Maschinenlernen

Machine Learning in Werkstofftechnik

Maschinenlernen

Maschinenlernen wird in den Materialwissenschaften eingesetzt, um große Datensätze auszuwerten, Muster zu erkennen und Vorhersagen über Werkstoffeigenschaften zu treffen. Dieser datengetriebene Ansatz unterstützt die Optimierung von Herstellungsprozessen und die Entdeckung neuer Materialkombinationen.

Verwandte Tags

Neues

Automated AI-based grain boundary detection of a single-phase ceramic microstructure

Klein, J. (V)¹

MaterialsWeek 2021

Unsupervised Machine Learning-Based STEM Diffraction Pattern Denoising for Enhanced Clustering and Grain Visualization in Phase Change Materials

Ruzaeva, K. (Speaker)¹; Weber, D.¹; Sandfeld, S.¹

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

Big Mineral Data and Artificial Intelligence: Mineral Profiles as Biomarkers in Neurodegenerative Diseases

Tillement, A.¹; Tillement, O.²; Decker, Y.³; Duroux, C.⁴; Hagège, A. (Speaker)⁵

EWCPS 2025 - 20th European Winter Conference on Plasma Spectrochemistry

Phase Prediction and Inverse Design of BCC Refractory High-Entropy Alloys using Meta-Heuristic Optimization and Explainable AI

Park, J. (Speaker)¹; Oh, C.-S.¹

Machine learning for post-processing of tomography in battery electrodes as a feature of the DataCharge.IO platform

Perius, D. (Speaker)¹; Beran, L.²; Heim, Y.³; Kraus, T.²; Nebel, V.³; Werth, D.³

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

A Machine Learning Approach to the estimation of geometrical quality on single scan tracks produced by Laser Powder Directed Energy Deposition

Wong, V. (Speaker)¹; Lahr, G.²; de Paula, G.³; Rodrigues, A.³; Teixeira, R.³

FEMS EUROMAT 2023

Automated Grain Density Estimation in Austenitic and Martensitic Alloys

Ilic, F. (V)¹; Bogensperger, L.¹; Ganster, H.²; Masana, M.¹; Pock, T.¹

56. Metallographie-Tagung 2022 - Materialographie

Machine learning based models for thermal ageing of Ni-base superalloys in adverse environment

Brückner-Foit, A. (V)¹; Rienäcker, A.¹; Senge, M.¹; Steger, J.¹

MaterialsWeek 2021

When is a triangle a triangle?

Bals, J. (V)¹; Epple, M.¹; Loza, K.¹

MaterialsWeek 2021

Project DigitalModelling - Data Analysis and Synthesis

Jahnke, A. (Speaker)¹; Naumenko, K.¹; Trelles, E.G.²; Schweizer, C.²; Kohler, C.³; Huang, M.³; Nöhring, W.⁴

MaterialDigital General Assembly 2025

Beliebt

Poster pitch presentation

Data-Driven Discovery of Coordination Compounds for Nonlinear Optical Terahertz Generation

Siwiak, A. (Speaker)¹; Forte, G.²; Gorczyński, A.¹

Machine learning-assisted equivalent circuit identification for dielectric spectroscopy of polymers

Albakri, B. (Speaker)¹; Diniz, A.²; Benner, P.¹; Muth, T.³; Nakajima, S.⁴; Favaro, M.²; Kister, A.¹

(U)Mapping the chemical landscape of Halide Double Perovskites.

Walterbos, L. (Speaker)¹; McEwan, A.²; Shinde, R.²; George, J.¹; Leppert, L.²

Predicting mechanical properties in aluminum alloys: A data-driven framework leveraging LLM-based data extraction and physics-based feature engineering

Pichlmann, L. (Speaker)¹; Österreicher, J.¹

Discovering physical formulas with deep symbolic regression - a universal crack tip correction algorithm

Melching, D. (Speaker)¹; Breitbarth, E.¹

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

Vergleich von tabellarischen Daten und Bild basierender Einschlussklassen-Kategorisierung

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

NIST-JARVIS infrastructure for Improved Materials Design

Choudhary, K. (Speaker)¹

Applied machine learning for rapid alloy development

Strohmann, T. (Speaker)¹; Bugelnig, K.¹; Tumminello, S.¹; Requena, G.¹

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

Poster - Young Scientist Poster

Fractal analysis for characterization of ultrashort pulse laser textured surfaces enabling machine learning powered categorization

Peters, O. (Speaker)¹; Löschner, U.¹; Schille, J.¹

Laser Precision Microfabrication (LPM) 2022

Keynote Lecture

Towards accurate determination of materials parameters for multiscale modeling from first principles

Abrikosov, I. (Speaker)¹

FEMS EUROMAT 2023

Toward Hierarchical Modeling: Machine Learning for Describing Elastic-Plastic Behavior in Nanoporous Metals

Dyckhoff, L. (Speaker)¹; Huber, N.¹

Advancing solid interfaces and lubricants by first principles materials design

Righi, M.C. (Speaker)¹

6th European Symposium on Friction, Wear and Wear Protection - Friction 2024

Crystal-structure identification via Bayesian deep learning

Leitherer, A. (Speaker)¹; Ghiringhelli, L.M.²; Ziletti, A.²

Collecting FAIR data as a base for machine learning in tribology

Garabedian, N.¹; Flachmann, M. (Speaker)¹; Bagov, I.¹; Biesinger, J.¹; Gorenflo, M.²; Ye, N.¹; Bresser, F.¹; Meller, M.³; Greiner, C.¹

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

Comparison of atomic environment descriptors with domain knowledge of the interatomic bond

Forti, M. (Speaker)¹; Drautz, R.¹; Hammerschmidt, T.¹

FEMS EUROMAT 2023

Microstructural Quantifications for Accelerated Prediction of Vickers Hardness Using Machine Learning

Swetlana, S. (Speaker)¹; Khatavkar, N.¹; Singh, A.K.¹

FEMS EUROMAT 2023

Bayesian inference framework for prediction of material properties from microstructure images based on generative models

Noguchi, S. (Speaker)¹; Inoue, J.²

Maschinelles Lernen zur Klassifizierung makroskaliger Bruchflächen

Herges, A., Ulrich, L., Scholl, S., Müller, M., Britz, D. and...

Multiscale Modelling of Bulk Organic Systems using Graph Convolutional Neural Networks

Ricci, E.¹; Gerakinis, D.-P.¹; Giannakopoulos, G.¹; Karkaletsis, V.¹; Theodorou, D.²; Vergadou, N. (Speaker)¹

FEMS EUROMAT 2023

Architectured Lattice Materials with Mesoscale

Kulagin, R. (Speaker)¹; Beygelzimer, Y.²; Estrin, Y.³; Friederich, P.¹; Gumbsch, P.⁴; Koeppe, A.¹; Reiser, P.¹; Truskovskyi, K.⁵

Optimum design of entropy alloy composition in chromium iron nickel

Ren, W. (Speaker)¹; Wang, W.-L.²; Zhang, Y.-X.³

FEMS EUROMAT 2023

Material and Process Data Integration: Enhancing Hybrid Material Design through Ontology-Based Data Management

Bjarsch, T. (Speaker)¹

Nano | Human Interfaces (NHI), a Lehigh University Presidential Initiative

Moyer, L. (V)¹

56. Metallographie-Tagung 2022 - Materialographie

Development of Model and Sensor-Supported Processes for Massive Forming of Titanium Materials With Optimal Microstructure

Farooqui, M.M.U. (Speaker)¹; Bisterfeld, F.¹; Katrakova-Krüger, D.¹; Witulski, T.²; Wolf, C.¹

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

Prediction of relevant parameters for Perovskite Solar Cells stability through mutual information

Velez Sanchez, J.E. (Speaker)¹; Botero Londoño, M.A.¹; Sepulveda Sepulveda, F.A.¹

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

Designing high strength alloys for additive manufacturing using combined experimental and machine learning approaches

Hariharan, A. (Speaker)¹; Ackermann, M.²; Koß, S.³; Khosravani, A.⁴; Kalidindi, S.⁴; Koehnen, P.⁵; Haase, C.⁶

4th Symposium on Materials and Additive Manufacturing

Highlight Lecture

Machine-Learning based microstructure evaluation of Li-ion batteries for quality assessment and process development

Kopp, A. (Speaker)¹; Bernthaler, T.²; Choudhary, A.-K.³; Jansche, A.²; Schneider, G.²; Trier, F.²

FEMS EUROMAT 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

Exploring refractory high entropy alloys hyper-space using high-throughput material science and machine learning methods

Wieczerzak, K. (Speaker)¹; Groetsch, A.¹; Michler, J.¹; Schwiedrzik, J.¹; Widmer, R.¹

FEMS EUROMAT 2023

© 2025