Automated Identification of Components of Feed Axes
Puchta, A.; Frisch, M.; Fleischer, J.
2024. Production at the Leading Edge of Technology. Hrsg.: T. Bauernhansl, 143–151, Springer Nature Switzerland. doi:10.1007/978-3-031-47394-4_15

Jacobian-Sensitivity Approach for Identifying Machine Dynamic Model Parameters of Robots with Flexible Joints
Oexle, F.; Benfer, A.; Puchta, A.; Fleischer, J.
2024. Procedia CIRP - 10th CIRP Conference on Assembly Technology and Systems (CIRP CATS 2024), 127, 116–121. doi:10.1016/j.procir.2024.07.021

Feature Ranking for the Prediction of Energy Consumption on CNC Machining Processes
Kader, H.; Ströbel, R.; Puchta, A.; Fleischer, J.; Noack, B.; Spiliopoulou, M.
2024. 2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), Pilsen, Czech Republic, 04-06 September 2024, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/MFI62651.2024.10705783

Black Box Adversarial Reprogramming for Time Series Feature Classification in Ball Bearings’ Remaining Useful Life Classification
Bott, A.; Schreyer, F.; Puchta, A.; Fleischer, J.
2024. Machine Learning and Knowledge Extraction, 6 (3), 1969 – 1996. doi:10.3390/make6030097

Training and validation dataset 3 of milling processes for time series prediction
Ströbel, R.; Mau, M.; Kader, H.; Erd, D.; Bless, D.; Deucker, S.; Puchta, A.; Fleischer, J.; Noack, B.
2024, June 18. doi:10.35097/feFwILjideOropmh

Improving Time Series Regression Model Accuracy via Systematic Training Dataset Augmentation and Sampling
Ströbel, R.; Mau, M.; Puchta, A.; Fleischer, J.
2024. Machine Learning and Knowledge Extraction, 6 (2), 1072–1086. doi:10.3390/make6020049

Autonome Modellierung von Maschinenverhalten – Ansatz für die autonome Modellierung des dynamischen Verhaltens von Fräsmaschinen und Potenziale dieses Ansatzes in der Industrie
Oexle, F.; Netzer, M.; Deiters, L.; Puchta, A.; Fleischer, J.
2024. ZWF – Zeitschrift für wirtschaftlichen Fabrikbetrieb, 119 (5), 318–323. doi:10.1515/zwf-2024-1054

Concept for Individual and Lifetime-Adaptive Modeling of the Dynamic Behavior of Machine Tools
Oexle, F.; Heimberger, F.; Puchta, A.; Fleischer, J.
2024. Machines, 12 (2), Art.-Nr.: 123. doi:10.3390/machines12020123

Towards a Testing Framework for Machine Learning Model Deployment in Manufacturing Systems
Heider, I.; Baumgärtner, J.; Bott, A.; Ströbel, R.; Puchta, A.; Fleischer, J.
2024. 10th CIRP Conference on Assembly Technology and Systems (CIRP CATS 2024) Hrsg.: Fleischer , Jürgen; Jörg, Krüger, 127, 122–128. doi:10.1016/j.procir.2024.07.022

Sensor- and Data-Supported Sustainable Manufacturing
Fleischer, J.; Hansjosten, M.; Sawodny, J.; Puchta, A.; Gönnheimer, P.
2024. Encyclopedia of Sustainable Technologies, 648–662, Elsevier. doi:10.1016/B978-0-323-90386-8.00128-5

Camera Placement Optimization for a Novel Modular Robot Tracking System
Baumgärtner, J.; Bertschinger, B.; Hoffmann, K.; Puchta, A.; Sawodny, O.; Reichelt, S.; Fleischer, J.
2023. 2023 IEEE SENSORS, Vienna, Austria, 29 October 2023 - 01 November 2023, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/SENSORS56945.2023.10324941

KI-Transferzentrum für die Industrie
Hansjosten, M.; Oexle, F.; Gönnheimer, P.; Heider, I.; Puchta, A.; Fleischer, J.
2023. wt Werkstattstechnik online

Increasing Robot Precision by Stroke Division
Baumgärtner, J.; Puchta, A.; Bertschinger, B.; Kanagalingam, G.; Sawodny, O.; Reichelt, S.; Fleischer, J.
2023. 2023 27th International Conference on Methods and Models in Automation and Robotics (MMAR), Międzyzdroje, Poland, 22-25 August 2023. Ed.: A. Bartoszewicz, 205–210, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/MMAR58394.2023.10242468

Auto-identification of dynamic axis models in machine tools
Puchta, A.; Riegel, V.; Barton, D.; Fleischer, J.
2023. 16th CIRP Conference on Intelligent Computation in Manufacturing Engineering Hrsg.: Teti, Prof Roberto, 118, 175–180. doi:10.1016/j.procir.2023.06.031

Software-Defined Manufacturing for the Entire Life Cycle at Different Levels of Production
Behrendt, S.; Martin, M.; Puchta, A.; Ströbel, R.; Fisel, J.; May, M.; Gönnheimer, P.; Fleischer, J.; Lanza, G.
2023. Advances in Automotive Production Technology – Towards Software-Defined Manufacturing and Resilient Supply Chains : Stuttgart Conference on Automotive Production (SCAP2022). Ed.: N. Kiefl, 25–34, Springer International Publishing. doi:10.1007/978-3-031-27933-1_3

Model-Based Diagnosis of Feed Axes with Contactless Current Sensing
Hansjosten, M.; Bott, A.; Puchta, A.; Gönnheimer, P.; Fleischer, J.
2023. M. Liewald, T. Bauernhansl, H.-C. Möhring & A. Verl (Eds.), Production at the Leading Edge of Technology: Proceedings of the 12th Concress of the German Academic Association for Production Technology (WGP), University of Stuttgart, October 2022. Hrsg.: Mathias Liewald, Thomas Bauernhansl, Hans-Christian Möhring, Alexander Verl, 314–323, Springer. doi:10.1007/978-3-031-18318-8_33

Introduction of an industrial transfer learning use case systematization for machine tools
Netzer, M.; Michelberger, J.; Puchta, A.; Verl, A.; Fleischer, J.
2023. Procedia CIRP, 120, 398 – 403. doi:10.1016/j.procir.2023.09.009

KI-Einsatz in KMU: Einstiegshürden ausräumen [Clearing entry hurdles for AI deployment in SMEs – Artificial intelligence for German SMEs]
Heider, I.; Yu, H.; Krischke, N.; Wirth, B.; Puchta, A.; Fleischer, J.
2023. wt Werkstattstechnik online, 113 (07-08), 282 – 287. doi:10.37544/1436-4980-2023-07-08-16

Convolutional-Based Encoder–Decoder Network for Time Series Anomaly Detection during the Milling of 16MnCr5
Schlagenhauf, T.; Wolf, J.; Puchta, A.
2022. mdpi data, 7 (175), Article no: 175. doi:10.3390/data7120175

Multivariate time series dataset of milling 16MnCr5 for anomaly detection
Schlagenhauf, T.; Wolf, J.; Puchta, A.
2022, October 19. doi:10.5445/IR/1000151546

Process Segmented based Intelligent Anomaly Detection in Highly Flexible Production Machines under Low Machine Data Availability
Netzer, M.; Bach, J.; Puchta, A.; Gönnheimer, P.; Fleischer, J.
2022. Procedia CIRP, 107, 647–652. doi:10.1016/j.procir.2022.05.040

Framework for the Application of Industry 4.0 in Lithium-Ion Battery Cell Production
Schmied, J.; Puchta, A.; Scharmann, T.; Töpper, H.-C.; Kampker, A.; Jürgen, F.; Dröder, K.; Daub, R.
2022. Proceedings of the Conference on Production Systems and Logistics, CPSL 17th - 20th May, Vancouver 2022, 151–160, Leibniz Universität Hannover. doi:10.15488/12168

Industrie 4.0 in der Batteriezellproduktion : Systematische Integration von Industrie 4.0 in der Batteriezellproduktion = Systemic integration of Industry 4.0 in battery cell production – Industry 4.0 in battery cell production
Puchta, A.; Schmied, J.; Scharmann, T.; Töpper, H.; Fleischer, J.; Kampker, A.; Dröder, K.; Daub, R.
2022. WT Werkstattstechnik, 112 (7-8), 496–500. doi:10.37544/1436-4980-2022-07-08-50

Modular and flexible Automation Middleware based on LabVIEW and OPC UA
Künzel, A.; Puchta, A.; Gönnheimer, P.; Fleischer, J.
2021. 9th Manufacturing Engineering Society International Conference (MESIC 2021) 23rd-25th June 2021, Gijόn, Spain, Art.Nr.: 012109, Institute of Physics Publishing Ltd (IOP Publishing Ltd)

Automated Identification of Parameters in Control Systems of Machine Tools
Gönnheimer, P.; Puchta, A.; Fleischer, J.
2021. B.-A. Behrens, A. Brosius, W. Hintze, S. Ihlenfeldt & J. P. Wulfsberg (Eds.), Production at the leading edge of technology – Proceedings of the 10th Congress of the German Academic Association for Production Technology (WGP), Dresden, 23-24 September 2020. Ed.: B.-A. Behrens, 568–577, Springer-Verlag. doi:10.1007/978-3-662-62138-7_57

Seamless and modular architecture for autonomous machine tools
Fleischer, J.; Puchta, A.; Gönnheimer, P.
2021. Journal of Machine Engineering, 21 (3), 40–46. doi:10.36897/jme/141565