Integrated geometric error modeling, identification and compensation of CNC machine tools

Zhu, Shaowei; Ding, Guofu; Qin, Shengfeng; Jiang, Lei; Li, Zhuang; Yan, Kaiyin

doi:10.1016/j.ijmachtools.2011.08.011

Cited by 312 publications

(135 citation statements)

References 18 publications

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“…Furthermore, the various methods for modeling the geometric errors from different perspectives have experienced a gradual development [7]. To describe the error of the cutter location and the tool orientation between the two kinematic chains, the error model is normally established using homogeneous transformation sciENcE aNd tEchNology matrices (HTM) [10,18,20], denavit-hartenberg (D-H) method [16], modified denavit-hartenberg (MD-H) method [19], or multi-body system (MBS) theory [31,32]. Among these different approaches, MBS theory, first proposed by Houston, has evolved into the best method for the modeling of geometric errors of machine tools because it provides for a simple and convenient method to describe the topological structure of an MBS [21].…”

Section: Volumetric Error Modelmentioning

confidence: 99%

A method to analyze the machining accuracy reliability sensitivity of machine tools based on Fast Markov Chain simulation

Cheng¹,

Sun²,

Zhao³

et al. 2016

EiN

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Section: Volumetric Error Modelmentioning

confidence: 99%

A method to analyze the machining accuracy reliability sensitivity of machine tools based on Fast Markov Chain simulation

Cheng¹,

Sun²,

Zhao³

et al. 2016

EiN

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“…• Error due to the placement of locators [5][6][7][8] • Geometric/form defects of the workpiece [9][10][11][12] • Errors due to deformation of locators [13][14][15][16][17][18] • Kinematic defects/ machine tool errors [19][20][21][22][23][24][25][26] • Misc. errors due to tool wear, heat, NC codes, etc… Fig.…”

Section: Positioning Errorsmentioning

confidence: 99%

A Kinematic Approach for 6-DOF Part Positioning

Butt

Antoine²,

Martin

2013

Lecture Notes in Production Engineering

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. Abstract. This article proposes a fixturing system consists of a cuboid baseplate located through a 3-2-1 configuration of locators. The locators are mounted on machine table/pallet and posses one axial DOF. The workpiece is mounted on the baseplate and all the elements are assumed to be rigid with zero friction. The positioning error of the workpiece is calculated and the compensation is performed by the axial movement of the locators. The proposed analytical model is verified by the simulation performed in the CAD model.

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“…Interesting works have also been performed in the machine-tools community where the positioning stages used are mechanical guiding based on the friction principle and ball screw mechanisms [22][23][24][25][26][27]. It was also established that temperature is a key parameter to guarantee a high positioning accuracy.…”

Section: Introductionmentioning

confidence: 99%

Calibration of Nanopositioning Stages

2015

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Accuracy is one of the most important criteria for the performance evaluation of microand nanorobots or systems. Nanopositioning stages are used to achieve the high positioning resolution and accuracy for a wide and growing scope of applications. However, their positioning accuracy and repeatability are not well known and difficult to guarantee, which induces many drawbacks for many applications. For example, in the mechanical characterisation of biological samples, it is difficult to perform several cycles in a repeatable way so as not to induce negative influences on the study. It also prevents one from controlling accurately a tool with respect to a sample without adding additional sensors for closed loop control. This paper aims at quantifying the positioning repeatability and accuracy based on the ISO 9283:1998 standard, and analyzing factors influencing positioning accuracy onto a case study of 1-DoF (Degree-of-Freedom) nanopositioning stage. The influence of thermal drift is notably quantified. Performances improvement of the nanopositioning stage are then investigated through robot calibration (i.e., open-loop approach). Two models (static and adaptive models) are proposed to compensate for both geometric errors and thermal drift. Validation experiments are conducted over a long period (several days) showing that the accuracy of the stage is improved from typical micrometer range to 400 nm using the static model and even down to 100 nm using the adaptive model. In addition, we extend the 1-DoF calibration to multi-DoF with a case study of a 2-DoF nanopositioning robot. Results demonstrate that the model efficiently improved the 2D accuracy from 1400 nm to 200 nm.

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Integrated geometric error modeling, identification and compensation of CNC machine tools

Cited by 312 publications

References 18 publications

A method to analyze the machining accuracy reliability sensitivity of machine tools based on Fast Markov Chain simulation

A method to analyze the machining accuracy reliability sensitivity of machine tools based on Fast Markov Chain simulation

A Kinematic Approach for 6-DOF Part Positioning

Calibration of Nanopositioning Stages

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