@article{(Open Science Index):https://publications.waset.org/pdf/10012516,
	  title     = {Networked Implementation of Milling Stability Optimization with Bayesian Learning},
	  author    = {C. Ramsauer and  J. Karandikar and  D. Leitner and  T. Schmitz and  F. Bleicher},
	  country	= {},
	  institution	= {},
	  abstract     = {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.},
	    journal   = {International Journal of Industrial and Systems Engineering},
	  volume    = {16},
	  number    = {4},
	  year      = {2022},
	  pages     = {103 - 106},
	  ee        = {https://publications.waset.org/pdf/10012516},
	  url   	= {https://publications.waset.org/vol/184},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 184, 2022},
	}