A Semi-Analytical Loaded Contact Model and Load Tooth Contact Analysis Approach of Ease-Off Spiral Bevel Gears

This paper presents an innovative and comprehensive methodology for loaded tooth contact analysis (LTCA) of spiral bevel gears, integrating ease-off surface computation with high-precision virtual generating tooth surfaces. The methodology integrates an error-sensitivity analysis model with a semi-analytical LTCA model for spiral bevel gears based on ease-off surfaces, developed using a Universal Generation Model. By leveraging sophisticated corrections in the machining process, the desired ease-off surfaces are obtained, ensuring the accuracy of the generated tooth surfaces. This simulation ensures minimal errors between theoretical and virtual generating tooth surfaces, providing a reliable basis for LTCA. The LTCA model is formulated using CNC-generated tooth surfaces, focusing on misalignments such as pinion offset, adjustment errors, and angular position errors along the pinion and gear axis. The feasibility and effectiveness of the proposed method are verified through comparisons with LTCA software analysis results, demonstrating its high accuracy in predicting the impact of misalignments on contact patterns and load distribution. This integrated approach offers significant advancements in the design and analysis of spiral bevel gears, providing a robust tool for predicting and analyzing gear performance under various misalignment conditions. The combined methodology enhances the reliability and accuracy of LTCA, ensuring optimal performance and durability of spiral bevel gears in practical applications.

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Publication Details

Journal
Machines
Published
2024-09-05
DOI
https://doi.org/10.3390/machines12090623
Citations
9
Primary Topic
Gear and Bearing Dynamics Analysis
Type
article
Field-Weighted Citation Impact
1.91
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article

A Semi-Analytical Loaded Contact Model and Load Tooth Contact Analysis Approach of Ease-Off Spiral Bevel Gears

Duansen Shangguan, Yuhui Liu, Xian Mao, Liping Chen
9 citations
Machines
Gear and Bearing Dynamics Analysis
1.91
article

A Semi-Analytical Loaded Contact Model and Load Tooth Contact Analysis Approach of Ease-Off Spiral Bevel Gears

Duansen Shangguan, Yuhui Liu, Xian Mao, Liping Chen
article en
9 citations

Abstract

This paper presents an innovative and comprehensive methodology for loaded tooth contact analysis (LTCA) of spiral bevel gears, integrating ease-off surface computation with high-precision virtual generating tooth surfaces. The methodology integrates an error-sensitivity analysis model with a semi-analytical LTCA model for spiral bevel gears based on ease-off surfaces, developed using a Universal Generation Model. By leveraging sophisticated corrections in the machining process, the desired ease-off surfaces are obtained, ensuring the accuracy of the generated tooth surfaces. This simulation ensures minimal errors between theoretical and virtual generating tooth surfaces, providing a reliable basis for LTCA. The LTCA model is formulated using CNC-generated tooth surfaces, focusing on misalignments such as pinion offset, adjustment errors, and angular position errors along the pinion and gear axis. The feasibility and effectiveness of the proposed method are verified through comparisons with LTCA software analysis results, demonstrating its high accuracy in predicting the impact of misalignments on contact patterns and load distribution. This integrated approach offers significant advancements in the design and analysis of spiral bevel gears, providing a robust tool for predicting and analyzing gear performance under various misalignment conditions. The combined methodology enhances the reliability and accuracy of LTCA, ensuring optimal performance and durability of spiral bevel gears in practical applications.

MachinesVol. 12(9)
Huazhong University of Science and Technology (CN)
Openalex Percentile: Top 15%
Gear and Bearing Dynamics Analysis
1.91
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A Semi-Analytical Loaded Contact Model and Load Tooth Contact Analysis Approach of Ease-Off Spiral Bevel Gears — Duansen Shangguan, Yuhui Liu, et al. · Machines (2024) | TGRS Research Map | TGRS