Follow
Benjamin Lutz
Benjamin Lutz
Institute for Factory Automation and Production Systems (FAPS), Friedrich-Alexander University
Verified email at faps.fau.de - Homepage
Title
Cited by
Cited by
Year
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
962021
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
792019
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
562018
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
312020
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
282017
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
262019
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
232020
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
172020
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
122021
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
112020
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
62018
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
42020
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
32021
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
32019
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
22023
The system can't perform the operation now. Try again later.
Articles 1–15