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A numerical control machining tool path step error prediction method based on BP neural network
Step error calculation of numerical control (NC) machining tool path is a premise for generating high-quality tool path and promoting its application. At present, iterative methods are generally used to calculate step error, and the computation time increases when accuracy improves. Neural networks...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10539533/ https://www.ncbi.nlm.nih.gov/pubmed/37770650 http://dx.doi.org/10.1038/s41598-023-43617-6 |
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author | Zhang, Zi-Yu Liu, Wei Li, Peng-Fei Zhang, Jia-Ping Fan, Lv-Yang |
author_facet | Zhang, Zi-Yu Liu, Wei Li, Peng-Fei Zhang, Jia-Ping Fan, Lv-Yang |
author_sort | Zhang, Zi-Yu |
collection | PubMed |
description | Step error calculation of numerical control (NC) machining tool path is a premise for generating high-quality tool path and promoting its application. At present, iterative methods are generally used to calculate step error, and the computation time increases when accuracy improves. Neural networks can be calculated on GPUs and cloud platforms, which is conducive to reducing computation time and improving accuracy through continuous learning. This article innovatively introduces a BP neural network model to predict step error values. Firstly, the core parameters required for step error calculation are taken as the data samples to construct the neural network model, and map to the same scale through Z-score normalization to eliminate the adverse effects of singular parameters on the calculation results. Then, considering only a small number of parameters determine theoretical values of step error, the Dropout technique can drop hidden layer neurons with a certain probability, which is helpful to avoid overfitting and used in the neural network model design. In the neural network model training, this paper adds the Stochastic Gradient Descent with Momentum (SGDM) optimizer to the back propagation of network training in order to improves the network’ stability and accuracy. The proposed neural network predicts step error of samples from three surface models, the results show that the prediction error decreases as sample training increases. After trained by 15% of the surface samples, the neural network predicts the step errors of the remaining samples. Compared with theoretical values, more than 99% of the predicted values have an absolute error less than 1 μm. Moreover, the cost time is only one-third of the geometric method, which verifies the effectiveness and efficiency of our method. |
format | Online Article Text |
id | pubmed-10539533 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-105395332023-09-30 A numerical control machining tool path step error prediction method based on BP neural network Zhang, Zi-Yu Liu, Wei Li, Peng-Fei Zhang, Jia-Ping Fan, Lv-Yang Sci Rep Article Step error calculation of numerical control (NC) machining tool path is a premise for generating high-quality tool path and promoting its application. At present, iterative methods are generally used to calculate step error, and the computation time increases when accuracy improves. Neural networks can be calculated on GPUs and cloud platforms, which is conducive to reducing computation time and improving accuracy through continuous learning. This article innovatively introduces a BP neural network model to predict step error values. Firstly, the core parameters required for step error calculation are taken as the data samples to construct the neural network model, and map to the same scale through Z-score normalization to eliminate the adverse effects of singular parameters on the calculation results. Then, considering only a small number of parameters determine theoretical values of step error, the Dropout technique can drop hidden layer neurons with a certain probability, which is helpful to avoid overfitting and used in the neural network model design. In the neural network model training, this paper adds the Stochastic Gradient Descent with Momentum (SGDM) optimizer to the back propagation of network training in order to improves the network’ stability and accuracy. The proposed neural network predicts step error of samples from three surface models, the results show that the prediction error decreases as sample training increases. After trained by 15% of the surface samples, the neural network predicts the step errors of the remaining samples. Compared with theoretical values, more than 99% of the predicted values have an absolute error less than 1 μm. Moreover, the cost time is only one-third of the geometric method, which verifies the effectiveness and efficiency of our method. Nature Publishing Group UK 2023-09-28 /pmc/articles/PMC10539533/ /pubmed/37770650 http://dx.doi.org/10.1038/s41598-023-43617-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Zhang, Zi-Yu Liu, Wei Li, Peng-Fei Zhang, Jia-Ping Fan, Lv-Yang A numerical control machining tool path step error prediction method based on BP neural network |
title | A numerical control machining tool path step error prediction method based on BP neural network |
title_full | A numerical control machining tool path step error prediction method based on BP neural network |
title_fullStr | A numerical control machining tool path step error prediction method based on BP neural network |
title_full_unstemmed | A numerical control machining tool path step error prediction method based on BP neural network |
title_short | A numerical control machining tool path step error prediction method based on BP neural network |
title_sort | numerical control machining tool path step error prediction method based on bp neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10539533/ https://www.ncbi.nlm.nih.gov/pubmed/37770650 http://dx.doi.org/10.1038/s41598-023-43617-6 |
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