Modellbasierte Überwachung von Fräsprozessen/Condition-specific models for universal process monitoring – Model-based monitoring of milling processes
Table of contents
Bibliographic information

Open Access Full access
wt Werkstattstechnik online
Volume 115 (2025), Issue 05
- Authors:
- | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
- Publisher
- VDI fachmedien, Düsseldorf
- Copyright Year
- 2025
- ISSN-Online
- 1436-4980
- ISSN-Print
- 1436-4980
Chapter information
Open Access Full access
Volume 115 (2025), Issue 05
Modellbasierte Überwachung von Fräsprozessen/Condition-specific models for universal process monitoring – Model-based monitoring of milling processes
- Authors:
- | |
- ISSN-Print
- 1436-4980
- ISSN-Online
- 1436-4980
- Preview:
Data-based process monitoring makes it possible to precisely analyze milling processes and detect anomalies at an early stage – without the need to retrofit additional sensors. By using specialized models and self-learning mechanisms, varying production conditions can be efficiently mapped, thereby increasing overall equipment effectiveness and manufacturing quality in production environments with
a high number of variants.
Bibliography
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- [1] Peichl, A.; Sauer, S.; Wohlrabe, K.: Fachkräftemangel in Deutschland und Europa – Historie, Status quo und was getan werden muss. ifo Schnelldienst Nr. 10 (2022), S. 70–75 Open Google Scholar DOI: 10.37544/1436-4980-2025-05-6
- [2] Gönnheimer, P.; Netzer, M.; Lange, C. et al.: Datenaufnahme und -verarbeitung in der Brownfield-Produktion: Studie zum Stand der Digitalisierung und bestehenden Herausforderung im Produktionsumfeld. Zeitschrift für wirtschaftlichen Fabrikbetrieb 117 (2022) 5, S. 317–320 Open Google Scholar DOI: 10.37544/1436-4980-2025-05-6
- [3] Samochowiec, J.; Bauer, J.; Neumüller, K.: Strategies for dealing with the labour shortage – An overview. SSRN Journal (2023) Open Google Scholar DOI: 10.37544/1436-4980-2025-05-6
- [4] Hadad, Y.; Keren, B.: A revised method for allocating the optimum number of similar machines to operators. International Journal of Productivity and Performance Management 65 (2016) 2, pp. 223–244 Open Google Scholar DOI: 10.37544/1436-4980-2025-05-6
- [5] Abellan-Nebot, J. V.; Romero Subirón, F.: A review of machining monitoring systems based on artificial intelligence process models. The International Journal of Advanced Manufacturing Technology 47 (2010) 1–4, pp. 237–257 Open Google Scholar DOI: 10.37544/1436-4980-2025-05-6
- [6] Hansjosten, M.; Bott, A.; Puchta, A. et al.: Model-Based Diagnosis of Feed Axes with Contactless Current Sensing. In: Liewald, M.; Verl, A.; Bauernhansl, T.; Möhring, H.-C. (Hrsg.): Production at the Leading Edge of Technology. Lecture Notes in Production Engineering. Cham: Springer International Publishing (2023), pp. 314–323 Open Google Scholar DOI: 10.37544/1436-4980-2025-05-6
- [7] Mohanraj, T.; Kirubakaran, E. S.; Madheswaran, D. K. et al.: Review of advances in tool condition monitoring techniques in the milling process. Measurement Science and Technology 35 (2024) 9, pp. 092002. Open Google Scholar DOI: 10.37544/1436-4980-2025-05-6
- [8] Möhring, H.-C.; Litwinski, K. M.; Gümmer, O.: Process monitoring with sensory machine tool components. CIRP Annals 59 (2010) 1, pp. 383–386 Open Google Scholar DOI: 10.37544/1436-4980-2025-05-6
- [9] Xi, T.; Benincá, I. M.; Kehne, S. et al.: Tool wear monitoring in roughing and finishing processes based on machine internal data. The International Journal of Advanced Manufacturing Technology 113 (2021) 11–12, pp. 3543–3554 Open Google Scholar DOI: 10.37544/1436-4980-2025-05-6
- [10] Brillinger, M.; Wuwer, M.; Abdul Hadi, M. et al.: Energy prediction for CNC machining with machine learning. CIRP Journal of Manufacturing Science and Technology 35 (2021), pp. 715–723 Open Google Scholar DOI: 10.37544/1436-4980-2025-05-6
- [11] Schmitt, A.-M.; Miller, E.; Engelmann, B. et al.: G-code evaluation in CNC milling to predict energy consumption through Machine Learning. Advances in Industrial and Manufacturing Engineering 8 (2024), pp. 100140 Open Google Scholar DOI: 10.37544/1436-4980-2025-05-6
- [12] Netzer, M.; Bach, J.; Puchta, A. et al.: Process Segmented based Intelligent Anomaly Detection in Highly Flexible Production Machines under Low Machine Data Availability. Procedia CIRP 107 (2022), pp. 647–652 Open Google Scholar DOI: 10.37544/1436-4980-2025-05-6
- [13] Ströbel, R.; Probst, Y.; Deucker, S. et al.: Time Series Prediction for Energy Consumption of Computer Numerical Control Axes Using Hybrid Machine Learning Models. Machines 11 (2023) 11, pp. 1015 Open Google Scholar DOI: 10.37544/1436-4980-2025-05-6
- [14] Ströbel, R.; Mau, M.; Hafez, K. et al.: Training and validation dataset 3 of milling processes for time series prediction. Karlsruher Institut für Technologie (2024) Open Google Scholar DOI: 10.37544/1436-4980-2025-05-6
- [15] Geurts, P.; Ernst, D.; Wehenkel, L.: Extremely randomized trees. Machine Learn 63 (2006) 1, pp. 3–42 Open Google Scholar DOI: 10.37544/1436-4980-2025-05-6