The Development and Experimental Validation of a Real-Time Coupled Gear Wear Prediction Model Considering Initial Surface Topography, Dynamics, and Thermal Deformation

Errors affect the actual meshing process of gears, alter the actual wear pattern of the tooth profile, and may even impact the overall service life of machinery. While existing research predominantly focuses on individual errors or a narrow set of factors, this study explores the combined effects of...

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Main Authors: Jingqi Zhang, Jianxing Zhou, Quanwei Cui, Ning Dong, Hong Jiang, Zhong Fang
Format: Article
Language:English
Published: MDPI AG 2024-10-01
Series:Machines
Subjects:
Online Access:https://www.mdpi.com/2075-1702/12/10/734
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author Jingqi Zhang
Jianxing Zhou
Quanwei Cui
Ning Dong
Hong Jiang
Zhong Fang
author_facet Jingqi Zhang
Jianxing Zhou
Quanwei Cui
Ning Dong
Hong Jiang
Zhong Fang
author_sort Jingqi Zhang
collection DOAJ
description Errors affect the actual meshing process of gears, alter the actual wear pattern of the tooth profile, and may even impact the overall service life of machinery. While existing research predominantly focuses on individual errors or a narrow set of factors, this study explores the combined effects of multiple errors on tooth profile wear. A comprehensive gear wear prediction model was developed, integrating the slice method, lumped mass method, Hertz contact model, and Archard’s wear theory. This model accounts for initial tooth surface topography, thermal deformation, dynamic effects, and wear, establishing strong correlations between gear wear prediction and key factors such as tooth surface morphology, temperature, and vibration. Experimental validation demonstrated the model’s high accuracy, with relatively small deviations from the observed wear. Initial profile errors (IPEs) at different positions along the tooth width result in varying relative sliding distances, leading to differences in wear depth despite a consistent overall trend. Notably, large IPEs at the dedendum and addendum can influence wear progression, either accelerating or decelerating the wear process over time.
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issn 2075-1702
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publishDate 2024-10-01
publisher MDPI AG
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series Machines
spelling doaj-art-e6632522c8fe44429de59d86a5c959f72025-08-20T02:10:56ZengMDPI AGMachines2075-17022024-10-01121073410.3390/machines12100734The Development and Experimental Validation of a Real-Time Coupled Gear Wear Prediction Model Considering Initial Surface Topography, Dynamics, and Thermal DeformationJingqi Zhang0Jianxing Zhou1Quanwei Cui2Ning Dong3Hong Jiang4Zhong Fang5College of Intelligent Manufacturing Modern Industry (School of Mechanical Engineering), Xinjiang University, Urumqi 830047, ChinaCollege of Intelligent Manufacturing Modern Industry (School of Mechanical Engineering), Xinjiang University, Urumqi 830047, ChinaCollege of Intelligent Manufacturing Modern Industry (School of Mechanical Engineering), Xinjiang University, Urumqi 830047, ChinaSchool of Mechanical and Electrical Engineering, Northwestern Polytechnical University, Xi’an 710072, ChinaCollege of Intelligent Manufacturing Modern Industry (School of Mechanical Engineering), Xinjiang University, Urumqi 830047, ChinaXinjiang Goldwind Science and Technology Co., Ltd., Urumqi 830026, ChinaErrors affect the actual meshing process of gears, alter the actual wear pattern of the tooth profile, and may even impact the overall service life of machinery. While existing research predominantly focuses on individual errors or a narrow set of factors, this study explores the combined effects of multiple errors on tooth profile wear. A comprehensive gear wear prediction model was developed, integrating the slice method, lumped mass method, Hertz contact model, and Archard’s wear theory. This model accounts for initial tooth surface topography, thermal deformation, dynamic effects, and wear, establishing strong correlations between gear wear prediction and key factors such as tooth surface morphology, temperature, and vibration. Experimental validation demonstrated the model’s high accuracy, with relatively small deviations from the observed wear. Initial profile errors (IPEs) at different positions along the tooth width result in varying relative sliding distances, leading to differences in wear depth despite a consistent overall trend. Notably, large IPEs at the dedendum and addendum can influence wear progression, either accelerating or decelerating the wear process over time.https://www.mdpi.com/2075-1702/12/10/734spur gearwear predictionthermal deformationinitial profile errorcompetition mechanismcoupling
spellingShingle Jingqi Zhang
Jianxing Zhou
Quanwei Cui
Ning Dong
Hong Jiang
Zhong Fang
The Development and Experimental Validation of a Real-Time Coupled Gear Wear Prediction Model Considering Initial Surface Topography, Dynamics, and Thermal Deformation
Machines
spur gear
wear prediction
thermal deformation
initial profile error
competition mechanism
coupling
title The Development and Experimental Validation of a Real-Time Coupled Gear Wear Prediction Model Considering Initial Surface Topography, Dynamics, and Thermal Deformation
title_full The Development and Experimental Validation of a Real-Time Coupled Gear Wear Prediction Model Considering Initial Surface Topography, Dynamics, and Thermal Deformation
title_fullStr The Development and Experimental Validation of a Real-Time Coupled Gear Wear Prediction Model Considering Initial Surface Topography, Dynamics, and Thermal Deformation
title_full_unstemmed The Development and Experimental Validation of a Real-Time Coupled Gear Wear Prediction Model Considering Initial Surface Topography, Dynamics, and Thermal Deformation
title_short The Development and Experimental Validation of a Real-Time Coupled Gear Wear Prediction Model Considering Initial Surface Topography, Dynamics, and Thermal Deformation
title_sort development and experimental validation of a real time coupled gear wear prediction model considering initial surface topography dynamics and thermal deformation
topic spur gear
wear prediction
thermal deformation
initial profile error
competition mechanism
coupling
url https://www.mdpi.com/2075-1702/12/10/734
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