Learning machining stability using a bayesian model
DOI:
https://doi.org/10.58368/MTT.22.2.2023.10-16Keywords:
Machining Stability, Bayesian Learning, Machine LearningAbstract
Instabilities in machining can be detrimental. Usually, analytical model-predicted stability charts guide selection of cutting parameters to ensure stable processes. However, since inputs to the model seldom account for the speed-dependent behaviour of the cutting process or the dynamics, models often fail to guide stable cutting parameter selection in real industrial settings. To address this issue, this paper discusses how real experimentally classified stable and unstable cutting data with all its vagaries and uncertainties can instead be used to learn the stability behaviour using a supervised Bayes' learning approach. We expand previously published work to systematically characterize how probability distributions, training data size, and thresholding influence the learning capacity of the Bayesian approach. Prediction accuracies of up to 95% are shown to be possible. We also show how the approach nicely extends itself to a continuous learning process. Results can hence inform further development towards self-optimizing and autonomous machining systems.
Metrics
References
Aggogeri, F., Pellegrini, N., &Tagliani F. L. (2021). Recent advances on machine learning applications in machining processes. Applied sciences, 11(18), 8764. https://doi.org/10.3390/ app11188764
Chen, G., Li, Y., Liu X., & Yang, B. (2021).Physics-informed bayesian inference for milling stability analysis.International Journal of Machine Tools and Manufacture, 167. https://doi. org/10.1016/j.ijmachtools.2021.103767
Denkana, B., Bergmann, B., & Reimer, S. (2020). Analysis of different machine learning algorithms to learn stability lobe diagram.Procedia CIRP, 88. https://doi.org/10.1016/j. procir.2020.05.049
Friedrich, J., Hinze C., Renner A., Verl A., &Lechler A. (2017). Estimation of stability lobe diagrams in milling with continuous learning algorithms. Robotics and Computer- Integrated Manufacturing, 43, 124-134. h t t p s : / / d o i . o r g / 1 0 . 1 0 1 6 / j .r c i m . 2015.10.003
Friedrich, J., Torzewski, J., &Verl, A. (2018). Online learning of stability lobe diagrams in milling. Procedia CIRP. https://doi.org/10.1016/j. procir.2017.12.213
Karandikar, J., Honeycutt, A., Schmitz, T., & Smith, S. (2020). Stability boundary and optimal operating parameter identification in milling using Bayesian learning.Journal of Manufacturing Processes 56.https://doi. org/10.1016/j.jmapro.2020.04.019
Sahu, G. N., & Law, M., (2022). Hardware-in-the-loop simulator for emulation and active control of chatter.HardwareX.https://doi. org/10.1016/j.ohx.2022.e00273
Sahu, G. N., Vashisht, S., Wahi, P., & Law, M. (2020). Validation of a hardware-in-the-loop simulator for investigating and actively damping regenerative chatter in orthogonal cutting.CIRP Journal of Manufacturing Science and Technology, 29, 115-129. https://doi. org/10.1016/j.cirpj.2020.03.002
Schmitz T., Cornelius A., Karandikar J., Tyler C. & Smith S. (2022). Receptance coupling substructure analysis and chatter frequency-informed machine learning for milling stability. CIRP Annals, 71(1), 321-324. https://doi. org/10.1016/j.cirp.2022.03.020
Downloads
Published
How to Cite
Issue
Section
License
All the articles published in Manufacturing Technology Today (MTT) Journal are held by the Publisher. Central Manufacturing Technology Institute (CMTI) as a publisher requires its authors to transfer the copyright prior to publication. This will permit CMTI to reproduce, publish, distribute, and archive the article in print and electronic form and also to defend against any improper use of the article.
