Inline-Acquired Product Point Clouds for Non-Destructive Testing: A Case Study of a Steel Part Manufacturer

Machines, Год журнала: 2025, Номер 13(2), С. 88 - 88

Опубликована: Янв. 23, 2025

Modern vision-based inspection systems are inherently limited by their two-dimensional nature, particularly when inspecting complex product geometries. These often unable to capture critical depth information, leading challenges in accurately measuring features such as holes, edges, and surfaces with irregular curvature. To address these shortcomings, this study introduces an approach that leverages computer-aided design-oriented three-dimensional point clouds, captured via a laser line triangulation sensor mounted onto motorized linear guide. This setup facilitates precise surface scanning, extracting geometrical features, which subsequently processed through AI-based analytical component. Dimensional properties, radii inter-feature distances, computed using combination of K-nearest neighbors least-squares circle fitting algorithms. is validated the context steel part manufacturing, where traditional 2D struggle due material’s reflectivity system achieves average accuracy 95.78% across three different types, demonstrating robustness adaptability varying configurations. An uncertainty analysis confirms measurement deviations remain within acceptable limits, supporting system’s potential for improving quality control industrial environments. Thus, proposed may offer reliable, non-destructive inline testing solution, enhance manufacturing efficiency.

Язык: Английский

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The Role Of Digital Banking Features In Bank Selection An Analysis Of Customer Preferences For Online And Mobile Banking

SSRN Electronic Journal, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

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Applications of Artificial Intelligence, Deep Learning, and Machine Learning to Support the Analysis of Microscopic Images of Cells and Tissues

Journal of Imaging, Год журнала: 2025, Номер 11(2), С. 59 - 59

Опубликована: Фев. 15, 2025

Artificial intelligence (AI) transforms image data analysis across many biomedical fields, such as cell biology, radiology, pathology, cancer and immunology, with object detection, feature extraction, classification, segmentation applications. Advancements in deep learning (DL) research have been a critical factor advancing computer techniques for mining. A significant improvement the accuracy of detection algorithms has achieved result emergence open-source software innovative neural network architectures. Automated now enables extraction quantifiable cellular spatial features from microscope images cells tissues, providing insights into organization various diseases. This review aims to examine latest AI DL mining microscopy images, aid biologists who less background knowledge machine (ML), incorporate ML models focus images.

Язык: Английский

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A Systematic Literature Review Of Machine Learning Adoption In Emerging Marketing Applications

SSRN Electronic Journal, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

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The Role of Image Processing and Deep Learning in IoT-Based Systems: A Comprehensive Review

Deleted Journal, Год журнала: 2025, Номер 3(1), С. 165 - 179

Опубликована: Фев. 19, 2025

The rates at which IoT is expanding are tremendous, literally touching our daily life experiences through various applications such as smart city, healthcare, agriculture and industrial automation among-couple others. From amongst a number of diverse types data produced by devices, image has risen to the forefront one most useful tools for real-time identification decision making. critical contribution processing deep learning in improving systems discussed this paper. Image acquisition, preprocessing, segmentation feature extraction procedures form basis acquiring significant information from raw imagery data. approaches CNNs, RNNs, transfer learning, makes classification extraction, object detection more accurate fully automated. These technologies have been incorporated used traffic monitoring application, medical diagnosis, environmental monitoring, fault diagnosis industries. Nonetheless, issues resource availability, temporal delay security act barriers adoption microservices especially edges fogs computing. To overcome these constraints, enhancement on lightweight Learning, Edge AI privacy protection methodologies being advanced efficient, secure real time performance. Hence, trends federated 5G can also define future based systems. This paper systematically critically reviews recent advances towards application architectures providing insight into its profile, challenges trends. It meant guide researchers industry experts who working building smarter scalable efficient

Язык: Английский

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The Role of Generative Artificial Intelligence in Digital Agri-Food

Journal of Agriculture and Food Research, Год журнала: 2025, Номер unknown, С. 101787 - 101787

Опубликована: Март 1, 2025

Язык: Английский

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Enhanced Vision-Based Quality Inspection: A Multiview Artificial Intelligence Framework for Defect Detection

Sensors, Год журнала: 2025, Номер 25(6), С. 1703 - 1703

Опубликована: Март 10, 2025

Automated defect detection is a critical component of modern industrial quality control. However, it particularly difficult to identify subtle defects such as scratches on metallic surfaces. Therefore, this paper investigates the effectiveness multiview deep learning approaches for improved by implementing and comparing early late fusion methodologies. We propose MV-UNet, novel architecture that aligns aggregates features using transformation block enhance accuracy. To evaluate performance, we conduct our experiments recorded plates dataset, traditional single-view inspection both methods. Our results demonstrate methods improve accuracy over mono-view baseline, with MV-UNet achieving hightest F1-score (0.942). Additionally, introduce adapted precision–recall metrics designed segmentation-based detection, addressing limitations IoU-based evaluations. These tailored more accurately reflect localization thin, elongated scratches. findings highlight advantages providing robust scalable approach analysis.

Язык: Английский

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Integrating computer vision and machine learning technologies for model building to quantify intermuscular fat content in salmonid fillets

Food Control, Год журнала: 2025, Номер unknown, С. 111293 - 111293

Опубликована: Март 1, 2025

Язык: Английский

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Machine Learning Algorithms for Manufacturing Quality Assurance: A Systematic Review of Performance Metrics and Applications

Array, Год журнала: 2025, Номер unknown, С. 100393 - 100393

Опубликована: Март 1, 2025

Язык: Английский

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A review of deep learning for super-resolution in fluid flows

Physics of Fluids, Год журнала: 2025, Номер 37(4)

Опубликована: Апрель 1, 2025

Integrating deep learning with fluid dynamics presents a promising path for advancing the comprehension of complex flow phenomena within both theoretical and practical engineering domains. Despite this potential, considerable challenges persist, particularly regarding calibration training models. This paper conducts an extensive review analysis recent developments in architectures that aim to enhance accuracy data interpretation. It investigates various applications, architectural designs, performance evaluation metrics. The covers several models, including convolutional neural networks, generative adversarial physics-informed transformer diffusion reinforcement frameworks, emphasizing components improving reconstruction capabilities. Standard metrics are employed rigorously evaluate models' reliability efficacy producing high-performance results applicable across spatiotemporal data. findings emphasize essential role representing flows address ongoing related systems' high degrees freedom, precision demands, resilience error.

Язык: Английский

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