Symmetry preserving neural network models for spur gear static transmission error curves
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Date
2023-09
Publication Type
Journal Article
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Abstract
This paper proposes the use of neural networks to predict static transmission error (STE) curves for spur gears. Initially, a dataset spanning a parametric space of 17 parameters and comprising 20000 STE curves is created with a physics based solver, utilizing a dimensionless formulation. This data is used to train and evaluate different neural network architectures, which incorporate the symmetries of periodicity and input–output interchangeability. Results show that a small fully connected network with 3 hidden layers of 60 neurons can capture the highly non-linear STE response accurately, achieving a mean absolute percentage error of 0.075% on previously unseen data, while the incorporation of symmetries noticeably improves performance. The highest errors are below 1% and occur in border regions of the dataset, where training data is sparse. These results indicate that neural network models can faithfully reproduce the predictions of traditional, non-linear solvers and are thus a promising approach for modeling the static response of spur gears over extended parametric spaces. Finally, indicative dynamic simulations investigate the extension of these results to the dynamic regime.
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published
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Journal / series
Volume
187
Pages / Article No.
105369
Publisher
Elsevier
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Subject
Static transmission error; Spur gears; Surrogate model; Machine learning; Periodic neural network