Affiliation:
1. State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China
Abstract
Abstract
Polymer gears have shown potential in power transmission by their comprehensive mechanical properties. One of the significant concerns with expanding their applications is the deficiency of reliability evaluation methods considering small data set circumstances. This work conducts a fair number of polyoxymethylene (POM) gear durability tests with adjustable loading and lubrication conditions via a gear durability test rig. A novel machine learning-based reliability model is developed to evaluate contact fatigue reliability for the POM gears with such a data set. Results reveal that the model predicts reasonable POM gear contact fatigue curves of reliability–stress–number of cycles with 2.0% relative error and 18.8% reduction of test specimens compared with the large sample data case. In contrast to grease lubrication, the oil-lubricated POM gear contact fatigue strength improves by 10.4% from 52.1 to 57.6 MPa.
Funder
National Key Research and Development Program of China
Publisher
Oxford University Press (OUP)
Subject
Computational Mathematics,Computer Graphics and Computer-Aided Design,Human-Computer Interaction,Engineering (miscellaneous),Modeling and Simulation,Computational Mechanics
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