Reduced order model-augmented neural network for thermal error modeling in machine tool spindle system.
Saved in:
| Title: | Reduced order model-augmented neural network for thermal error modeling in machine tool spindle system. |
|---|---|
| Authors: | Tan, Feng1 (AUTHOR) itanfeng@qq.com, Chen, Hongxu2 (AUTHOR), Peng, Ji3 (AUTHOR), Deng, Congying1 (AUTHOR) |
| Source: | International Journal of Advanced Manufacturing Technology. Jun2025, Vol. 138 Issue 9, p4293-4311. 19p. |
| Subjects: | Spindles (Machine tools), Singular value decomposition, K-means clustering, Support vector machines, Artificial intelligence |
| Abstract: | Accurate thermal error modeling is fundamental for machine tool thermal error compensation. However, traditional data-driven thermal error modeling methods rely solely on the experimental temperature and thermal error data, neglecting the underlying thermal structural mechanism. To incorporate both mechanism-driven and data-driven characteristics, we propose a novel reduced order model (ROM)-augmented neural network (NN) thermal error modeling method. Firstly, a rapid and precise ROM is developed to supplant the original, expensive transient thermal structural simulation. The ROM preserves the thermal structural mechanism through singular value decomposition (SVD) on a series of simulations. Given measured key temperatures, it can reconstruct the thermal displacement field in real time through truncated eigenmodes superposition leveraging predicted modal coefficients. Subsequently, introducing the output of the ROM as an additional pivotal variable for the middle layers of the NN, the ROM-augmented NN model can be trained with experimental data. Key temperature points, the input in thermal error prediction, are selected by combining K-means clustering with information gain. Finally, the proposed model demonstrates a 71%, 41%, 34%, and 21% relative improvement in performance across five spindle speeds compared to the ROM, multiple linear regression (MLR), least square support vector machine (LSSVM) and NN models, respectively. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Advanced Manufacturing Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Accurate thermal error modeling is fundamental for machine tool thermal error compensation. However, traditional data-driven thermal error modeling methods rely solely on the experimental temperature and thermal error data, neglecting the underlying thermal structural mechanism. To incorporate both mechanism-driven and data-driven characteristics, we propose a novel reduced order model (ROM)-augmented neural network (NN) thermal error modeling method. Firstly, a rapid and precise ROM is developed to supplant the original, expensive transient thermal structural simulation. The ROM preserves the thermal structural mechanism through singular value decomposition (SVD) on a series of simulations. Given measured key temperatures, it can reconstruct the thermal displacement field in real time through truncated eigenmodes superposition leveraging predicted modal coefficients. Subsequently, introducing the output of the ROM as an additional pivotal variable for the middle layers of the NN, the ROM-augmented NN model can be trained with experimental data. Key temperature points, the input in thermal error prediction, are selected by combining K-means clustering with information gain. Finally, the proposed model demonstrates a 71%, 41%, 34%, and 21% relative improvement in performance across five spindle speeds compared to the ROM, multiple linear regression (MLR), least square support vector machine (LSSVM) and NN models, respectively. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 02683768 |
| DOI: | 10.1007/s00170-025-15758-7 |
