On the Identification of Manufacturing Processes’ Signature

Manufacturing processes produce a specific machined surface that acts as a fingerprint of the process used. When a model of the process signature is available, it can be used to improve the quality control strategy and to define proper feedback control actions. Models proposed up to now in the literature can be useful to identify simple signatures, where data measured on the profile are not autocorrelated. Unfortunately, most of the times data collected on a machined profile are autocorrelated because they are obtained in similar condition of the machining process and because they are related to local properties of the material machined. In this paper, a novel and efficient method for modeling machined profiles for autocorrelated data is presented. The model is build by using experimental data and is based on the autoregressive moving average with exogenous variables (ARMAX) model. Fourier and wavelet-based approach are both exploited in the ARMAX model to describe signature in the discrete frequency domain.