Deep Learning

Machine Learning in Werkstofftechnik

Deep Learning

Deep Learning bezeichnet den Einsatz mehrschichtiger neuronaler Netze, um komplexe Muster in großen Datensätzen zu erkennen. In den Materialwissenschaften wird es zur Vorhersage von Eigenschaften, Optimierung von Herstellungsprozessen und Entdeckung neuer Werkstoffe eingesetzt.

Verwandte Tags

Neues

Quantitative precipitate analysis of an age-hardenable aluminum alloy using a deep learning approach

Bayerlein, B. (Speaker)¹; Zia, G.A.J. (Speaker)¹; Moreno Torres, B.¹; Skrotzki, B.¹

Deep learning image segmentation enabling automated quantitative microstructure analysis for manufacturing environments

Pham, G.-K. (Speaker)¹; Lampenscherf, S.²

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

Dissecting the Biomolecular Interactions at Materials’ Surfaces Using Deep Learning Approaches

Dadfar, B. (Speaker)¹

Deep Learning-based automated defect classification for Powder Bed Fusion

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

FEMS EUROMAT 2023

Comparative evaluation of Detectron2 Mask R-CNN and YOLOV8-Seg for microstructural image analysis in high-temperature superalloys

Iwediba, I. (Speaker)¹; Vorontsov, V.¹; Wong, A.¹

Towards explainable and interpretable machine learning for precise fatigue crack detection

Melching, D. (V)¹; Breitbarth, E.¹; Requena, G.¹; Strohmann, T.¹

MaterialsWeek 2021

Artificial Intelligence for materials industry: an open online course using open data

Jaeken, C. (Speaker)¹; Cottenier, S.²; Lis, B.¹; Sluydts, M.¹; Tomecka, D.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

Guided Diffusion for 3D Virtual Staining of Bone Implants

Constante Guedert, B. (Speaker)¹; Irvine, S.²; Zeller-Plumhoff, B.²

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

Beliebt

Vergleich von tabellarischen Daten und Bild basierender Einschlussklassen-Kategorisierung

R. Babu, S., Musi, R. and K. Michelic, S.. "Comparison between image...

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

Poster-Pitch-Presentation

Deep learning assisted investigation of drug-carrier interactions using drying droplet pattern

Elrawy, A. (Speaker)¹; Koenig, M.¹; Lahann, J.²; Théato, P.¹

NextGen Materials 2025: The Convergence of Living Essence and Engineered Innovation

Materials characterization using deep learning image analysis

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

Quantitative precipitate analysis of an age-hardenable aluminum alloy using a deep learning approach

Bayerlein, B. (Speaker)¹; Zia, G.A.J. (Speaker)¹; Moreno Torres, B.¹; Skrotzki, B.¹

Schädigungsmechanismenbasierte Quantifikation des Laminatrandeffektes in faserverstärkten Kunststoffen unter Ermüdungsbeanspruchung

Helwing, R. (V)¹; Hülsbusch, D.¹; Walther, F.¹

40. Vortrags- und Diskussionstagung Werkstoffprüfung 2022

A raw data approach for porosity prediction in PBF-LB/M based on thermographic image sequences

Oster, S. (Speaker)¹; Altenburg, S.J.¹; Becker, T.¹; Breese, P.P.¹; Scheuschner, N.¹

4th Symposium on Materials and Additive Manufacturing

Von der quantitativen Metallografie zur Mikrostrukturinformatik: Aufklärung von Elementarprozessen der dendritischen Erstarrung in einkristalliner Ni-Basis Superlegierungen

Richter, A.¹; Scholz, F.²; Frenzel, J.¹; Eggeler, G. (V)¹; Thome, P.³

59. Metallographie-Tagung 2025 - Materialographie

Deep learning image segmentation enabling automated quantitative microstructure analysis for manufacturing environments

Pham, G.-K. (Speaker)¹; Lampenscherf, S.²

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

A Semantic Segmentation Methodology for Macroscale Fracture Surfaces Analysis

Tlatlik, J. (Speaker)¹; Rosenberger, J.¹; Münstermann, S.²

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

Oral-Poster-Presentation

A new approach of training a deep learning algorithm for phase segmentation of steel microstructures

Chaurasia, N. (Speaker)¹; Jha, S.K.¹; Sangal, S.¹

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

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

Plenary Lecture

Four-Dimensional (4D) Materials Science: Probing Microstructural Evolution of Materials in Real-Time

Chawla, N. (Speaker)¹

Microstructure informatics: Digital microscopy & quantitative characterization of microstructures

Thome, P. (Speaker)¹; Eggeler, G.²; Frenzel, J.²; Richter, A.²; Scholz, F.²

Surrogate Models for Woven Composites Homogenization with Deep Multi-Domain Adaptation and Contrastive Learning

Ghane, E. (Speaker)¹; Taghiyarrenani, Z.²

Dissecting the Biomolecular Interactions at Materials’ Surfaces Using Deep Learning Approaches

Dadfar, B. (Speaker)¹

Automated Sample Extraction and Semantic Segmentation for industrial Image Analysis of Charpy V-notch samples using Machine Learning

Herges, A. (Speaker)¹; Bachmann, B.-I.¹; Mücklich, F.¹; Müller, M.¹; Scholl, S.²

A generic grain boundary detection model for quantitative grain size analysis from materials microscopy images

Rathod, K. (Speaker)¹; Choudhary, A.K.¹; Jansche, A.¹; Trier, F.¹; Ketzer-Raichle, G.¹; Bernthaler, T.¹; Schneider, G.¹

Uncovering process-structure relationships in inert gas atomization of high-alloy tool steels using a convolutional neural network embedded in a quantitative metallography and stereology workflow

Benito, S.M. (Speaker)¹; Weber, S.¹

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

KI-gestützte Gefügeuntersuchung - wirklich besser?

Janovsky, A. (V)¹; Hesseling, C.²

56. Metallographie-Tagung 2022 - Materialographie

Vi-Fi – AI-Based Secure Health Monitoring System Using Wi-Fi Signals

Qassem, K.¹; Seyam, S.¹; Salem, Y.¹; Hadi, A. (Speaker)¹; Al-Rajab, M.¹

7th International School and Conference on Biological Materials Science - Bioinspired Materials 2024

Deep Learning Predicts Elasticity Tensors and their Bounds in Homogenization by Links to the Microstructure

Eidel, B. (Speaker)¹

Combining Phase Field Modeling and Deep Learning for Accurate Modeling of Crystal Orientation in Solidification Microstructure obtained by Wire Arc Additive Manufacturing

Klöcker, H. (Speaker)¹; Aboleinein, H.¹; Bergheau, J.-M.²; Herbeaux, A.¹; Maurice, C.¹; Villani, A.¹

Deep Learning-based automated defect classification for Powder Bed Fusion

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

FEMS EUROMAT 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

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

Comparative evaluation of Detectron2 Mask R-CNN and YOLOV8-Seg for microstructural image analysis in high-temperature superalloys

Iwediba, I. (Speaker)¹; Vorontsov, V.¹; Wong, A.¹

Towards explainable and interpretable machine learning for precise fatigue crack detection

Melching, D. (V)¹; Breitbarth, E.¹; Requena, G.¹; Strohmann, T.¹

MaterialsWeek 2021

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

© 2025