Error Measurement, Analysis, and Compensation Technology for CNC Machine Tools

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 1103

Special Issue Editors


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Guest Editor
Department of Electromechanical Measuring and Controlling, School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Interests: precision enhancement of multi-axis machine tools and precision machining, including thermal error modeling and correction, cutting error control based on signal processing and intelligent control; CNC tool path generation and optimization

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Co-Guest Editor
1. The State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
2. Key Lab of 3D Printing Process and Equipment of Zhejiang Province, Zhejiang University, Hangzhou 310027, China
Interests: intelligent manufacturing technology; intelligent CAD/CAM/CNC; 3D printing technology and equipment

Special Issue Information

Dear Colleagues,

CNC machine tools represent the core competitiveness of a country’s manufacturing industry. They play an important role in national defense, aerospace, and automobile manufacturing. With the development of intelligent manufacturing, the demands regarding the accuracy of CNC machine tools have been increasing. Many factors influence the machining accuracy of CNC machine tools, including geometric errors, thermal errors, cutting force deformation errors, servo tracking errors, and so on. Error measurement, analysis, and compensation is one of the important ways to enhance the accuracy of the CNC machine tools.

The objective of this Special Issue is to discover the most recent and significant developments in error measurement, error analysis, error modeling, and compensation for CNC machine tools. This Special Issue encourages and welcomes original research articles with a significant contribution to numerical, theoretical, and experimental analysis. Review articles related to these application areas are also invited.

Potential topics include, but are not limited to:

Geometric errors of machine tools;
Thermal errors of machine tools;
Error measurement and identification;
Error modeling;
Error compensation;
Data acquisition and measurement methods;
Machine learning and intelligent error modeling.

Dr. Guoqiang Fu
Prof. Dr. Jianzhong Fu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • CNC machine tools
  • error measurement and identification
  • error modeling
  • error compensation

Published Papers (1 paper)

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Research

17 pages, 2697 KiB  
Article
Kinematic Parameter Identification and Error Compensation of Industrial Robots Based on Unscented Kalman Filter with Adaptive Process Noise Covariance
by Guanbin Gao, Xinyang Guo, Gengen Li, Yuan Li and Houchen Zhou
Machines 2024, 12(6), 406; https://doi.org/10.3390/machines12060406 - 12 Jun 2024
Viewed by 455
Abstract
Kinematic calibration plays a pivotal role in enhancing the absolute positioning accuracy of industrial robots, with parameter identification and error compensation constituting its core components. While the conventional parameter identification method, based on linearization, has shown promise, it suffers from the loss of [...] Read more.
Kinematic calibration plays a pivotal role in enhancing the absolute positioning accuracy of industrial robots, with parameter identification and error compensation constituting its core components. While the conventional parameter identification method, based on linearization, has shown promise, it suffers from the loss of high-order system information. To address this issue, we propose an unscented Kalman filter (UKF) with adaptive process noise covariance for robot kinematic parameter identification. The kinematic model of a typical 6-degree-of-freedom industrial robot is established. The UKF is introduced to identify the unknown constant parameters within this model. To mitigate the reliance of the UKF on the process noise covariance, an adaptive process noise covariance strategy is proposed to adjust and correct this covariance. The effectiveness of the proposed algorithm is then demonstrated through identification and error compensation experiments for the industrial robot. Results indicate its superior stability and accuracy across various initial conditions. Compared to the conventional UKF algorithm, the proposed approach enhances the robot’s accuracy stability by 25% under differing initial conditions. Moreover, compared to alternative methods such as the extended Kalman algorithm, particle swarm optimization algorithm, and grey wolf algorithm, the proposed approach yields average improvements of 4.13%, 26.47%, and 41.59%, respectively. Full article
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