贾振元

个人信息Personal Information

教授

博士生导师

硕士生导师

主要任职:校长、党委副书记

性别:男

毕业院校:大连理工大学

学位:博士

所在单位:机械工程学院

电子邮箱:jzyxy@dlut.edu.cn

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A three-dimensional triangular vision-based contouring error detection system and method for machine tools

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论文类型:期刊论文

发表时间:2017-10-01

发表刊物:PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY

收录刊物:Scopus、SCIE、EI

卷号:50

页面范围:85-98

ISSN号:0141-6359

关键字:Machine tools; Contouring error; Machine tool accuracy; Triangular vision; Image analysis; 3D measurement

摘要:Contouring error detection for machine tools can be used to effectively evaluate their dynamic performances. A triangular vision-based contouring error detection system and method is proposed in this paper, realizing the three-dimensional error measurement of an arbitrary trajectory in conditions of a high feed rate and wide motion range. First, a high-precision measurement fixture, which consists of high-precision circular coded markers and a highly uniform light source, is designed to accurately characterize the motion trajectory of a machine tool and realize the high-quality collection of an image sequence. Then, to improve the contouring error detection accuracy, a coded marker decoding and center location method for the automatic recognition and high-precision center positioning of the circular coded markers are applied. Using image preprocessing and matching, the markers' three-dimensional coordinates in the camera coordinate system can be constructed. Moreover a data transformation method induced by the orthogonal motion of machine tools is proposed to obtain the three-dimensional trajectory in the machine tool coordinate frame and the contouring error can be calculated. Finally, a three-dimensional contouring error detection study of an equiangular spiral interpolation at different feed rates is performed in the laboratory. It is shown that the average contouring error for a feed rate of 1000 min/min is about 3 mu m, which verifies the vision measurement accuracy and feasibility. (C) 2017 Elsevier Inc. All rights reserved.