Development of Chatter Threshold Determination Methodology in Milling Process by Using Inclined Workpiece

Machined product quality depends on its dimension and surface quality. The dimension quality depends on machine tool accuracy while the surface quality depends on machining system stiffness. A low machining system stiffness will shift spindle shaft-tool resonance frequencies to low frequencies. When one of these frequencies coincides with spindle rotational speed or its harmonics, chatter will be generated which in turn worsen the workpiece surface roughness. In addition to increasing machining system stiffness, chatter can be eliminated by decreasing the axial depth of cut as well. Maximum axial depth at certain spindle rotational speed which will not generate chatter is called as chatter threshold. A diagram describing chatter thresholds for certain range of spindle rotational speed is called as a SLD (stability lobe diagram). The diagram is very useful for selecting a maximum depth of cut at certain rotational speed in order to obtain chatter-free machining process. The SLD can be generated theoretically or experimentally. The theoretical one is fast and cheap but it is not guaranteed to be correct. On the other hand, although the experimental one will produce exact values but it is long, cumbersome and expensive, because for certain rotational speed many machining with different axial depth of cut must be conducted until chatter threshold is reached. The same process is then repeated for other rotational speeds. This paper deals with a new method in determining the chatter threshold or SLD experimentally, by using inclined workpiece, by which it only needs one time machining-test for each rotational speed. In this method, during machining process, chatter occurrence is detected by using accelerometer and validated by its surface roughness afterwards. It is shown in this paper that the new method works well for machining aluminium workpiece in vertical machining center.

[1]  Yaguo Lei,et al.  Chatter identification in end milling process using wavelet packets and Hilbert–Huang transform , 2013 .

[2]  Simon S. Park,et al.  Robust chatter stability in micro-milling operations , 2010 .

[3]  Yusuf Altintas,et al.  Analytical Prediction of Stability Lobes in Milling , 1995 .

[4]  S. A. Tobias,et al.  A Theory of Nonlinear Regenerative Chatter , 1974 .

[5]  Manfred Weck,et al.  Chatter Stability of Metal Cutting and Grinding , 2004 .

[6]  Jean-François Debongnie,et al.  A stability diagram computation method for milling adapted to automotive industry , 2006 .

[7]  Elso Kuljanić,et al.  Development of an intelligent multisensor chatter detection system in milling , 2009 .

[8]  Brian P. Mann,et al.  Uncharted islands of chatter instability in milling , 2008 .

[9]  Joaquim Ciurana,et al.  Sound mapping for identification of stability lobe diagrams in milling processes , 2009 .

[10]  Jose Alvarez-Ramirez,et al.  Characterization of machining chattering dynamics: An R/S scaling analysis approach , 2009 .

[11]  M. Siddhpura,et al.  A review of chatter vibration research in turning , 2012 .

[12]  Zhengjia He,et al.  Chatter stability of milling with speed-varying dynamics of spindles , 2012 .

[13]  Guillem Quintana,et al.  Chatter in machining processes: A review , 2011 .

[14]  Philip K. Chan,et al.  In-process detection and suppression of chatter in milling , 1992 .

[15]  P PalPandian,et al.  Identification of stability lobes in high-speed machining of thin ribs , 2010 .

[16]  Wei Xiao Tang,et al.  Prediction of chatter stability in high-speed finishing end milling considering multi-mode dynamics , 2009 .

[17]  Simon S. Park,et al.  Robust prediction of chatter stability in milling based on the analytical chatter stability , 2013 .

[18]  Eckart Uhlmann,et al.  Chatter frequencies of micromilling processes: Influencing factors and online detection via piezoactuators , 2012 .

[19]  Elso Kuljanić,et al.  Multisensor approaches for chatter detection in milling , 2008 .

[20]  Simon S. Park,et al.  Chatter suppression in micro end milling with process damping , 2009 .

[21]  Ruxu Du,et al.  Chatter detection in milling based on the probability distribution of cutting force signal , 1992 .