Pre-evaluation on surface profile in turning process based on cutting parameters

Traditional online or in-process surface profile (quality) evaluation (prediction) needs to integrate cutting parameters and several in-process factors (vibration, machine dynamics, tool wear, etc.) for high accuracy. However, it might result in high measuring cost and complexity, and moreover, the surface profile (quality) evaluation result can only be obtained after machining process. In this paper, an approach for surface profile pre-evaluation (prediction) in turning process using cutting parameters and radial basis function (RBF) neural networks is presented. The aim was to only use three cutting parameters to predict surface profile before machining process for a fast pre-evaluation on surface quality under different cutting parameters. The input parameters of RBF networks are cutting speed, depth of cut, and feed rate. The output parameters are FFT vector of surface profile as prediction (pre-evaluation) result. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. It was found that a very good performance of surface profile prediction, in terms of agreement with experimental data, can be achieved before machining process with high accuracy, low cost, and high speed. Furthermore, a new group of training and testing data was also used to analyze the influence of tool wear on prediction accuracy.

[1]  Fushuan Wen,et al.  On-line fault section estimation in power systems with radial basis function neural network , 2002 .

[2]  D. Bajić,et al.  Regression analysis, support vector machines, and Bayesian neural network approaches to modeling surface roughness in face milling , 2009 .

[3]  Xiaowen Wang,et al.  Development of Empirical Models for Surface Roughness Prediction in Finish Turning , 2002 .

[4]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[5]  Robert J. Schalkoff,et al.  Artificial neural networks , 1997 .

[6]  Liu Gui,et al.  On-line Measuring of Ground Surface Roughness Based on Friction-induced Acoustic Emission , 2003 .

[7]  Ossama B. Abouelatta,et al.  Surface roughness prediction based on cutting parameters and tool vibrations in turning operations , 2001 .

[8]  Ashraf A. Kassim,et al.  Application of image and sound analysis techniques to monitor the condition of cutting tools , 2000, Pattern Recognit. Lett..

[9]  W.-C. Liu,et al.  A surface topography model for automated surface finishing , 1998 .

[10]  P. V. Rao,et al.  A surface roughness prediction model for hard turning process , 2007 .

[11]  Anselmo Eduardo Diniz,et al.  Correlating tool life, tool wear and surface roughness by monitoring acoustic emission in finish turning , 1992 .

[12]  Du-Ming Tsai,et al.  A vision system for surface roughness assessment using neural networks , 1998 .

[13]  D. R. Salgado,et al.  In-process surface roughness prediction system using cutting vibrations in turning , 2009 .

[14]  George-Christopher Vosniakos,et al.  Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments , 2002 .

[15]  D. Osypiw,et al.  Surface quality monitoring for process control by on-line vibration analysis using an adaptive spline wavelet algorithm , 2003 .

[16]  Dong Young Jang,et al.  Study of the correlation between surface roughness and cutting vibrations to develop an on-line roughness measuring technique in hard turning , 1996 .

[17]  Uday S. Dixit,et al.  A knowledge-based system for the prediction of surface roughness in turning process , 2006 .

[18]  Shi-Jer Lou,et al.  In-Process Surface Roughness Recognition (ISRR) System in End-Milling Operations , 1999 .

[19]  Uday S. Dixit,et al.  A neural-network-based methodology for the prediction of surface roughness in a turning process , 2005 .

[20]  Wisley Falco Sales,et al.  Modelling the correlation between cutting and process parameters in high-speed machining of Inconel 718 alloy using an artificial neural network , 2005 .

[21]  Kornel Ehmann,et al.  A Generalized Model of the Surface Generation Process in Metal Cutting , 1994 .

[22]  D. T. Ndumu,et al.  Neural network applications in surface topography , 1998 .

[23]  Shih-Chieh Lin,et al.  A study on the effects of vibrations on the surface finish using a surface topography simulation model for turning , 1998 .

[24]  Ching-Kao Chang,et al.  Study on the prediction model of surface roughness for side milling operations , 2006 .

[25]  Y. S. Tarng,et al.  Surface roughness inspection by computer vision in turning operations , 2001 .

[26]  W. Grzesik.  A revised model for predicting surface roughness in turning , 1996 .

[27]  T. D. Faust.  Real time measurement of veneer surface roughness by image analysis , 1987 .

[28]  George-Christopher Vosniakos,et al.  Predicting surface roughness in machining: a review , 2003 .

[29]  Uday S. Dixit,et al.  Prediction of surface roughness and dimensional deviation by measuring cutting forces and vibrations in turning process , 2003 .