Julia Gonski

“…Results are presented from low-latency and resource-efficient VAEs for front-end data processing in a futuristic silicon pixel detector. …”
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A novel machine learning-based approach to thermal integrity profiling of concrete pile foundations by Javier Sánchez Fernández, Agustín Ruiz López, David M.G. Taborda

“…Thermal integrity profiling (TIP) is a nondestructive testing technique that takes advantage of the concrete heat of hydration (HoH) to detect inclusions during the casting process. This method is becoming more popular due to its ease of application, as it can be used to predict defects in most concrete foundation structures requiring only the monitoring of temperatures. …”
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Comprehensive study of characteristic signs of defects detected during magnetic powder control at the final stage of production of seamless hot‑rolled pipes by E. A. Naumenko, O. V. Rozhkova, I. A. Kovaleva

“…Detection of violations of technology, control of the technological process, carrying out metallographic studies allow classifying defects and establishing the nature and causes of their formation.The article presents the results of a metallographic study of a defect on the outer surface of a hot‑rolled seamless pipe. …”
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Design and Improvement of an Automated Tool for Quality Control and Performance Assessment of Photovoltaic Modules by Alain Foutche Tchouli, Stephane Ndiya Ngasop, Jean Hilaire Tchami, Claude Bertin Nzoundja Fapi, Hyacinthe Tchakounté

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Learning to Be a Transformer to Pinpoint Anomalies by Alex Costanzino, Pierluigi Zama Ramirez, Giuseppe Lisanti, Luigi Di Stefano

“…To efficiently deploy strong, often pre-trained feature extractors, recent Industrial Anomaly Detection and Segmentation (IADS) methods process low-resolution images, e.g., <inline-formula> <tex-math notation="LaTeX">$224 \times 224$ </tex-math></inline-formula> pixels, obtained by downsampling the original input images. …”
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Smartphone-Based Automated Non-Destructive Testing Devices by V. F. Petryk, A. G. Protasov, R. M. Galagan, A. V. Muraviov, I. I. Lysenko

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Self-Supervised Image Anomaly Detection Through Diverse Pseudo Anomaly Insertion by Rizwan Ali Shah, Odilbek Urmonov, Hyungwon Kim

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From Tables to Computer Vision: Transforming HPDC Process Data into Images for CNN-Based Deep Learning by A. Burzyńska

“…This paper proposes a methodology for leveraging convolutional neural networks (CNNs) in conjunction with advanced data preprocessing to facilitate optimal quality control decision-making in high pressure casting (HPDC) processes. …”
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Transforming Tofu Quality Control: Integrating Statistical Process Control, Ishikawa, and Interpretive Structural Modeling for Superior Outcomes by DWI IRYANING Handayani, Qurtubi

“…In this study, the control process quality was evaluated using SPC, the cause of defects was identified using Ishikawa diagrams, and priority action repair was performed via ISM. …”
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Complex-Valued CNN-Based Defect Reconstruction of Carbon Steel from Eddy Current Signals by Bing Chen, Tengwei Yu

“…Notably, this approach processes the complete complex-valued signal without relying on prior structural parameters or baseline data, thereby achieving substantial improvements in both defect visualization and classification performance. …”
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Multi-information fusion welding defect identification combining neighborhood rough set and optimized SVM by Zhiqiang FENG, Xianping ZENG, Naiwen FANG, Daidi ZHAO, Quan LI, Jiutian LUO, Xin LI

“…A multi-information fusion welding defect recognition method is proposed by combining NRS with optimized SVM to address the “big data” generated during the multi-sensor information fusion welding process. …”
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Automatic identification and characteristics analysis of crack tips in rocks with prefabricated defects based on deep learning methods. by Mingtao Gao, Minhui Li, Lu Chen, Zihao Guo, Chengyang Guo, Liping Li, Changsen Bu

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Enhancing Sustainable Manufacturing in Industry 4.0: A Zero-Defect Approach Leveraging Effective Dynamic Quality Factors by Rouhollah Khakpour, Ahmad Ebrahimi, Seyed Mohammad Seyed Hosseini

“…<a href="#_ENREF_10">Leit&atilde;o et al. (2018)</a> apply multi-agent system (MAS) infrastructure, which combines with data analysis, provides early and real time detection of deviations, prevents defects occurrence and their propagation to downstream processes, and finally enables the system to be predictive by early detection of defects and to be proactive through self-adaptation with different situations.…”
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Explainable Graph Neural Networks for Power Grid Fault Detection by Richard Bosso, Corey Chang, Mahdi Zarif, Yufei Tang

“…This research presents a comprehensive framework that systematically evaluates state-of-the-art explanation strategies, representing the first use of such a framework for Graph Neural Network models for defect location detection. By assessing the strengths and weaknesses of different explanatory methods, it identifies and recommends the most effective strategies for clarifying the decision-making processes of GNN models. …”
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Deep Learning Techniques for Early Fault Detection in Bearings: An Intelligent Approach by Omar Mohammed Amin Ali, Rebin Abdulkareem Hamaamin, Shahab Wahhab Kareem

“…Machine learning (ML) and deep learning (DL) algorithms have improved image processing, speech recognition, defect detection, item identification, and medical sciences. …”
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Detection of square wave impulse interference in eddy current rail defectograms by Leonid Y. Bystrov, Artemy N. Gladkov, Egor V. Kuzmin

“…Traffic safety in rail transport requires continuous monitoring of the rail condition for timely detection and elimination of defects. One of the methods of non-destructive testing of rails is eddy current flaw detection. …”
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On-Line Process Monitoring for Aero-Space Components Using Different Technologies of Fiber Optic Sensors During Liquid Resin Infusion (LRI) Process by Cristian Builes Cárdenas, Tania Grandal González, Arántzazu Núñez Cascajero, Mario Román Rodríguez, Rubén Ruiz Lombera, Paula Rodríguez Alonso

“…During the study, both FOS technologies were introduced into the materials, varying process conditions and the introduction of some artificial defects to evaluate the sensors response to correlate them after with their signals. …”
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Construction of Fully Automated Key Production Line by Guo-Cheng Lee, Yi-Hsuan Chiu, Kuang-Chyi Lee

“…The defects inspection station ensures comprehensive quality checks, automatically stops the production line for detected defects, and prevents defective products from proceeding to subsequent stages. …”
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PAVEMENT CONDITION ASSESSMENT USING LIDAR AND ARCGIS: AN EXPERIENCE FROM MALAYSIA by Babak GOLCHIN, Muhammad Aiman Badrish RAFFI, Nur Zarifah ZAKARIA, Noor Halizah ABDULLAH

“…Initially, point cloud data were collected from both expressways using LiDAR, and related images were processed through ArcGIS software to identify defects on the road surfaces. …”
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