Abstract
Abstract This study discusses frequency analysis based on frequency spectrum and autoregressive (AR) time series model, and process characterization in orthogonal cutting of fiber–matrix composite materials. A sparsely distributed idealized composite material, namely a glass fiber reinforced polyester (GFRP) was used as the workpiece. The analysis method employs a force sensor and the signals from the sensor are processed using either the fast Fourier transform (FFT) technique or AR time series model. Signal distortion measure based on discrimination information is also introduced. The experimental correlations between the different chip formation mechanisms and power spectrum or AR model coefficients are then established. In particular, only those features that are most sensitive to different types of cutting mechanisms are selected by feature extraction method in AR modeling. Selected features are used to characterize the chip formation. Discrimination information measure proves to be useful in signal analysis when any characteristic of the cutting process is apparent in the form of spectral peaks. Effects of fiber orientation, cutting parameters and tool geometry on the cutting mechanisms and surface quality are also discussed.
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