Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32231
Networked Implementation of Milling Stability Optimization with Bayesian Learning

Authors: C. Ramsauer, J. Karandikar, D. Leitner, T. Schmitz, F. Bleicher


Machining instability, or chatter, can impose an important limitation to discrete part machining. In this work, a networked implementation of milling stability optimization with Bayesian learning is presented. The milling process was monitored with a wireless sensory tool holder instrumented with an accelerometer at the TU Wien, Vienna, Austria. The recorded data from a milling test cut were used to classify the cut as stable or unstable based on a frequency analysis. The test cut result was used in a Bayesian stability learning algorithm at the University of Tennessee, Knoxville, Tennessee, USA. The algorithm calculated the probability of stability as a function of axial depth of cut and spindle speed based on the test result and recommended parameters for the next test cut. The iterative process between two transatlantic locations was repeated until convergence to a stable optimal process parameter set was achieved.

Keywords: Bayesian learning, instrumented tool holder, machining stability, optimization strategy.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 313


[1] Y. Altintaş and E. Budak, “Analytical Prediction of Stability Lobes in Milling,” CIRP Annals, vol. 44, no. 1, pp. 357–362, 1995
[2] Y. Altintas and M. Weck, “Chatter Stability of Metal Cutting and Grinding,” CIRP Annals, vol. 53, no. 2, pp. 619–642, 2004
[3] E. Budak and A. Tekeli, “Maximizing Chatter Free Material Removal Rate in Milling through Optimal Selection of Axial and Radial Depth of Cut Pairs,” CIRP Annals, vol. 54, no. 1, pp. 353–356, 2005
[4] T. L. Schmitz and K. S. Smith, Machining Dynamics: Frequency Response to Improved Productivity, 2nd ed. Cham: Springer International Publishing, 2019
[5] G. Quintana and J. Ciurana, “Chatter in machining processes: A review,” International Journal of Machine Tools and Manufacture, vol. 51, no. 5, pp. 363–37, 2011
[6] C. Brecher, C. Kiesewetter, A. Epple, and M. Fey, “Automatisierte Erstellung von Stabilitätskarten für Fräsbearbeitungen,” Zeitschrift für wirtschaftlichen Fabrikbetrieb, vol. 110, no. 4, pp. 191–195, 2015
[7] Z. Li, Z. Wang, and X. Shi, “Fast prediction of chatter stability lobe diagram for milling process using frequency response function or modal parameters,” The International Journal of Advanced Manufacturing Technology, vol. 89, 9-12, pp. 2603–2612, 2017
[8] R. Teti, K. Jemielniak, G. O’Donnell, and D. Dornfeld, “Advanced monitoring of machining operations,” CIRP Annals, vol. 59, no. 2, pp. 717–739, 2010
[9] F. Bleicher et al., “Tooling systems with integrated sensors enabling data based process optimization,” Journal of Machine Engineering, vol. 21, no. 1, pp. 5–21, 2021
[10] F. Bleicher, C. Ramsauer, R. Oswald, N. Leder, and P. Schoerghofer, “Method for determining edge chipping in milling based on tool holder vibration measurements,” CIRP Annals, vol. 69, no. 1, pp. 101–104, 2020
[11] C. Ramsauer and F. Bleicher, “New method for determining single cutting edge breakage of a multi-tooth milling tool based on acceleration measurements by an instrumented tool holder,” Journal of Machine Engineering, vol. 21, no. 1, pp. 67–77, 2021
[12] J. Karandikar, A. Honeycutt, T. Schmitz, and S. Smith, “Stability boundary and optimal operating parameter identification in milling using Bayesian learning,” Journal of Manufacturing Processes, vol. 56, pp. 1252–1262, 2020