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An RBF neural network approach to geometric error compensation with displacement measurements only

  • Engineering Applications of Neural Networks
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Abstract

A novel radial basis function (RBF) neural network-based geometric error compensation method with displacement measurements only is proposed in this paper. The individual geometric error components are formulated mathematically based on laser interferometer calibration with displacement measurements only and modeled using RBF neural network for error compensation in motion controller. Only 4 and 15 displacement measurements are required to identify the error components for XY and XYZ table, respectively. The experiment results on two XY tables illustrate the effectiveness of the proposed method. The overall errors can be reduced significantly after compensation, and different data intervals can be selected to reduce calibration time but maintain a high level of accuracy. The proposed methodology can be extended to other types of precision machine and is more suitable for precision machines requiring a relative low level of accuracy, but fast calibration like those used for acceptance testing and periodic checking.

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Acknowledgments

The authors would like to thank to Research Fund for the Taishan Scholar Project of Shandong Province of China.

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Authors and Affiliations

  1. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, China

    Rui Yang & Zidong Wang

  2. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore

    Kok Kiong Tan & Arthur Tay

  3. Temasek Laboratorie, National University of Singapore, Singapore, 117411, Singapore

    Sunan Huang

  4. Interactive and Digital Media Institute, National University of Singapore, Singapore, 119613, Singapore

    Jie Sun

  5. Department of Mechanical Engineering, National University of Singapore, Singapore, 117575, Singapore

    Jerry Fuh & Yoke San Wong

  6. Singapore Institute of Manufacturing Technology, Singapore, 638075, Singapore

    Chek Sing Teo

  7. Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, UK

    Zidong Wang

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  1. Rui Yang
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  2. Kok Kiong Tan
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  3. Arthur Tay
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  4. Sunan Huang
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  5. Jie Sun
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  6. Jerry Fuh
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  8. Chek Sing Teo
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  9. Zidong Wang
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Correspondence to Rui Yang.

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Yang, R., Tan, K.K., Tay, A. et al. An RBF neural network approach to geometric error compensation with displacement measurements only. Neural Comput & Applic 28, 1235–1248 (2017). https://doi.org/10.1007/s00521-016-2486-2

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  • DOI: https://doi.org/10.1007/s00521-016-2486-2

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