A review of machining monitoring systems based on artificial intelligence process models

Abstract

Many machining monitoring systems based on artificial intelligence (AI) process models have been successfully developed in the past for optimising, predicting or controlling machining processes. In general, these monitoring systems present important differences among them, and there are no clear guidelines for their implementation. In order to present a generic view of machining monitoring systems and facilitate their implementation, this paper reviews six key issues involved in the development of intelligent machining systems: (1) the different sensor systems applied to monitor machining processes, (2) the most effective signal processing techniques, (3) the most frequent sensory features applied in modelling machining processes, (4) the sensory feature selection and extraction methods for using relevant sensory information, (5) the design of experiments required to model a machining operation with the minimum amount of experimental data and (6) the main characteristics of several artificial intelligence techniques to facilitate their application/selection.