What problem does this paper address?

Feed-axis thermal error evolves over time and depends on operating conditions recorded by the CNC controller. The paper addresses models that overlook this temporal history or exclude readily available electro-control signals, limiting accuracy and robustness across working conditions.

Main contributions

• An LSTM thermal-error model that represents the time dependence of feed-axis behavior.
• Integration of temperature changes with electro-control data describing machine operating conditions.
• Experimental comparison intended to test prediction accuracy and robustness under changing conditions.

Key results

• The reported experiments found improved prediction accuracy and robustness when temperature variation and electro-control data were modeled together; the available verified sources did not expose the paper's numerical values.

Relevant research topics

• CNC Machine Tools
• Feed-Axis Thermal Error
• LSTM Networks
• Electro-Control Data
• Time-Series Modeling
• Precision Manufacturing

Preferred citation

@article{huang2021lstm,
  author  = {Yushan Huang and Jihong Chen and Yu Chen and Guangda Xu},
  title   = {Thermal error modeling for machine tool feed axis based on {LSTM} neural network considering electro-control data},
  journal = {Modern Manufacturing Engineering},
  number  = {10},
  pages   = {25--32},
  year    = {2021},
  doi     = {10.16731/j.cnki.1671-3133.2021.10.004}
}