A novel prediction approach of polymer gear contact fatigue based on a WGAN‐XGBoost model

Abstract

Polymer gears have long been used on power transmissions with the fundamental durability data, including fatigue S‐N curves, yielding important data informing reliable and compact designs. This paper proposed a prediction method for polyformaldehyde (POM) gear fatigue life based on the innovative WGAN‐XGBoost algorithm. The findings generated herein revealed that the proposed method performs well in terms of prediction accuracy. The predicted fatigue lives, analyzed under different loading conditions, were within 1.5 times dispersion band compared with experimental results. Furthermore, based upon the enhanced fatigue dataset, a thermo‐mechanical coupled prediction formula for POM gear contact fatigue life was proposed. These findings offered a new approach for high‐power density design of polymer gears.

Effect of Process Parameters on the Crystallinity and Geometric Quality of Injection Molded Polymer Gears and the Resulting Stress State during Gear Operation

“…Based on the recognized polymer gear failure modes and available design methods, a comprehensive polymer gears design optimization approach was introduced in [12]. Attempts have been undertaken to integrate machine learning algorithms into the gear design [13,14], demonstrating their effectiveness in assessing unconventional gear designs.…”