Prediction and analysis of thermal drift in large-scale tooling reference points under non-uniform temperature field
In the digital measurement of aircraft assembly, the accuracy of large-size measurement field construction is highly dependent on the stability of the reference points laid on the tooling. The position of the reference points of large-sized tooling is very susceptible to thermal drift due to changes...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Advances in Aeronautical Science and Engineering
2025-06-01
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| Series: | Hangkong gongcheng jinzhan |
| Subjects: | |
| Online Access: | http://hkgcjz.cnjournals.com/hkgcjz/article/abstract/2023268 |
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| Summary: | In the digital measurement of aircraft assembly, the accuracy of large-size measurement field construction is highly dependent on the stability of the reference points laid on the tooling. The position of the reference points of large-sized tooling is very susceptible to thermal drift due to changes in ambient temperature, leading to a reduction in the accuracy of the measurement field or even failure. Therefore, this paper takes a combined largescale tooling as an example to construct a numerical model for predicting the thermal drift of the reference points of large-scale tooling under a non-uniform temperature field; constructs a proxy model for the thermal drift of the tooling based on a large amount of thermal drift data obtained from the simulation of the aforementioned model using BP neural network; and formulates a program for improving the accuracy of the measurement field based on the aforementioned proxy model. The temperature and coordinate measurement data collected in the field at the reference points of the tooling are used to verify the validity and correctness of the proxy model, and the temperature-coordinate drift data at the reference points obtained from the model are compared and analyzed. The results show that the average relative errors of the simulation results are below 18%, and the average relative errors of the BP neural network results are below 26%, which can effectively improve the measurement field construction accuracy. |
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| ISSN: | 1674-8190 |