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基于冲击载荷的三体磨料作用下斜齿轮磨损机理研究

Study on Wear Mechanism of Helical Gear by Three-Body Abrasive Based on Impact Load.

作者信息

Yuan Wei, Wang Haotian, Guo Qianjian, Wang Wenhua, Zhu Yuqi, Yu Jie, Yang Xianhai

机构信息

School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China.

出版信息

Materials (Basel). 2022 Jun 10;15(12):4135. doi: 10.3390/ma15124135.

DOI:10.3390/ma15124135
PMID:35744196
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9229044/
Abstract

This study aimed to explore the wear characteristics and evolution mechanisms of large-scale wind power gears under the impact load of particles of the three-body abrasive Al2O3 (0.2 mg/mL) from four aspects: oil analysis, vibration analysis, amount of gear wear, and tooth-surface-wear profile analysis. A magnetic powder brake was used to simulate the actual working conditions. Combined with the abrasive particle monitoring and the morphology analysis of the tooth-surface-wear scar, by setting quantitative hard particles in the lubricating oil, the gears are mainly operated in the abrasive wear state, and wear monitoring and wear degree analysis are carried out for the whole life cycle of the gears. Oil samples were observed and qualitatively analyzed using a particle counter, a single ferrograph, a metallographic microscope, and a scanning electron microscope. The experiments demonstrate that the initial hard particles have a greater impact in the early wear stage of the gears (<20 h), and abrasive particle concentration increases by 30%. This means that Al2O3 particles accelerate the gear wear during the running-in period. The loading method of the impact load on the oblique gear exacerbates the abrasion particle wear and expands the stress concentration, which reduces the surface of large milling particles on the surface, and reduces the width of the tooth (the part above the pitch line is severely worn), which causes the gear to break into failure. The research provides help for analyzing the mechanism of abrasive wear of gears and predicting wear life.

摘要

本研究旨在从油液分析、振动分析、齿轮磨损量及齿面磨损轮廓分析四个方面,探究在三体磨料Al2O3(0.2 mg/mL)颗粒冲击载荷作用下大型风力发电齿轮的磨损特性及演变机制。采用磁粉制动器模拟实际工况。结合磨粒监测及齿面磨损瘢痕的形貌分析,通过在润滑油中设置定量硬颗粒,使齿轮主要处于磨粒磨损状态,对齿轮全生命周期进行磨损监测及磨损程度分析。利用颗粒计数器、单通道铁谱仪、金相显微镜及扫描电子显微镜对油样进行观察和定性分析。实验表明,初始硬颗粒在齿轮早期磨损阶段(<20 h)影响较大,磨粒浓度增加30%。这意味着Al2O3颗粒在磨合期间加速了齿轮磨损。斜齿轮上冲击载荷的加载方式加剧了磨粒磨损并扩大了应力集中,使表面大铣削颗粒减少,齿宽减小(节线以上部分磨损严重),导致齿轮断裂失效。该研究为分析齿轮磨粒磨损机理及预测磨损寿命提供了帮助。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81c1/9229044/a24e1f445a28/materials-15-04135-g010a.jpg
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本文引用的文献

1
Evaluation of Thermal Damage Impact on Microstructure and Properties of Carburized AISI 9310 Gear Steel Grade by Destructive and Non-Destructive Testing Methods.通过破坏性和非破坏性测试方法评估热损伤对渗碳AISI 9310齿轮钢微观结构和性能的影响。
Materials (Basel). 2021 Sep 14;14(18):5276. doi: 10.3390/ma14185276.
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Characterization of the microstructure and hardness of case-carburized gear steel.渗碳齿轮钢的微观结构与硬度表征
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Planetary Gear Fault Diagnosis via Feature Image Extraction Based on Multi Central Frequencies and Vibration Signal Frequency Spectrum.
基于多中频特征提取和振动信号频谱的行星齿轮故障诊断
Sensors (Basel). 2018 May 28;18(6):1735. doi: 10.3390/s18061735.
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An Intelligent Gear Fault Diagnosis Methodology Using a Complex Wavelet Enhanced Convolutional Neural Network.一种基于复小波增强卷积神经网络的智能齿轮故障诊断方法
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