Effect of insitu texture formed by removing spherical graphite on tribological properties of ductile cast iron

石墨 摩擦学 材料科学 铸铁 纹理(宇宙学) 冶金 球墨铸铁 复合材料 计算机科学 图像(数学) 人工智能
作者
Wei Yuan,Nannan Wang,Qianjian Guo,Wenhua Wang,Baotao Chi,Angang Yan,Jie Yu
出处
期刊:Industrial Lubrication and Tribology [Emerald Publishing Limited]
卷期号:77 (4): 526-537
标识
DOI:10.1108/ilt-09-2024-0340
摘要

Purpose The high-load operation of the engine crankshaft causes severe wear and fatigue. This study aims to prepare in situ textures with effective density and study their wear mechanism on the surface of ductile cast iron, which optimizes the tribological properties of engine crankshafts and reduces wear. Design/methodology/approach A new method was proposed based on the hardness difference in graphite removal to form an in situ texture. The friction performance was evaluated using a combination of computational fluid dynamics and tribological testings. The influence of the texture characteristic parameters on the bearing capacity of the oil film was analyzed. The surface wear morphology was studied by scanning electron microscopy. Findings The texture density significantly affected the oil film bearing capacity. The surface texture can reduce the average friction coefficient (COF) by more than 35% owing to the oil film bearing and storage capacity. Specifically, the 13% texture density exhibited the lowest wear rate and COF under all three experimental conditions. The reduction in abrasive particles in the wear area of the textured surface indicates that the surface texture can improve the lubrication mechanism. Originality/value This study systematically explored the influence of the weight of each model parameter on tribological properties. Subsequently, focusing on the critical parameter (texture density), detailed tribological testings were carried out to reveal the specific effect of texture density on the wear mechanism under different working conditions, and the optimal texture density to achieve the optimal tribological performance was determined accordingly.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
kkscanl完成签到 ,获得积分10
4秒前
mmmaosheng完成签到,获得积分10
5秒前
淡淡依霜完成签到 ,获得积分10
6秒前
咖啡不加糖完成签到,获得积分10
7秒前
薄雪草完成签到,获得积分10
11秒前
心无杂念完成签到 ,获得积分10
12秒前
科研通AI2S应助Zoi采纳,获得10
14秒前
小猴子完成签到 ,获得积分10
16秒前
16秒前
健忘的溪灵完成签到 ,获得积分10
17秒前
20秒前
健脊护柱完成签到 ,获得积分10
22秒前
又壮了完成签到 ,获得积分10
22秒前
蓝天发布了新的文献求助10
23秒前
妞妞发布了新的文献求助10
25秒前
都要多喝水完成签到,获得积分10
26秒前
26秒前
poly完成签到,获得积分10
27秒前
香菜冲冲冲完成签到 ,获得积分10
27秒前
不秃燃的小老弟完成签到 ,获得积分10
31秒前
Zoi发布了新的文献求助10
31秒前
唐糖糖完成签到 ,获得积分10
31秒前
善良的樱完成签到 ,获得积分10
39秒前
热心市民完成签到 ,获得积分10
40秒前
doctor_loong完成签到 ,获得积分10
43秒前
whywhy发布了新的文献求助20
44秒前
小甜甜的美好完成签到 ,获得积分10
48秒前
sdbz001完成签到,获得积分0
49秒前
晚风完成签到,获得积分10
52秒前
熊熊阁发布了新的文献求助10
52秒前
东都哈士奇完成签到,获得积分10
53秒前
zy完成签到,获得积分10
56秒前
BinSir完成签到 ,获得积分10
1分钟前
mix完成签到 ,获得积分10
1分钟前
CC完成签到 ,获得积分10
1分钟前
whywhy完成签到,获得积分10
1分钟前
在水一方应助科研通管家采纳,获得10
1分钟前
DKX完成签到 ,获得积分10
1分钟前
Yivano完成签到 ,获得积分10
1分钟前
1分钟前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
The Composition and Relative Chronology of Dynasties 16 and 17 in Egypt 1500
Picture this! Including first nations fiction picture books in school library collections 1500
Signals, Systems, and Signal Processing 610
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
17α-Methyltestosterone Immersion Induces Sex Reversal in Female Mandarin Fish (Siniperca Chuatsi) 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6366871
求助须知:如何正确求助?哪些是违规求助? 8180654
关于积分的说明 17247081
捐赠科研通 5421639
什么是DOI,文献DOI怎么找? 2868595
邀请新用户注册赠送积分活动 1845686
关于科研通互助平台的介绍 1693175