Cutting force sensor signals are used for chip form monitoring during longitudinal turning of carbon steel with coated carbide inserts, producing different chip forms. Advanced signal analysis was carried out by spectral valuation through a parametric method and feature extraction from the frequency spectrum. In this methodology, the signal spectrum is assumed to take on a specific functional form, the parameters of which are unknown. The spectral estimation problem becomes one of estimating the unknown parameters of the spectrum model, rather than the spectrum itself. A group of features characteristic of the spectrum model were obtained by linear predictive analysis from the cutting force signal. The analysis of these features was carried out by pairwise plotting in a 2D feature space and neural network processing in a higher dimension feature space in order to identify the chip form through a cutting force sensor monitoring methodology
