Prediction of machining errors for free-form surfaces based on an improved grey model (1,

\nAn improved grey model (GM) for predicting free-form surface machining errors was established to address the low measuring efficiencies of coordinate measuring machines (CMMs) and the low prediction accuracy of the GM(1,1) model. A number of points on a free-form surface are measured with a CMM, and machining errors are obtained. Ideas from metabolic methods and the GM(1,N) prediction model were combined. To reduce the impact of random fluctuations of the machining errors, a Markov prediction model was then used to correct the fitted results for the residuals and obtain the predicted results of the machining errors for free-form surfaces, thus improving the prediction accuracy of the model. The predicted results of the metabolic GM(1,1) and metabolic GM(1,N) models were then compared. The experimental results showed that the combination of metabolic theory, a grey model, and Markov theory effectively improved the prediction accuracy.\n

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Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2024-08-20
DOI
https://doi.org/10.1051/ijmqe/2024011/pdf
Primary Topic
Genetic Mapping and Diversity in Plants and Animals
Type
article
Field-Weighted Citation Impact
0.00

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article

Prediction of machining errors for free-form surfaces based on an improved grey model (1,

Springer Link (Chiba Institute of Technology)
Genetic Mapping and Diversity in Plants and Animals
article

Prediction of machining errors for free-form surfaces based on an improved grey model (1,

article en

Abstract

\nAn improved grey model (GM) for predicting free-form surface machining errors was established to address the low measuring efficiencies of coordinate measuring machines (CMMs) and the low prediction accuracy of the GM(1,1) model. A number of points on a free-form surface are measured with a CMM, and machining errors are obtained. Ideas from metabolic methods and the GM(1,N) prediction model were combined. To reduce the impact of random fluctuations of the machining errors, a Markov prediction model was then used to correct the fitted results for the residuals and obtain the predicted results of the machining errors for free-form surfaces, thus improving the prediction accuracy of the model. The predicted results of the metabolic GM(1,1) and metabolic GM(1,N) models were then compared. The experimental results showed that the combination of metabolic theory, a grey model, and Markov theory effectively improved the prediction accuracy.\n

Springer Link (Chiba Institute of Technology)
Guangxi University of Science and Technology (CN)
National Natural Science Foundation of China, Natural Science Foundation of Guangxi Province, Guangxi University, Guangxi University of Science and Technology
Openalex Percentile: Top 50%
Genetic Mapping and Diversity in Plants and Animals
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