Prediction of tool chatter in turning using RSM and ANN

Abstract This paper presents a new technique to explore the mechanism of tool chatter in turning process using statistical approach along with signal pre-processing technique. In this paper, experiments have been conducted considering depth of cut, feed rate and spindle speed to acquire chatter signals. Further, wavelet transform (WT) has been used to process the acquire signals and a new parameter called chatter index (CI) has been calculate to quantify the chatter severity. Moreover, response surface methodology (RSM) and feed forward back propagation based artificial neural network (ANN) have been used to develop mathematical model for CI. Analysis of variance (ANOVA) and regression analysis has been done to check the adequacy of RSM and ANN model respectively

[1]  B. Singh,et al.  Slip Damping Mechanism in Welded Structures Using Response Surface Methodology , 2012 .

[2]  Yung C. Shin,et al.  A comprehensive chatter prediction model for face turning operation including tool wear effect , 2002 .

[3]  Laixi Zhang,et al.  Analysis of dynamic stability in a turning process based on a 2-DOFs model with overlap factor , 2012 .

[4]  H. E. Merritt.  Theory of Self-Excited Machine-Tool Chatter: Contribution to Machine-Tool Chatter Research—1 , 1965 .

[5]  L. Debnath.  Wavelets and Signal Processing , 2012 .

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

[7]  İlhan Asiltürk,et al.  Multi response optimisation of CNC turning parameters via Taguchi method-based response surface analysis , 2012 .

[8]  N. K. Chandiramani,et al.  Dynamics of 2-dof regenerative chatter during turning , 2006 .

[9]  Neil D. Sims,et al.  Chatter, process damping, and chip segmentation in turning: A signal processing approach , 2010 .

[10]  Zichen Chen,et al.  On-line chatter detection and identification based on wavelet and support vector machine , 2010 .

[11]  Ming Liang,et al.  Chatter detection based on probability distribution of wavelet modulus maxima , 2009 .

[12]  Achala V. Dassanayake,et al.  On nonlinear cutting response and tool chatter in turning operation , 2008 .

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

[14]  Ibrahim N. Tansel,et al.  Transformations in machining. Part 2. Evaluation of machining quality and detection of chatter in turning by using s-transformation , 2006 .

[15]  Amit Kumar Gupta,et al.  Predictive modelling of turning operations using response surface methodology, artificial neural networks and support vector regression , 2010 .

[16]  Tony L. Schmitz,et al.  An investigation of the dynamic absorber effect in high-speed machining , 2005 .

[17]  Eyup Bagci,et al.  Investigation of surface roughness in turning unidirectional GFRP composites by using RS methodology and ANN , 2006 .

[18]  J. Paulo Davim,et al.  Investigations into the effect of cutting conditions on surface roughness in turning of free machining steel by ANN models , 2008 .