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

ABSTRACT: Conventional compensation approaches for gear error (e.g., Monte Carlo simulations) have shown efficacy in uniform meshing scenarios, however, two drawbacks limit further applications: (1) high computationally cost and low efficiency in practical implementation; (2) inadequate adaptability to complex non-uniform meshing dynamics. Addressing these issues, this work proposes a Data-Augmentation Long Short-Term Memory (DA-LSTM) model to achieve high-precision manufacturing (GB5-level, GB/T 10095 - 2008 Grade 5) of gears machined through shaping processes. The developed DA-LSTM framework combines LSTM architecture with adaptive Data-Augmentation strategies, enabling efficient prediction and compensation of machining errors under non-uniform engagement conditions. By optimizing tools trajectori

SUBMITTER: Liu F 

PROVIDER: S-EPMC12009318 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature