Thermal error prediction and control method combining residual-based one-dimensional convolution-minimum gate unit model with physical-data-edge-cloud terminal architecture-Reference-Cited by-同舟云学术

Thermal error prediction and control method combining residual-based one-dimensional convolution-minimum gate unit model with physical-data-edge-cloud terminal architecture

Published:2023-04-12 Issue:21 Volume:35 Page:15477-15502
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Luo Fangqiong,Ma Chi^ORCID,Liu Jialan,Gui Hongquan,Li Mengyuan

Funder

National Natural Science Foundation of China

Natural Science Foundation Project of Chongqing, Chongqing Science and Technology Commission

Fundamental Research Funds for the Central Universities

Venture and Innovation Support Program for Chongqing Overseas Returnees

State Key Laboratory for Manufacturing Systems Engineering

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s00521-023-08553-6.pdf

Reference56 articles.

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2. Liu JL, Ma C, Wang SL et al (2019) Thermal-structure interaction characteristics of a high-speed spindle-bearing system. Int J Mach Tools Manuf 137:42–57. https://doi.org/10.1016/j.ijmachtools.2018.10.004

3. Wu CY, Xiang ST, Xiang WS (2021) Spindle thermal error prediction approach based on thermal infrared images: a deep learning method. J Manuf Syst 59:67–80. https://doi.org/10.1016/j.jmsy.2021.01.013

4. Li Y, Zhao WH, Lan SH et al (2015) A review on spindle thermal error compensation in machine tools. Int J Mach Tools Manuf 95:20–38. https://doi.org/10.1016/j.ijmachtools.2015.04.008

5. Mayr J, Jedrzejewski J, Uhlmann E et al (2012) Thermal issues in machine tools. CIRP Ann Manuf Technol 61(2):771–791. https://doi.org/10.1016/j.cirp.2012.05.008

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