Configuration optimization of laser tracker stations for position measurement in error identification of heavy-duty machine tools. (14th March 2019)

• Record Type: Journal Article
• Authors: Wang, Han; Shao, Zhongxi; Fan, Zhaoyan; Han, Zhenyu
• Is Part Of: Measurement science & technology. Volume 30:Number 4(2019:Apr.)
• Publication Date: 2019-03-14
• Subjects: configuration optimization -- laser tracker -- position measurement -- heavy-duty machine tools; 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: 10.1088/1361-6501/ab048b
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: Location configurations greatly affect the accuracy of sequential multilateration measurements using a laser tracker. This paper presents an approach that optimizes the locations of a laser tracker to improve the accuracy of position measurement in geometric error identification for heavy-duty machine tools. The approach is based on a physical model of the whole working space with the considered constraints determined by the structures of heavy-duty machine tools. The optimal location configuration of the laser tracker is searched by minimizing the average error magnification factor for all the measurement points on the machine tool movement trajectory. The Monte Carlo method is then used to evaluate the final measurement uncertainty of the sequential multilateration measurement under different configurations considering the influence of the repeatability error. The experimental validations are conducted on a coordinate measuring machine and a realistic floor-type milling machine with a 5 m × 2 m × 1 m working space. The experimental results show that position measurement of the optimized configuration has higher accuracy with an improvement of 57.5% compared with that of another feasible configuration analyzed in the uncertainty evaluation. The validity of the location optimization approach is further verified for identifying geometric error with high accuracy using an error identification experiment based on the nine-line method.