Numerical robust stability estimation in milling process

The conventional prediction of milling stability has been extensively studied based on the assumptions that the milling process dynamics is time invariant. However, nominal cutting parameters cannot guarantee the stability of milling process at the shop floor level since there exists many uncertain factors in a practical manufacturing environment. This paper proposes a novel numerical method to estimate the upper and lower bounds of Lobe diagram, which is used to predict the milling stability in a robust way by taking into account the uncertain parameters of milling system. Time finite element method, a milling stability theory is adopted as the conventional deterministic model. The uncertain dynamics parameters are dealt with by the non-probabilistic model in which the parameters with uncertainties are assumed to be bounded and there is no need for probabilistic distribution densities functions. By doing so, interval instead of deterministic stability Lobe is obtained, which guarantees the stability of milling process in an uncertain milling environment. In the simulations, the upper and lower bounds of Lobe diagram obtained by the changes of modal parameters of spindle-tool system and cutting coefficients are given, respectively. The simulation results show that the proposed method is effective and can obtain satisfying bounds of Lobe diagrams. The proposed method is helpful for researchers at shop floor to making decision on machining parameters selection.

[1]  Rph Ronald Faassen,et al.  Chatter prediction and control for high-speed milling : modelling and experiments , 2003 .

[2]  G. Alefeld,et al.  Introduction to Interval Computation , 1983 .

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

[4]  Gábor Stépán,et al.  Stability of up-milling and down-milling, part 1: alternative analytical methods , 2003 .

[5]  Ramon E. Moore,et al.  Methods and Applications of Interval Analysis (SIAM Studies in Applied and Numerical Mathematics) (Siam Studies in Applied Mathematics, 2.) , 1979 .

[6]  A. Galip Ulsoy,et al.  Robust Machining Force Control With Process Compensation , 2001, Dynamic Systems and Control.

[7]  G. Totis,et al.  RCPM—A new method for robust chatter prediction in milling , 2009 .

[8]  J. D. Chrostowski,et al.  Effects of product and experimental variability on model verification of automobile structures , 1997 .

[9]  Gábor Stépán,et al.  Updated semi‐discretization method for periodic delay‐differential equations with discrete delay , 2004 .

[10]  Rodolfo E. Haber Guerra,et al.  Using circle criteria for verifying asymptotic stability in PI-like fuzzy control systems: application to the milling process , 2003 .

[11]  Han Ding,et al.  Numerical robust optimization of spindle speed for milling process with uncertainties , 2012 .

[12]  Etienne Balmes Predicted variability and differences between tests of a single structure , 1998 .

[13]  Marian Wiercigroch,et al.  Sources of nonlinearities, chatter generation and suppression in metal cutting , 2001, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[14]  Yusuf Altintas,et al.  An Improved Time Domain Simulation for Dynamic Milling at Small Radial Immersions , 2003 .

[15]  Osita D. I. Nwokah,et al.  A Digital Robust Controller for Cutting Force Control in the End Milling Process , 1997 .