Machine vision based condition monitoring and fault diagnosis of machine tools using information from machined surface texture: A review

Abstract

Machine vision based condition monitoring and fault diagnosis of machine tools (MVCMFD-MTs) is a vital technique of condition-based maintenance (CBM) in both metal removal manufacturing and metal additive fabrication. In these domains, many methods utilize information from imaging matrices of machined surfaces to extract sensitive features and obtain potential degradation tendencies. Over recent years, no comprehensive review covers the whole monitoring or diagnostic procedures. To fill this gap, this paper systematically summarizes MVCMFD-MTs, which aims to provide researchers and engineers with a theoretical basis and roadmap to further study or build MVCMFD-MTs using information from the machined surface texture. Firstly, two data acquisition systems and several institutional public datasets are revisited. Secondly, the methodologies are illustrated in two aspects, feature descriptors and diagnostic decision-making. Thirdly, an intuitive illustration on applications is provided from the perspective of surface quality monitoring (i.e., roughness evaluation, surface defect inspection) and indirect tool condition monitoring (i.e., tool wear monitoring, chatter identification). Finally, this paper discusses current challenges and potential research directions in nowadays intelligent manufacturing.