A review on transfer learning in spindle thermal error compensation of spindle
Data-driven approaches offer unprecedented opportunities for smart manufacturing to facilitate the transition to Industry 4.0-based production. One of the key factors affecting the accuracy of machine tools is the thermal error caused by thermal deformation. A major heat source among those that caus...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
ELSPublishing
2024-09-01
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| Series: | Advanced Manufacturing |
| Subjects: | |
| Online Access: | https://elsp-homepage.oss-cn-hongkong.aliyuncs.compaper/journal/open/AM/2024/manuscript-AM-24070016-final_v3.pdf |
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| Summary: | Data-driven approaches offer unprecedented opportunities for smart manufacturing to facilitate the transition to Industry 4.0-based production. One of the key factors affecting the accuracy of machine tools is the thermal error caused by thermal deformation. A major heat source among those that cause thermal deformation in machine tools is the spindle. Transfer learning plays a key role in developing intelligent systems for thermal error prediction in machine tools. In this paper, the opportunities and challenges of migration learning for thermal error modeling of spindles are reviewed. The main models of transfer learning are discussed, including, and their application to spindle thermal error modeling is overviewed. The purpose of this paper is to provide a basic introduction to the whole process of thermal error compensation in spindles and to give an overview of the different topics of thermal error modeling methods. |
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| ISSN: | 2959-3263 2959-3271 |