| Porosity in wire arc additive manufacturing of aluminium alloys T Hauser, RT Reisch, PP Breese, BS Lutz, M Pantano, Y Nalam, K Bela, ... Additive Manufacturing 41, 101993, 2021 | 111 | 2021 |
| Machine Learning in Production–Potentials, Challenges and Exemplary Applications A Mayr, D Kißkalt, M Meiners, B Lutz, F Schäfer, R Seidel, A Selmaier, ... Procedia CIRP 86, 49-54, 2019 | 84 | 2019 |
| Evaluation of Machine Learning for Quality Monitoring of Laser Welding Using the Example of the Contacting of Hairpin Windings A Mayr, B Lutz, M Weigelt, T Gläßel, D Kißkalt, M Masuch, A Riedel, ... 2018 8th International Electric Drives Production Conference (EDPC), 1-7, 2018 | 57 | 2018 |
| A human-cyber-physical system approach to lean automation using an industrie 4.0 reference architecture M Pantano, D Regulin, B Lutz, D Lee Procedia Manufacturing 51, 1082-1090, 2020 | 33 | 2020 |
| Distance-Based Multivariate Anomaly Detection in Wire Arc Additive Manufacturing R Reisch, T Hauser, B Lutz, M Pantano, T Kamps, A Knoll 2020 19th IEEE International Conference on Machine Learning and Applications …, 2020 | 30 | 2020 |
| Potentials of machine learning in electric drives production using the example of contacting processes and selective magnet assembly A Mayr, A Meyer, J Seefried, M Weigelt, B Lutz, D Sultani, M Hampl, ... 2017 7th International Electric Drives Production Conference (EDPC), 1-8, 2017 | 30 | 2017 |
| Evaluation of Deep Learning for Semantic Image Segmentation in Tool Condition Monitoring B Lutz, D Kisskalt, D Regulin, R Reisch, A Schiffler, J Franke 2019 18th IEEE International Conference On Machine Learning And Applications …, 2019 | 28 | 2019 |
| Streamlining the development of data-driven industrial applications by automated machine learning D Kißkalt, A Mayr, B Lutz, A Rögele, J Franke Procedia CIRP 93, 401-406, 2020 | 18 | 2020 |
| In-situ identification of material batches using machine learning for machining operations B Lutz, D Kisskalt, A Mayr, D Regulin, M Pantano, J Franke Journal of Intelligent Manufacturing 32, 1485-1495, 2021 | 14 | 2021 |
| Benchmark of Automated Machine Learning with State-of-the-Art Image Segmentation Algorithms for Tool Condition Monitoring B Lutz, R Reisch, D Kisskalt, B Avci, D Regulin, A Knoll, J Franke Procedia Manufacturing 51, 215-221, 2020 | 12 | 2020 |
| Development of a joining gap control system for laser welding of zinc-coated steel sheets driven by process observation F Tenner, E Eschner, B Lutz, M Schmidt Journal of Laser Applications 30 (3), 2018 | 7 | 2018 |
| AI-based Approach for Predicting the Machinability under Consideration of Material Batch Deviations in Turning Processes B Lutz, D Kisskalt, D Regulin, J Franke Procedia CIRP 93, 1382-1387, 2020 | 4 | 2020 |
| Material Identification for Smart Manufacturing Systems: A Review B Lutz, D Kisskalt, D Regulin, T Hauser, J Franke 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems …, 2021 | 3 | 2021 |
| Elektromotorenproduktion 4.0 A Mayr, B Lutz, M Weigelt, T Gläßel, J Seefried, D Kißkalt, J Franke Zeitschrift für wirtschaftlichen Fabrikbetrieb 114 (3), 145-149, 2019 | 3 | 2019 |
| Interactive Image Segmentation Using Superpixels and Deep Metric Learning for Tool Condition Monitoring B Lutz, L Janisch, D Kisskalt, D Regulin, J Franke Procedia CIRP 118, 459-464, 2023 | 2 | 2023 |
Jörg FrankeFriedrich-Alexander University of Erlangen-Nürnberg在 faps.uni-erlangen.de 的电子邮件经过验证
