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.
Abstract: This study was aimed to investigate the machining
stability of a spindle tool with different preloaded amount. To this end,
the vibration tests were conducted on the spindle unit with different
preload to assess the dynamic characteristics and machining stability
of the milling machine. Current results demonstrate that the tool tip
frequency response characteristics and the machining stabilities in X
and Y direction are affected to change due to the different preload of
spindle bearings. As found from the results, a high preloaded spindle
tool shows higher limited cutting depth at mid position, while a spindle
with low preload shows a higher limited depth. This indicates that the
machining stability of a milling machine is affected to vary by the
spindle unit when it was assembled with different bearing preload.
Abstract: Chatter vibration has been a troublesome problem
for a machine tool toward the high precision and high speed machining.
Essentially, the machining performance is determined by the dynamic
characteristics of the machine tool structure and dynamics of cutting
process, which can further be identified in terms of the stability lobe
diagram. Therefore, realization on the machine tool dynamic behavior
can help to enhance the cutting stability. To assess the dynamic
characteristics and machining stability of a vertical milling system
under the influence of a linear guide, this study developed a finite
element model integrated the modeling of linear components with the
implementation of contact stiffness at the rolling interface. Both the
finite element simulations and experimental measurements reveal that
the linear guide with different preload greatly affects the vibration
behavior and milling stability of the vertical column spindle head
system, which also clearly indicate that the predictions of the
machining stability agree well with the cutting tests. It is believed that
the proposed model can be successfully applied to evaluate the
dynamics performance of machine tool systems of various
configurations.