Empirical analysis of the efficient use of geometric error identification in a machine tool by tracking measurement techniques. (25th January 2016)

• Record Type: Journal Article
• Authors: Aguado, S; Santolaria, J; Samper, D; Velazquez, J; Aguilar, J J
• Is Part Of: Measurement science & technology. Volume 27:Number 3(2016:Mar.)
• Publication Date: 2016-01-25
• Subjects: volumetric verification -- laser tracker -- active target -- number of points -- regression functions; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/0957-0233/27/3/035002
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: Volumetric verification is becoming increasingly accepted as a suitable technique with which to improve machine tool accuracy. In the same way, the use of laser trackers to obtain machine error information using the new Active Target motorised retro-reflector allows the verification of all types of machine tool throughout their workspaces. Non-linear optimisation methods and machine tool kinematic models are the mainstays of this technique. Whereas the latter provide the relationship between the nominal coordinates, the geometric errors of the machine and laser tracker measurement, the former reduces the combined influence of geometric errors by obtaining their approximation functions. However, within these two procedures, several factors affect the scope of the produced verification results. The present paper focuses on the analysis of the adequacy of commercial measurement techniques using laser trackers and the new motorised retro-reflector in a real milling machine. An examination is also made regarding the influence of the optimisation sequence defined by the identification strategy, as well as the impact of the number of measured points in relation to the employed regression functions.