<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The relative positions between the four slide blocks vary with the movement of the table due to the geometric errors of the guide rail. Consequently, the additional load on the slide blocks is increased. A new method of error measurement and identification by using a self-designed stress test plate was presented. BP neural network model was used to establish the mapping between the stress of key measurement points on the test plate and the displacements of slide blocks. By measuring the stress, the relative displacements of slide blocks were obtained, from which the geometric errors of the guide rails were converted. Firstly, the finite element model was built to find the key measurement points of the test plate. Then the BP neural network was trained by using the samples extracted from the finite element model. The stress at the key measurement points were taken as the input and the relative displacements of the slide blocks were taken as the output. Finally, the geometric errors of the two guide rails were obtained according to the measured stress. The results show that the maximum difference between the measured geometric errors and the output of BP neural network was 5 μm. Therefore, the correctness and feasibility of the method were verified.</p></abstract>

The relative positions between the four slide blocks vary with the movement of the table due to the geometric errors of the guide rail. Consequently, the additional load on the slide blocks is increased. A new method of error measurement and identification by using a self-designed stress test plate was presented. BP neural network model was used to establish the mapping between the stress of key measurement points on the test plate and the displacements of slide blocks. By measuring the stress, the relative displacements of slide blocks were obtained, from which the geometric errors of the guide rails were converted. Firstly, the finite element model was built to find the key measurement points of the test plate. Then the BP neural network was trained by using the samples extracted from the finite element model. The stress at the key measurement points were taken as the input and the relative displacements of the slide blocks were taken as the output. Finally, the geometric errors of the two guide rails were obtained according to the measured stress. The results show that the maximum difference between the measured geometric errors and the output of BP neural network was 5 μm. Therefore, the correctness and feasibility of the method were verified.

Identification and Adjustment of Guide Rail Geometric Errors Based on BP Neural Network

Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education

Measurement Science Review

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The relative positions between the four slide blocks vary with the movement of the table due to the geometric errors of the guide rail. Consequently, the...

Identification and Adjustment of Guide Rail Geometric Errors Based on BP Neural Network

Publié en ligne: 15 juin 2017

Pages: 135 - 144

Reçu: 02 févr. 2017

Accepté: 18 mai 2017

DOI: https://doi.org/10.1515/msr-2017-0017

Mots clés
Guide rail geometric error, stress, the test plate, finite element model, BP neural network

© 2017 Gaiyun He et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Identification and Adjustment of Guide Rail Geometric Errors Based on BP Neural Network

Publié en ligne: 15 juin 2017

Pages: 135 - 144

Reçu: 02 févr. 2017

Accepté: 18 mai 2017

DOI: https://doi.org/10.1515/msr-2017-0017

Mots clésGuide rail geometric error, stress, the test plate, finite element model, BP neural network

© 2017 Gaiyun He et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
Guide rail geometric error, stress, the test plate, finite element model, BP neural network