A novel weakly supervised adversarial network for thermal error modeling of electric spindles with scarce samples-Reference-Cited by-同舟云学术

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A novel weakly supervised adversarial network for thermal error modeling of electric spindles with scarce samples

Published:2024-03 Issue: Volume:238 Page:122065
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Ma Shuai,Leng Jiewu,Chen Zhuyun,Li Bo,Li Xing,Zhang Ding,Li Weihua,Liu Qiang^ORCID

Funder

National Natural Science Foundation of China

State Administration for Science, Technology and Industry for National Defense

Foundation for Innovative Research Groups of the National Natural Science Foundation of China

Science and Technology Program of Hubei Province

Basic and Applied Basic Research Foundation of Guangdong Province

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference36 articles.

1. International Status of Thermal Error Research (1990);Bryan;CIRP Annals,1990

2. A data-driven model for thermal error prediction considering thermoelasticity with gated recurrent unit attention;Chen;Measurement,2021

3. Spindle thermal error modeling method considering the operating condition based on Long Short-Term Memory;Chen;Engineering Research Express,2021

4. A Multi-Source Weighted Deep Transfer Network for Open-Set Fault Diagnosis of Rotary Machinery;Chen;Ieee Transactions On Cybernetics,2023

5. Generalized open-set domain adaptation in mechanical fault diagnosis using multiple metric weighting learning network;Chen;Advanced Engineering Informatics,2023

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3. Research on thermal error of electric spindle based on ISCA-GRNN;2024-07-01

4. A novel adaptive deep transfer learning method towards thermal error modeling of electric spindles under variable conditions;Journal of Manufacturing Systems;2024-06

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