Identification of Manufacturing Processes Signature by a Principal Component Based Approach

Machined surfaces and profiles often present a systematic pattern, usually referred to as “the signature” of the process. Advantages related with identification of process’ signature have been clearly outlined in the literature. The proposed approaches are mainly based on parametric models, in which the signature is described as a combination of analytical functions (predictors) that have to be properly chosen by the analyst depending on the specific case faced. Analytical tools, which do not use parametric model to describe profiles, were also presented in the so-called “profile monitoring” research field. In particular, the Principal Component Analysis (PCA), which is a statistical technique utilized to identify patterns in multivariate data, was successfully applied in chemometrics. In this paper, the use of PCA is investigated for process’ signature modelling in the case of machined profiles. The goal is to describe a general-purpose approach, which alleviates the analyst from the need to identify a suitable kind of analytical functions for the statistical description of machined profiles. The illustration of the PCA method is based on real measurements data of circular items obtained by turning