| A deep learning approach for complex microstructure inference AR Durmaz, M Müller, B Lei, A Thomas, D Britz, EA Holm, C Eberl, ... Nature communications 12 (1), 6272, 2021 | 48 | 2021 |
| Classification of bainitic structures using textural parameters and machine learning techniques M Müller, D Britz, L Ulrich, T Staudt, F Mücklich Metals 10 (5), 630, 2020 | 38 | 2020 |
| Addressing materials’ microstructure diversity using transfer learning A Goetz, AR Durmaz, M Müller, A Thomas, D Britz, P Kerfriden, C Eberl npj Computational Materials 8 (1), 27, 2022 | 15 | 2022 |
| Machine Learning for Microstructure Classification - How to Assign the Ground Truth in the Most Objective Way? M Müller, D Britz, F Mücklich Advanced Materials & Processes, 16-21, 2021 | 10 | 2021 |
| Microstructural classification of bainitic subclasses in low-carbon multi-phase steels using machine learning techniques M Müller, D Britz, T Staudt, F Mücklich Metals 11 (11), 1836, 2021 | 8 | 2021 |
| Addressing materials’ microstructure diversity using transfer learning. npj Comput A Goetz, AR Durmaz, M Muller, A Thomas, D Britz, P Kerfriden Mater 8 (1), 27, 2022 | 7 | 2022 |
| Image processing using open source tools and their implementation in the analysis of complex microstructures UP Nayak, M Müller, D Britz, MA Guitar, F Mücklich Practical Metallography 58 (8), 484-506, 2021 | 7 | 2021 |
| Scale-bridging microstructural analysis–a correlative approach to microstructure quantification combining microscopic images and EBSD data M Müller, D Britz, F Mücklich Practical Metallography 58 (7), 408-426, 2021 | 7 | 2021 |
| Efficient reconstruction of prior austenite grains in steel from etched light optical micrographs using deep learning and annotations from correlative microscopy BI Bachmann, M Müller, D Britz, AR Durmaz, M Ackermann, O Shchyglo, ... Frontiers in Materials 9, 1033505, 2022 | 5 | 2022 |
| Segmentation of Lath-Like Structures via Localized Identification of Directionality in a Complex-Phase Steel M Müller, G Stanke, U Sonntag, D Britz, F Mücklich Metallography, Microstructure, and Analysis 9, 709-720, 2020 | 3 | 2020 |
| Application of trainable segmentation to microstructural images using low-alloy steels as an example M Müller, D Britz, F Mücklich Practical Metallography 57 (5), 337-358, 2020 | 3 | 2020 |
| Reproducible Quantification of the Microstructure of Complex Quenched and Quenched and Tempered Steels Using Modern Methods of Machine Learning BI Bachmann, M Müller, D Britz, T Staudt, F Mücklich Metals 13 (8), 1395, 2023 | 1 | 2023 |
| Determination of grain size distribution of prior austenite grains through a combination of a modified contrasting method and machine learning M Laub, BI Bachmann, E Detemple, F Scherff, T Staudt, M Müller, D Britz, ... Practical Metallography 60 (1), 4-36, 2022 | 1 | 2022 |
| Chaldene: Towards Visual Programming Image Processing in Jupyter Notebooks F Chen, P Slusallek, M Müller, T Dahmen 2022 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC …, 2022 | 1 | 2022 |
| Improved carbide volume fraction estimation in as-cast HCCI alloys using machine learning techniques UP Nayak, M Müller, N Quartz, MA Guitar, F Mücklich Computational Materials Science 240, 113013, 2024 | | 2024 |
| Enhancing machine learning classification of microstructures: A workflow study on joining image data and metadata in CNN M Stiefel, M Müller, BI Bachmann, MA Guitar, UP Nayak, F Mücklich MRS Communications, 1-9, 2024 | | 2024 |
| New possibilities for macroscopic imaging in test laboratories–Modern light field objective lenses serving as the basis for large-scale 3D topography reconstruction and … M Kasper, M Müller, K Illgner-Fehns, K Stanishev, D Britz, F Mücklich Practical Metallography 59 (8-9), 500-519, 2022 | | 2022 |
