Operational stability prediction in milling based on impact tests

Abstract Chatter detection is usually based on the analysis of measured signals captured during cutting processes. These techniques, however, often give ambiguous results close to the stability boundaries, which is a major limitation in industrial applications. In this paper, an experimental chatter detection method is proposed based on the system’s response for perturbations during the machining process, and no system parameter identification is required. The proposed method identifies the dominant characteristic multiplier of the periodic dynamical system that models the milling process. The variation of the modulus of the largest characteristic multiplier can also be monitored, the stability boundary can precisely be extrapolated, while the manufacturing parameters are still kept in the chatter-free region. The method is derived in details, and also verified experimentally in laboratory environment.

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

[2]  Yusuf Altintas,et al.  Analytical Prediction of Chatter Stability in Milling—Part I: General Formulation , 1998 .

[3]  W. Marsden.  I and J , 2012 .

[4]  B. Mann,et al.  Stability of Interrupted Cutting by Temporal Finite Element Analysis , 2003 .

[5]  Zoltan Dombovari,et al.  On the bistable zone of milling processes , 2015, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

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

[7]  Gábor Stépán,et al.  Improved prediction of stability lobes with extended multi frequency solution , 2013 .

[8]  Zoltan Dombovari,et al.  The Effect of Helix Angle Variation on Milling Stability , 2012 .

[9]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[10]  D. Barton.  Control-based continuation: bifurcation and stability analysis for physical experiments , 2015, 1506.04052.

[11]  C. A. Halijak.  Digital filtering and signal processing : D. Childers and A. Durling. 539 pages, diagrams, 6 x 9 in. St. Paul, Minnesota, 1975. Price, $18.95. (approx £9.85). , 1976 .

[12]  Gabor Stepan,et al.  Robust stability analysis of machining operations , 2017 .

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

[14]  P. Holmes,et al.  Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields , 1983, Applied Mathematical Sciences.

[15]  Lawrence N. Virgin,et al.  Measuring the Stability of Periodic Attractors Using Perturbation-Induced Transients: Applications to Two Non-Linear Oscillators , 1994 .

[16]  Yang Fu,et al.  Timely online chatter detection in end milling process , 2016 .

[17]  S. Smith,et al.  Use of Audio Signals for Chatter Detection and Control , 1992 .

[18]  Jokin Munoa,et al.  Identification of cutting force characteristics based on chatter experiments , 2011 .

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

[20]  Gábor Stépán,et al.  Numerical Stability Test of Linear Time-Delay Systems of Neutral Type , 2017 .

[21]  Jon Rigelsford,et al.  Manufacturing Automation: Metal Cutting Mechanics, Machine Tool Vibrations, and CNC Design , 2004 .

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

[23]  Han Ding,et al.  A full-discretization method for prediction of milling stability , 2010 .

[24]  Waleed Fekry Faris,et al.  Image processing for chatter identification in machining processes , 2006 .

[25]  Tony L. Schmitz,et al.  Chatter recognition by a statistical evaluation of the synchronously sampled audio signal , 2003 .

[26]  Neil D. Sims,et al.  Analytical prediction of chatter stability for variable pitch and variable helix milling tools , 2008 .

[27]  Brian P Mann,et al.  An empirical approach for delayed oscillator stability and parametric identification , 2006, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[28]  Habibolla Latifizadeh,et al.  A spectral element approach for the stability of delay systems , 2017 .

[29]  Yunn-Shiuan Liao,et al.  A new on-line spindle speed regulation strategy for chatter control , 1996 .

[30]  Gábor Stépán,et al.  Semi-Discretization for Time-Delay Systems: Stability and Engineering Applications , 2011 .

[31]  Eric A. Butcher,et al.  Analysis of Milling Stability by the Chebyshev Collocation Method: Algorithm and Optimal Stable Immersion Levels , 2009 .

[32]  M. Shiraishi,et al.  Scope of in-process measurement, monitoring and control techniques in machining processes—Part 3: In-process techniques for cutting processes and machine tools , 1989 .

[33]  Zoltan Dombovari,et al.  Stability analysis of milling with irregular pitch tools by the implicit subspace iteration method , 2014 .

[34]  Donald G. Childers,et al.  Digital Filtering and Signal Processing , 1975 .

[35]  Michele Monno,et al.  Efficient evaluation of process stability in milling with Spindle Speed Variation by using the Chebyshev Collocation Method , 2014 .

[36]  S. A. Tobias,et al.  Theory of finite amplitude machine tool instability , 1984 .

[37]  Ye Ding,et al.  Milling stability analysis with simultaneously considering the structural mode coupling effect and regenerative effect , 2012 .

[38]  P. J. Holmes,et al.  Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields , 1983, Applied Mathematical Sciences.

[39]  Tony L. Schmitz,et al.  A New Metric for Automated Stability Identification in Time Domain Milling Simulation , 2016 .

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

[41]  Gábor Stépán,et al.  Experimental Determination of Dominant Multipliers in Milling Process by Means of Homogeneous Coordinate Transformation , 2017 .

[42]  Christian Brecher,et al.  Chatter suppression techniques in metal cutting , 2016 .