Neuronales Netzwerk

Neuronales Netzwerk

Machine Learning in Werkstofftechnik

Neuronales Netzwerk

Neuronale Netzwerke, insbesondere künstliche neuronale Systeme, werden in der Werkstoffwissenschaft zur Analyse und Vorhersage komplexer Materialverhalten eingesetzt. Durch maschinelles Lernen können Zusammenhänge aus großen Datensätzen extrahiert und Prozessparameter optimiert werden.

Verwandte Tags

Neues

Application of neural networks on electron scattering data

Scheunert, J. (Speaker)¹

Utilizing VQ-VAE and GPT to Generate Synthetic Images of Flake Graphite Cast Iron

Goedecke, M.¹; Pfaff, M.¹; Kaip, M. (V)²

59. Metallographie-Tagung 2025 - Materialographie

Generative Adversarial Networks for Generation of Synthetic High Entropy Alloys

Precker, C. (Speaker)¹; Gregores-Coto, A.¹; Muíños-Landín, S.¹

FEMS EUROMAT 2023

Generative Adversarial Networks for Generation of Synthetic High Entropy Alloys

Gregores Coto, A. (Speaker)¹; Precker, C.E. (Speaker)¹; Muiños Landin, S.¹

FEMS EUROMAT 2023

Efficient inverse design of spinodoid architected materials

Kästner, M. (Speaker)¹

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

Lessons learned during on-site AI analysis of synchrotron diffraction data

Gussone, J. (Speaker)¹; Bugelnig, K.¹; Requena, G.¹; Schell, N.²; Stark, A.²; Strohmann, T.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

Neural network assisted X-ray quality control of metal foams

Kammbach, T.¹; Seeliger, H.W.²; Kamm, P.H.³; Garcia-Moreno, F. (Speaker)⁴

8th International Conference on Cellular Materials - CellMAT 2024

Data-integrated Artificial Intelligence in Kadi4Mat: an Overview

Koeppe, A. (Speaker)¹; Griem, L.¹; Grolig, J.¹; Nestler, B.¹; Rajagopal, D.¹; Selzer, M.¹; Tosato, G.¹; Zhao, Y.¹

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

Employing deep generative models for metamaterials with curvy beams

Slesarenko, V. (Speaker)¹; Felsch, G.¹; Ghavidelnia, N.¹; Schwarz, D.¹

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

Beliebt

Maschinelles Lernen zur Gefügequantifizierung verschiedener Werkstoffklassen

© Carl Hanser Verlag GmbH & Co. KG ISSN 0032-678X

Identification of microstructural fatigue degradation with machine learning

Heckmann, K. (Speaker)¹; Arndt, J.¹; Sievers, J.²; Bill, T.³; Starke, P.³; Donnerbauer, K.⁴; Walther, F.⁴; Yerrapa Reddy, B.⁵; Boller, C.⁵; Silber, F.⁶; Veile, G.⁶

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

Neural network assisted X-ray quality control of metal foams

Kammbach, T.¹; Seeliger, H.W.²; Kamm, P.H.³; Garcia-Moreno, F. (Speaker)⁴

8th International Conference on Cellular Materials - CellMAT 2024

Materials Informatics: A short overview and an example of a neural network interatomic potential for Magnesium

Stricker, M. (V)¹

MaterialsWeek 2021

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

DRAGen - A Tool for Microstructure Reconstruction and an Application for Fatigue Modelling

Henrich, M. (Speaker)¹; Dölz, M.¹; Fehlemann, N.¹; Münstermann, S.¹

FEMS EUROMAT 2023

Application of neural networks on electron scattering data

Scheunert, J. (Speaker)¹

Training Machine Learning Material Models for Finite Element Simulations Based on Data available from Physical Experiments

Böhringer, P. (Speaker)¹; Middendorf, P.²; Sommer, D.²; Stoll, M.³

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

Employing deep generative models for metamaterials with curvy beams

Slesarenko, V. (Speaker)¹; Felsch, G.¹; Ghavidelnia, N.¹; Schwarz, D.¹

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

Utilizing VQ-VAE and GPT to Generate Synthetic Images of Flake Graphite Cast Iron

Goedecke, M.¹; Pfaff, M.¹; Kaip, M. (V)²

59. Metallographie-Tagung 2025 - Materialographie

Plenary lecture

AI-enabled advanced laser remelting process for polishing and structuring of tooling and functional surfaces

Bordatchev, E. (Speaker)¹

Laser Precision Microfabrication (LPM) 2022

A framework to describe the elastic-plastic deformation behavior of foam-like media using neural networks

Abendroth, M. (Speaker)¹; Hütter, G.²; Kiefer, B.¹; Malik, A.¹

Efficient inverse design of spinodoid architected materials

Kästner, M. (Speaker)¹

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

Application of deep neural networks for computationally efficient thermal modelling of metal additive manufacturing

Gh Ghanbari, P. (Speaker)¹; Hosseini, E.¹; Mazza, E.²

Application of deep learning to multi-phase-field modelling and simulation

Elfetni, S. (Speaker)¹; Kamachali, R.D.¹

FEMS EUROMAT 2023

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

Plenary Lecture

Dahmen, T. (Speaker)¹

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

Data-integrated Artificial Intelligence in Kadi4Mat: an Overview

Koeppe, A. (Speaker)¹; Griem, L.¹; Grolig, J.¹; Nestler, B.¹; Rajagopal, D.¹; Selzer, M.¹; Tosato, G.¹; Zhao, Y.¹

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

Generative Adversarial Networks for Generation of Synthetic High Entropy Alloys

Precker, C. (Speaker)¹; Gregores-Coto, A.¹; Muíños-Landín, S.¹

FEMS EUROMAT 2023

Oral-Poster-Präsentation

Image processing methods and light optical microscopy for in-situ quantification of chromatic change and layer dilation in Li-ion battery graphite anodes during (de-)lithiation

Jansche, A. (V)¹; Bernthaler, T.¹; Choudhary, A.K.¹; Desapogu, S.¹; Hogrefe, C.²; Kopp, A.¹; Schneider, G.¹; Trier, F.¹; Waldmann, T.²; Weisenberger, C.¹; Wohlfahrt-Mehrens, M.²

56. Metallographie-Tagung 2022 - Materialographie

Generative Adversarial Networks for Generation of Synthetic High Entropy Alloys

Gregores Coto, A. (Speaker)¹; Precker, C.E. (Speaker)¹; Muiños Landin, S.¹

FEMS EUROMAT 2023

Oral-Poster-Presentation

In-situ HRTEM experiments evaluated with neural networks

Leist, C. (Speaker)¹; Biskupek, J.¹; Fernandes, J.²; Kaiser, U.¹; Khlobystov, A.²; Qi, H.¹

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

© 2025