Top

Published in:

2021 | OriginalPaper | Chapter

Concept for Predicting Vibrations in Machine Tools Using Machine Learning

Authors : D. Barton, J. Fleischer

Published in: Production at the leading edge of technology

Publisher: Springer Berlin Heidelberg

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Vibrations have a significant influence on quality and costs in metal cutting processes. Existing methods for predicting vibrations in machine tools enable an informed choice of process settings, however they rely on costly equipment and specialised staff. Therefore, this contribution proposes to reduce the modelling effort required by using machine learning based on data gathered during production. The approach relies on two sub-models, representing the machine structure and machining process respectively. A method is proposed for initialising and updating the models in production.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

previous chapter Development of a Temperature Strategy for Motor Spindles with Synchronous Reluctance Drive Using Multiple Linear Regression and Neural Network

next chapter Automated Profiling of Energy Data in Manufacturing

Altintas, Y., Kersting, P., Biermann, D., Budak, E., Denkena, B., Lazoglu, I.: Virtual process systems for part machining operations. CIRP Ann. (2014). https://doi.org/10.1016/j.cirp.2014.05.007CrossRef

Brecher, C., Esser, M., Witt, S.: Interaction of manufacturing process and machine tool. CIRP Ann. (2009). https://doi.org/10.1016/j.cirp.2009.09.005CrossRef

Schwarz, S.: Prognosefähigkeit dynamischer Simulationen von Werkzeugmaschinenstrukturen. Dissertation. Forschungsberichte IWB, vol. 313 (2015)

Bonin, T.: Moderne Ordnungsreduktionsverfahren für die Simulation des dynamischen Verhaltens von Werkzeugmaschinen. Dissertation. Forschungsberichte IWB, Band 306 (2015)

Pfrommer, J., Zimmerling, C., Liu, J., Kärger, L., Henning, F., Beyerer, J.: Optimisation of manufacturing process parameters using deep neural networks as surrogate models. Procedia CIRP (2018). https://doi.org/10.1016/j.procir.2018.03.046CrossRef

Reuß, M., Dadalau, A., Verl, A.: Friction variances of linear machine tool axes. Procedia CIRP (2012). https://doi.org/10.1016/j.procir.2012.10.021CrossRef

Brecher, C., Weck, M.: Werkzeugmaschinen, Fertigungssysteme 2. Konstruktion, Berechnung und messtechnische Beurteilung, 9th edn. VDI-Buch. Springer Vieweg, Berlin (2017)

Berthold, J., Kolouch, M., Wittstock, V., Putz, M.: Broadband excitation of machine tools by cutting forces for performing operation modal analysis. MM SJ (2016). https://doi.org/10.17973/MMSJ.2016_11_2016164

Putz, M., Wittstock, V., Kolouch, M., Berthold, J.: Investigation of the time-invariance and causality of a machine tool for performing operational modal analysis. Procedia CIRP (2016). https://doi.org/10.1016/j.procir.2016.04.052CrossRef

10.

Li, B., Cai, H., Mao, X., Huang, J., Luo, B.: Estimation of CNC machine–tool dynamic parameters based on random cutting excitation through operational modal analysis. Int. J. Mach. Tools Manuf. (2013). https://doi.org/10.1016/j.ijmachtools.2013.04.001CrossRef

11.

Karkalos, N.E., Galanis, N.I., Markopoulos, A.P.: Surface roughness prediction for the milling of Ti–6Al–4V ELI alloy with the use of statistical and soft computing techniques. Measurement (2016). https://doi.org/10.1016/j.measurement.2016.04.039CrossRef

12.

Cherukuri, H., Perez-Bernabeu, E., Selles, M.A., Schmitz, T.L.: A neural network approach for chatter prediction in turning. Procedia Manuf. (2019). https://doi.org/10.1016/j.promfg.2019.06.159CrossRef

13.

Friedrich, J., Torzewski, J., Verl, A.: Online learning of stability lobe diagrams in milling. Procedia CIRP (2018). https://doi.org/10.1016/j.procir.2017.12.213CrossRef

14.

Denkena, B., Bergmann, B., Reimer, S.: Analysis of different machine learning algorithms to learn stability lobe diagrams. Procedia CIRP (2020). https://doi.org/10.1016/j.procir.2020.05.049CrossRef

15.

Postel, M., Bugdayci, B., Wegener, K.: Ensemble transfer learning for refining stability predictions in milling using experimental stability states. Int. J. Adv. Manuf. Technol. (2020). https://doi.org/10.1007/s00170-020-05322-w

16.

Denkena, B., Dittrich, M.-A., Stamm, S.C., Prasanthan, V.: Knowledge-based process planning for economical re-scheduling in production control. Procedia CIRP (2019). https://doi.org/10.1016/j.procir.2019.03.238CrossRef

17.

Wiederkehr, P., Siebrecht, T.: Virtual machining: capabilities and challenges of process simulations in the aerospace industry. Procedia Manuf. (2016). https://doi.org/10.1016/j.promfg.2016.11.011CrossRef

18.

Saadallah, A., Finkeldey, F., Morik, K., Wiederkehr, P.: Stability prediction in milling processes using a simulation-based machine learning approach. Procedia CIRP (2018). https://doi.org/10.1016/j.procir.2018.03.062CrossRef

19.

Finkeldey, F., Hess, S., Wiederkehr, P.: Tool wear-dependent process analysis by means of a statistical online monitoring system. Prod. Eng. Res. Dev. (2017). https://doi.org/10.1007/s11740-017-0773-0

20.

Quintana, G., Ciurana, J.: Chatter in machining processes: a review. Int. J. Mach. Tools Manuf. (2011). https://doi.org/10.1016/j.ijmachtools.2011.01.001CrossRef

21.

Denkena, B., Hollmann, F. (eds.): Process Machine Interactions. Predicition and Manipulation of Interactions between Manufacturing Processes and Machine Tool Structures. Lecture Notes in Production Engineering. Springer, Berlin (2013)

22.

Guo, M., Ye, Y., Jiang, X., Wu, C.: Comprehensive effect of multi-parameters on vibration in high-speed precision milling. Int. J. Adv. Manuf. Technol. (2020). https://doi.org/10.1007/s00170-020-05441-4

23.

Park, S.S., Altintas, Y., Movahhedy, M.: Receptance coupling for end mills. Int. J. Mach. Tools Manuf. (2003). https://doi.org/10.1016/S0890-6955(03)00088-9CrossRef

24.

Caruana, R., Karampatziakis, N., Yessenalina, A.: An empirical evaluation of supervised learning in high dimensions. In: Proceedings of the 25th International Conference on Machine Learning, pp. 96–103 (2008)

Title: Concept for Predicting Vibrations in Machine Tools Using Machine Learning
Authors: D. Barton
J. Fleischer
Publisher: Springer Berlin Heidelberg
Book: Production at the leading edge of technology
Print ISBN: 978-3-662-62137-0

Electronic ISBN: 978-3-662-62138-7

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-3-662-62138-7_55

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partners