Analysis and compensation of workpiece errors in turning

A new method for workpiece error analysis and compensation in turning is introduced. It is known that the workpiece error consists of two parts: machine tool error (including the geometric error and thermal-induced error) and cutting-induced error. The geometric error of the machine tool is independent on machining operation and, hence, can be measured off-line using a fine-touch sensor with a Q-setter (FTS-Q) (also called quick touch setter). The thermal error of the machine tool is dependent on cutting speed, feed, machining time and environmental temperature. It can be estimated using a radius basis function (RBF) artificial neural network (ANN). The cutting-induced error plays a dominant role and can be estimated based on the cutting condition (speed, feed and depth of cut) and the motor currents (main spindle motor current and feed spindle motor current) using a two-stage RBF ANN. Based on the estimated error, the compensation can be done by overwriting the CNC code on-line. Experimental results indicate that the new method can reduce the workpiece error by as much as 75% (average workpiece error is reduced to 8 µm from 14 µm). The new method is also easy to implement in the shop floor.

[1]  P. K. Venuvinod,et al.  Feed Cutting Force Estimation from the Current Measurement with Hybrid Learning , 2000 .

[2]  Shang-Liang Chen,et al.  Orthogonal least squares learning algorithm for radial basis function networks , 1991, IEEE Trans. Neural Networks.

[3]  Stephen C.-Y. Lu,et al.  Simulation of Intermittent Turning Processes , 1991 .

[4]  John W. Sutherland,et al.  Dynamic model of the cutting force system in the turning process , 1990 .

[5]  J. Guckenheimer.  Dynamic model , 1989, Nature.

[6]  James B. Bryan,et al.  International Status of Thermal Error Research (1990) , 1990 .

[7]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[8]  Angel Alique,et al.  Toward intelligent machining: hierarchical fuzzy control for the end milling process , 1998, IEEE Trans. Control. Syst. Technol..

[9]  J. L. Stein,et al.  Evaluation of DC Servo Machine Tool Feed Drives as Force Sensors , 1986 .

[10]  T Poggio,et al.  Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks , 1990, Science.

[11]  Patri K. Venuvinod.  A NEW ELECTRO-MAGNETIC CONTACT SENSING TECHNIQUE FOR ENHANCING MACHINING ACCURACY , 1997 .

[12]  P. K. Venuvinod,et al.  Error Compensation in CNC Turning Solely from Dimensional Measurements of Previously Machined Parts , 1999 .

[13]  Jun Ni,et al.  Real-time cutting force induced error compensation on a turning center , 1999 .

[14]  M. Kubat,et al.  Decision trees can initialize radial-basis function networks , 1998, IEEE Trans. Neural Networks.

[15]  Tae-Yong Kim,et al.  Adaptive cutting force control for a machining center by using indirect cutting force measurements , 1996 .

[16]  Masafumi Sakamoto,et al.  Precision Turning by Means of a Simplified Predictive Function of Machining Error , 1992 .

[17]  S. M. W. Manufacturin.  The real-time error compensation technique for CNC machining systems , 2002 .

[18]  A. Galip Ulsoy,et al.  Error Source Diagnostics Using a Turning Process Simulator , 1998 .

[19]  Jongwon Kim,et al.  Real-Time Tool Breakage Monitoring for NC Milling Process , 1995 .

[20]  David S. Broomhead,et al.  Multivariable Functional Interpolation and Adaptive Networks , 1988, Complex Syst..

[21]  Mohamed A. Elbestawi,et al.  A Strategy for the Compensation of Errors in Five-Axis Machining , 1995 .

[22]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[23]  Xiaoli Li.  Real-Time Prediction of Workpiece Errors for a CNC Turning Centre, Part 2. Modelling and Estimation of Thermally Induced Errors , 2001 .

[24]  L. Kops,et al.  Improved analysis of the workpiece accuracy in turning, based on the emerging diameter , 1993 .