Prediction of Gear Deviations in Dry Hobbing Process Based on CNN-BiLSTM

Huangshuai Li; Benjie Li

doi:10.54097/qt197505

Authors

Huangshuai Li
Benjie Li

DOI:

https://doi.org/10.54097/qt197505

Keywords:

Dry hobbing; Gear deviations; Feature fusion; long short-term memory.

Abstract

Dry hobbing is an advanced gear machining technology widely used in automotive manufacturing due to its high efficiency, cost-effectiveness, and environmental benefits. As the demand for high-precision gear production continues to rise, achieving consistent machining accuracy remains a critical challenge. To address this issue, this paper proposes an in-situ deviation prediction model for dry hobbing, integrating machining parameters and vibration data. First, time-frequency domain dynamic features of spindle vibrations during dry hobbing are extracted, while machining parameters such as hob rotational speed and axial feed rate are incorporated as static features. These fused features are then used to train an in-situ prediction model based on a CNN-BiLSTM architecture. Furthermore, Bayesian optimization is employed to fine-tune the hyperparameters of the model. The proposed approach is validated using test and verification datasets, with results demonstrating that the fusion of static and dynamic features significantly enhances prediction accuracy. This in-situ prediction model provides manufacturers with a valuable tool for real-time monitoring and control of gear deviations in industrial production.

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