TY - JFULL AU - C. Ramsauer and J. Karandikar and D. Leitner and T. Schmitz and F. Bleicher PY - 2022/5/ TI - Networked Implementation of Milling Stability Optimization with Bayesian Learning T2 - International Journal of Industrial and Systems Engineering SP - 102 EP - 106 VL - 16 SN - 1307-6892 UR - https://publications.waset.org/pdf/10012516 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 184, 2022 N2 - 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. ER -