Defekterkennung

Chemische Prozesse in der Werkstofftechnik

Detektion in der Werkstoffcharakterisierung

Defekterkennung

Defekterkennung umfasst experimentelle und rechnergestützte Methoden zur Identifikation von Unregelmäßigkeiten wie Versetzungen, Poren, Rissen oder Einschlüsse in Werkstoffen. Sie nutzt zerstörungsfreie Prüfverfahren (z.B. Ultraschall, Röntgen- und Neutronenbildgebung, EDX/EBSD), bildgebende Auswertung und zunehmend KI-basierte Mustererkennung. Ziel ist die Quantifizierung von Defektart, -größe, -verteilung und deren Einfluss auf Lebensdauer und Zuverlässigkeit.

Verwandte Tags

semantische Segmentierung

Objekterkennung

Konvolutionales Neuronales Netzwerk

Maschinenlernen

Qualitätskontrolle

Lithium-Ionen-Batterien

Spannungsrisskorrosion

Künstliche Intelligenz

Metallographische Analyse

Datenanreicherung

Bildverarbeitung

mikrostrukturelle Veränderungen

Korngrößenverteilung

Digitalisierung & Künstliche Intelligenz

Künstliche Intelligenz

Rasterelektronenmikroskop

Quantitative Gefügeanalyse

Metall-Additive Fertigung

Datenmanagement

Prozessparameter

Titanlegierungen

Duplex-Edelstahl

Röntgentomographie

Lithium-Ionen-Batterien

In situ 2D und 3D Charakterisierung

Anomalieerkennung

Hochdurchsatz-Experimentierung

Neuronales Netzwerk

Lichtoptische Mikroskopie

Selektives Lasersintern

Materialcharakterisierung & -prüfung

Fertigung und Produktionstechnik

Werkstoffe der Energietechnik

Röntgenmikroskopie

Neue Inhalte

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

Data-driven approaches in materials science and microscopy: From image quality to material properties

Jansche, A. (Speaker)¹; Krawczyk, P.¹; Deeg, P.¹; Rathod, K.¹; Choudhary, A.K.¹; Bernthaler, T.¹; Goll, D.¹; Schneider, G.¹

MaterialsWeek 2025

MIPAR Spotlight: Shedding Light on Next-Generation Detection for Steel Applications

Nordqvist, S.¹; Kudlinski, M.²; Stratulat, A.²; Langner, S. (Speaker)³

MaterialsWeek 2025

Development of a high-throughput large-area Scanning Electron Microscopy Framework for Efficient and Accurate Defect Analysis

König, C.¹; Jinschek, J. (Speaker)¹

MaterialsWeek 2025

Unsupervised deep learning approaches for defect detection in light optical microscopy images of Li-ion batteries

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

ML-assisted approaches for digital image analysis to perform reliable failure analysis in microelectronic components

Choudhary, A. (V)¹; Volman-Stern, M.O.¹; Jansche, A.¹; Bernthaler, T.¹; Gounet, P.²; Krinke, J.³; Hollerith, C.⁴

58. Metallographie-Tagung 2024 - Materialographie

In-situ subsurface defect detection in PBF-LB/M with active thermography

Sauer, C. (Speaker)¹; Höfflin, D.¹; Schiffler, A.¹

4th Symposium on Materials and Additive Manufacturing

Invited Lecture

AI supported quality control in the steel industry

Schäfer, M. (Speaker)¹

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

Deep learning approach for the detection of chipping defects in IR microscopy images of microelectronic components for reliable failure analysis

Volman Stern, M.O. (Speaker)¹; Jansche, A.¹; Bernthaler, T.¹; Nagel, A.¹; Gounet, P.²

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

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

Data-driven approaches in materials science and microscopy: From image quality to material properties

Jansche, A. (Speaker)¹; Krawczyk, P.¹; Deeg, P.¹; Rathod, K.¹; Choudhary, A.K.¹; Bernthaler, T.¹; Goll, D.¹; Schneider, G.¹

MaterialsWeek 2025

Deep learning for defect detection in energy materials

Jansche, A. (Speaker)¹; Badmos, O.¹; Bernthaler, T.¹; Goll, D.¹; Hohs, D.¹; Kopp, A.¹; Schiele, T.¹; Schneider, G.¹; Trier, F.¹

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

ML-assisted approaches for digital image analysis to perform reliable failure analysis in microelectronic components

Choudhary, A. (V)¹; Volman-Stern, M.O.¹; Jansche, A.¹; Bernthaler, T.¹; Gounet, P.²; Krinke, J.³; Hollerith, C.⁴

58. Metallographie-Tagung 2024 - Materialographie

Automatic detection of defects in KOH etch images using instance segmentation

Holub, G. (Speaker)¹; Hofer, S.²; Obermüller, T.²; Romaner, L.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

Overview of deep learning methods for image-based defect detection in Li-ion batteries using destructive and non-destructive microscopy methods

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

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

Deep learning approach for the detection of chipping defects in IR microscopy images of microelectronic components for reliable failure analysis

Volman Stern, M.O. (Speaker)¹; Jansche, A.¹; Bernthaler, T.¹; Nagel, A.¹; Gounet, P.²

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

Development of a high-throughput large-area Scanning Electron Microscopy Framework for Efficient and Accurate Defect Analysis

König, C.¹; Jinschek, J. (Speaker)¹

MaterialsWeek 2025

Deep Learning-based automated defect classification for Powder Bed Fusion

Kunkel, N. (Speaker)¹; Thölken, D.¹; Behler, K.¹

FEMS EUROMAT 2023

Unsupervised deep learning approaches for defect detection in light optical microscopy images of Li-ion batteries

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

In-situ subsurface defect detection in PBF-LB/M with active thermography

Sauer, C. (Speaker)¹; Höfflin, D.¹; Schiffler, A.¹

4th Symposium on Materials and Additive Manufacturing

Invited Lecture

AI supported quality control in the steel industry

Schäfer, M. (Speaker)¹

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

MIPAR Spotlight: Shedding Light on Next-Generation Detection for Steel Applications

Nordqvist, S.¹; Kudlinski, M.²; Stratulat, A.²; Langner, S. (Speaker)³

MaterialsWeek 2025

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