Using artificial intelligence to predict the tribology behavior of MoS2-Al2O3 hybrid nanofluid

doi:10.1088/2051-672X/ad2056

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	Using artificial intelligence to predict the tribology behavior of MoS2-Al2O3 hybrid nanofluid
	He, Jiaqi 1; Li, Huijian 2; Tang, Huajie 3; Guo, Zihan 4
	2024-03-01
发表期刊	SURFACE TOPOGRAPHY-METROLOGY AND PROPERTIES
ISSN	2051-672X
卷号	12 期号:1
摘要	Artificial intelligence algorithms including two artificial neural network and two machine learning algorithms were employed to predict the four-ball tribology behavior of MoS2-Al2O3 hybrid nanofluid. MoS2-Al2O3 composite nanoparticles were synthesized using solvothermal method and then dispersed in water-based fluids. 27 groups of tribology tests were conducted according to Box-Behnken experimental design were set as the training groups. The input variables (velocity of friction pairs, test force, test temperature, nanoparticle concentration) and output parameters (friction coefficient, wear scar diameter, wear surface roughness) were selected as the main variables. It was found that the random forest (RF) had better predict accuracy and stability for the four-ball tribology behavior of MoS2-Al2O3 nanofluid than multilayer perceptron (MLP), back propagation (BP) and k-nearest neighbors (KNN) algorithms. Besides, Pearson correlation analysis was carried out to reveal the relationship between input and output as well as different output variables. Through in-depth characterization of worn surface, a tribofilm in the thickness of 15 similar to 20 nm composed of amorphous phases, ultra-fine nanoparticles and iron compounds was found. Finally, the lubrication mechanism of MoS2-Al2O3 nanofluid were discussed based on analyzing the tribology behavior data and tribofilm structure. Through the above findings, we hope to promote the application and development of artificial intelligence techniques in lubricants design and performance evaluation in the future.
关键词	nanofluid LUBRICATION PROPERTIES lubricant NANOCOMPOSITES tribology behavior NANOPARTICLES artificial intelligence FRICTION performance prediction WEAR
其他关键词	LUBRICATION PROPERTIES ; NANOPARTICLES
DOI	10.1088/2051-672X/ad2056
收录类别	SCIE
语种	英语
WOS研究方向	Engineering ; Instruments & Instrumentation ; Materials Science
WOS类目	Engineering, Mechanical ; Instruments & Instrumentation ; Materials Science, Multidisciplinary
WOS记录号	WOS:001152796600001
出版者	IOP Publishing Ltd
原始文献类型	Article
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.library.ouchn.edu.cn/handle/39V7QQFX/168759
专题	国家开放大学
通讯作者	He, Jiaqi
作者单位	1.Open Univ China, Beijing 100039, Peoples R China; 2.Shandong Hispeed Energy Dev Co Ltd, Jinan 250014, Peoples R China; 3.Tsinghua Univ, State Key Lab Tribol, Beijing 100084, Peoples R China; 4.Beihang Univ, Sch Comp Sci & Engn, Beijing 100191, Peoples R China
推荐引用方式 GB/T 7714	He, Jiaqi,Li, Huijian,Tang, Huajie,et al. Using artificial intelligence to predict the tribology behavior of MoS2-Al2O3 hybrid nanofluid[J]. SURFACE TOPOGRAPHY-METROLOGY AND PROPERTIES,2024,12(1).
APA	He, Jiaqi,Li, Huijian,Tang, Huajie,&Guo, Zihan.(2024).Using artificial intelligence to predict the tribology behavior of MoS2-Al2O3 hybrid nanofluid.SURFACE TOPOGRAPHY-METROLOGY AND PROPERTIES,12(1).
MLA	He, Jiaqi,et al."Using artificial intelligence to predict the tribology behavior of MoS2-Al2O3 hybrid nanofluid".SURFACE TOPOGRAPHY-METROLOGY AND PROPERTIES 12.1(2024).