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Based on Data-Augmentation long short-term memory gear meshing accuracy and error compensation.

Fucong Liu¹, Luyang Zou², Ze Cao^2,3

¹Tianjin High-end Intelligent Machine Tools Engineering Research Center, Tianjin University of Technology and Education, No 1310, Dagu South Road, Tianjin, 300222, People's Republic of China. 21202058@qq.com.

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Summary

This summary is machine-generated.

This study introduces a Data-Augmentation Long Short-Term Memory (DA-LSTM) model for precise gear manufacturing. The model efficiently compensates for errors in complex, non-uniform gear meshing, achieving high accuracy with reduced computational cost.

Keywords:

Error evaluation LSTM Non-uniform meshing Precision optimization

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Area of Science:

Mechanical Engineering
Manufacturing Technology
Artificial Intelligence

Background:

Conventional gear error compensation methods struggle with non-uniform meshing and high computational costs.
Existing techniques lack adaptability for complex gear shaping processes.

Purpose of the Study:

To develop a novel method for high-precision gear manufacturing using advanced machine learning.
To address the limitations of traditional error compensation in non-uniform meshing scenarios.

Main Methods:

Implementation of a Data-Augmentation Long Short-Term Memory (DA-LSTM) model.
Integration of adaptive data augmentation with LSTM architecture for error prediction.
Optimization of tool trajectories and process parameters for gear shaping.

Main Results:

Achieved GB5-level machining accuracy (GB/T 10095-2008 Grade 5) for gears.
Reduced computational cost by 40% compared to baseline methods.
Demonstrated superior prediction accuracy across varying non-uniform meshing conditions.

Conclusions:

The DA-LSTM model offers an efficient and adaptive solution for high-precision gear manufacturing.
This approach overcomes limitations of traditional methods in complex, non-ideal meshing environments.
Provides a new technical pathway for advanced gear manufacturing processes.