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research-article

Prediction of surface wear of involute gears based on a modified fractal method

[+] Author and Article Information
Gang Li

Department of Mechanical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250
ligangteller@163.com

Zhonghou Wang

School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
wang_zhonghou18@163.com

Weidong Zhu

Fellow ASME, Division of Dynamics and Control, School of Astronautics, Harbin Institute of Technology, Harbin 150001, China; Department of Mechanical Engineering, University of Maryland, Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250
wzhu@umbc.edu

1Corresponding author.

ASME doi:10.1115/1.4041587 History: Received February 09, 2018; Revised September 20, 2018

Abstract

A new wear prediction method of tooth surfaces of involute gears based on a real tooth surface model and a modified fractal method is developed. The real tooth surface model of an involute gear pair is introduced, and micro-geometry feature detection of tooth surfaces is achieved by monitoring variations of normal vectors of each discrete data point of the real tooth surface model. To predict wear progression of tooth surfaces of a gear pair, an abrasive wear analysis model and the modified fractal method are used to analyze contact performance and its changes with accumulation of surface wear. The abrasive wear analysis model can analyze wear depths of gear tooth surfaces with sliding distances, local contact pressure and directions of wear progression based on the Archard's model. The modified fractal method is proposed to calculate instantaneous contact stiffness and estimate elastic and plastic deformation regions based on an asperity contact model. Micro-geometry features of tooth surface asperities can be described as the basis of an asperity contact model, and allow tooth contact analysis of real tooth surface models with their local micro-geometry feature changes due to plastic deformations. Feasibility and effectiveness of this wear prediction method were verified by comparing predicted results of gear surface wear progression with gear wear test results.

Copyright (c) 2018 by ASME
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