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

The accuracy and precision of computer numerical control (CNC) machine tool s directly affect the dimensional accuracy of machined parts. Fast detectio n of machine tool contouring errors is required to guarantee the accuracy o f the manufacturing process and, further, to eliminate errors through error compensation techniques. In this paper, several typical contouring error p atterns 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 f irst step, wavelet transform is applied to contouring error signals to extr act error features. In the second step, wavelet coefficients are grouped in to proper input units for neural networks; that is, data were compressed by omitting unnecessary details. In this study, cascade-correlation (CC) neur al networks are selected to recognize the seven basic patterns of CNC conto uring errors. Multiple contouring errors can also be identified quantitativ ely in the WT-NN approach.