Detection of machine tool contouring errors using wavelet transforms and neural networks

The accuracy and precision of computer numerical control (CNC) machine tools directly affect the dimensional accuracy of machined parts. Fast detection of machine tool contouring errors is required to guarantee the accuracy of the manufacturing process and, further, to eliminate errors through error compensation techniques. In this paper, several typical contouring error patterns of CNC machine tools (i.e., cyclic, backlash, scale mismatch, etc.) are presented. Detection of machine tool contouring errors is conducted in two steps using wavelet transforms (WT) and neural networks (NN). In the first step, wavelet transform is applied to contouring error signals to extract error features. In the second step, wavelet coefficients are grouped into proper input units for neural networks; that is, data were compressed by omitting unnecessary details. In this study, cascade-correlation (CC) neural networks are selected to recognize the seven basic patterns of CNC contouring errors. Multiple contouring errors can also be identified quantitatively in the WT-NN approach.