Datenanreicherung

Digitalisierung in der Werkstofftechnik

Anreicherung in Werkstoffen

Datenanreicherung

Datenanreicherung bezeichnet die gezielte Erweiterung vorhandener Datensätze durch synthetische oder ergänzende Informationen. In den Materialwissenschaften umfasst sie z.B. Simulationsergebnisse, physikalisch konsistente Transformationen von Messdaten, Hinzufügen von Metadaten oder Unsicherheiten. Ziel ist die Verbesserung von Modellgeneralisation, Robustheit und Interpretierbarkeit, insbesondere bei datengestützten Werkstoffdesign- und Prozessmodellen.

Verwandte Tags

Neue Inhalte

A deep learned nanowire segmentation model using synthetic data augmentation

Lin, B. (Speaker)¹; Xu, B.-X.¹

Einsatz von VQ-VAE und GPT zur Generierung synthetischer Schliffbilder von Lamellengraphit

Goedecke, M. (V)¹; Pfaff, M.¹

59. Metallographie-Tagung 2025 - Materialographie

Verbesserung der Nutzbarkeit von Deep Learning Modellen zur Erkennung von Defekten in Li-Ionen Batterien

Bäuml, J. (V)¹; Bernthaler, T.¹; Fuchs, K.¹; Jansche, A.¹; Schneider, G.¹

59. Metallographie-Tagung 2025 - Materialographie

Ultrasonic Guided Wave Signal Transformation with Polynomial Artificial Neural Networks for Data Augmentation

Polle, C. (V)¹; Bosse, S.²; May, D.¹

International Conference on System-Integrated Intelligence - SysInt 2025

Explainable Residual Attention U-Net for Advancing Damage Diagnostics in Fibre Metal Laminates

Kumar, S. (V)¹

International Conference on System-Integrated Intelligence - SysInt 2025

Using machine learning to classify pore types in ceramic replica foams

Dreyer, J.¹; Fey, T. (Speaker)²

8th International Conference on Cellular Materials - CellMAT 2024

Oral-Poster-Presentation

AI-assisted orientation map processing for martensite/bainite/ferrite identification

Lavrskyi, M. (Speaker)¹; Lavrskyi, M.²; Gey, N.¹; Charobert, P.³; Loukachenko, N.³; Couturier, A.³; Somrani, S.²; Germain, L.¹

Conference on Artificial Intelligence in Materials Science and Engineering - AI MSE 2023

Oral-Poster-Presentation

Predicting weld-penetration in Laser Welding using Artificial Intelligence

Hayot, V. (Speaker)¹; Alves Ferreira, A.²; Chabrol, G.¹; LECLER, S.³

Conference on Artificial Intelligence in Materials Science and Engineering - AI MSE 2023

Inverse microstructure design by Bayesian optimisation through statistical descriptors

Raßloff, A. (Speaker)¹; Seibert, P.¹; Kästner, M.¹

Conference on Artificial Intelligence in Materials Science and Engineering - AI MSE 2023

Non-destructive defect detection in additive manufacturing using machine learning and image classification

Westphal, E. (Speaker)¹; Seitz, H.¹

Conference on Artificial Intelligence in Materials Science and Engineering - AI MSE 2023

Beliebte Inhalte

Classification of fracture surface types based on SEM images

Botsch, B. (V)¹

MaterialsWeek 2021

Highlight Lecture

Artificial Intelligence Based Classification of Wear Mechanisms

Sieberg, P. (Speaker)¹; Hanke, S.¹

Oral-Poster-Presentation

AI-assisted orientation map processing for martensite/bainite/ferrite identification

Lavrskyi, M. (Speaker)¹; Lavrskyi, M.²; Gey, N.¹; Charobert, P.³; Loukachenko, N.³; Couturier, A.³; Somrani, S.²; Germain, L.¹

Conference on Artificial Intelligence in Materials Science and Engineering - AI MSE 2023

Explainable Residual Attention U-Net for Advancing Damage Diagnostics in Fibre Metal Laminates

Kumar, S. (V)¹

International Conference on System-Integrated Intelligence - SysInt 2025

Oral-Poster-Presentation

Predicting weld-penetration in Laser Welding using Artificial Intelligence

Hayot, V. (Speaker)¹; Alves Ferreira, A.²; Chabrol, G.¹; LECLER, S.³

Conference on Artificial Intelligence in Materials Science and Engineering - AI MSE 2023

ConvNeXt adaptions in convolutional neural networks for defect detection in Li-ion batteries

Jansche, A. (Speaker)¹; Kopp, A.¹; Bernthaler, T.¹; Schneider, G.¹

FEMS EUROMAT 2023

Inverse microstructure design by Bayesian optimisation through statistical descriptors

Raßloff, A. (Speaker)¹; Seibert, P.¹; Kästner, M.¹

Conference on Artificial Intelligence in Materials Science and Engineering - AI MSE 2023

A deep learned nanowire segmentation model using synthetic data augmentation

Lin, B. (Speaker)¹; Xu, B.-X.¹

Using machine learning to classify pore types in ceramic replica foams

Dreyer, J.¹; Fey, T. (Speaker)²

8th International Conference on Cellular Materials - CellMAT 2024

Einsatz von VQ-VAE und GPT zur Generierung synthetischer Schliffbilder von Lamellengraphit

Goedecke, M. (V)¹; Pfaff, M.¹

59. Metallographie-Tagung 2025 - Materialographie

Maschinelles Lernen statt energiedisperser Röntgenspektroskopie: Vorhersage der chemischen Zusammensetzung von Stahl-Einschlüssen nur über Bildinformationen

Britz, D. (V)¹; Müller, M. (V)²; Mücklich, F.²; Scholl, S.³; Staudt, T.³

55. Metallographie-Tagung 2021 - Materialographie

Verbesserung der Nutzbarkeit von Deep Learning Modellen zur Erkennung von Defekten in Li-Ionen Batterien

Bäuml, J. (V)¹; Bernthaler, T.¹; Fuchs, K.¹; Jansche, A.¹; Schneider, G.¹

59. Metallographie-Tagung 2025 - Materialographie

Non-destructive defect detection in additive manufacturing using machine learning and image classification

Westphal, E. (Speaker)¹; Seitz, H.¹

Conference on Artificial Intelligence in Materials Science and Engineering - AI MSE 2023

Ultrasonic Guided Wave Signal Transformation with Polynomial Artificial Neural Networks for Data Augmentation

Polle, C. (V)¹; Bosse, S.²; May, D.¹

International Conference on System-Integrated Intelligence - SysInt 2025

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