Discover the SciOpen Platform and Achieve Your Research Goals with Ease.
Search articles, authors, keywords, DOl and etc.
This study employs a stacking ensemble learning framework to establish a regression model for predicting the tribological properties of amide-based lubricating grease and determining the optimal additive ratios. Melamine cyanuric acid (MCA) was selected as the thickener, and three extreme-pressure anti-wear additives were used to prepare the lubricating grease. The tribological performance was tested using an MFT-R4000 reciprocating friction and wear machine. Based on the tribological experimental data, the synthetic minority oversampling technique (SMOTE) was utilized for data augmentation, and a stacking ensemble algorithm with Bayesian optimization of hyperparameters was used to construct a predictive model for tribological performance. Subsequently, within this model framework, single and multi-objective optimization models were developed, and the fruit fly algorithm was employed to find the optimal additive combination ratios, which were experimentally validated. The results demonstrated that the learning framework based on the stacking ensemble model could effectively predict the tribological properties of amide-based lubricating grease in small sample datasets, with the R2 for the average friction coefficient prediction reaching 0.9939 and for the wear scar width prediction reaching 0.9535. In the experimental validation of the optimal additive ratios, the relative error of the friction coefficient ratio scheme was 0.51%, and the relative error of the wear scar width was 1.10%. This finding suggests that the learning framework provides a novel approach for predicting the performance of amide-based lubricating grease and studying additive combinations.
Xia Y Q, Yang K, Feng X, Wang Y H. Tribological properties of modified Kaolin doped polymer as polytetrafluoroethylene grease additive. Tribol Int 173: 107612 (2022)
Chen C, Liu Y J, Tang Q, Xu H, Tang M X, Li X K, Liu L, Dong J X. Tribological and rheological performance of lithium grease with poly-α-olefin and alkyl-tetralin as base oils. Chin J Chem Eng 56: 180–192 (2023)
Yang K, Xia Y Q, Zhang Y, Chen W H, Feng X. Insights into the tribological properties and electrical conductivity of Cu–C coating under grease lubrication. Tribol Lett 72(2): 64 (2024)
Ren G L, Sun X W, Li W, Li H, Zhang L, Fan X Q, Li D S, Zhu M H. Improving the lubrication and anti-corrosion performance of polyurea grease via ingredient optimization. Friction 9(5): 1077–1097 (2021)
Manu M, Aravind J, Sanal Mohammed B, Reby Roy K E, Mubarak A M, Shaik U. Optimization of tribological characteristics in cryo-treated plastic/graphene oxide modified CFRP via ANN-based predictive modeling for aerospace applications. Compos Sci Technol 250: 110520 (2024)
Chen G J, Jiang S, Huang Y F, Wang X R, Chai C P. Design of ternary solid lubricants SiO2/Ti3C2/PTFE for wear-resistant, self-lubricating polyimide composites. J Taiwan Inst Chem E 157: 105429 (2024)
Wang G M, Wu Y Y, Jiang H F, Zhang Y J, Quan J R, Huang F C. Physical and chemical indexes of synthetic base oils based on a wavelet neural network and genetic algorithm. Ind Lubr Tribol 72(1): 116–121 (2019)
Yahya S I, Aghel B. Estimation of kinematic viscosity of biodiesel-diesel blends: Comparison among accuracy of intelligent and empirical paradigms. Renew Energy 177: 318–326 (2021)
Yu T, Yin P, Zhang W, Song Y L, Zhang X. A compounding-model comprising back propagation neural network and genetic algorithm for performance prediction of bio-based lubricant blending with functional additives. Ind Lubr Tribol 73(2): 246–252 (2021)
Wen G, Liu W M, Wen X L, Wei P, Cao H, Bai P P, Tian Y. Effective tribological performance-oriented concentration optimization of lubricant additives based on a machine learning approach. Tribol Int 197: 109770 (2024)
Feng X, Yang R, Xie P Y, Xia Y Q. Prediction of lubricant physicochemical properties based on Gaussian Copula data expansion. China Pet Process Pe 26(1): 161–174 (2024)
Feng X, Xia Y Q, Xie P Y, Li X H. Classification and spectrum optimization method of grease based on infrared spectrum. Friction 12(6): 1154–1164 (2024)
Zhang J P, Wang L L, Wang G D. Prediction and comparative analysis of friction material properties using a GA-SVM optimization model. Ind Lubr Tribol 76(3): 345–355 (2024)
Ning H F, Chen F Q, Su Y F, Li H B, Fan H Z, Song J J, Zhang Y S, Hu L T. Modeling and prediction of tribological properties of copper/aluminum-graphite self-lubricating composites using machine learning algorithms. Friction 12(6): 1322–1340 (2024)
Altay O, Gurgenc T, Ulas M, Özel C. Prediction of wear loss quantities of Ferro-alloy coating using different machine learning algorithms. Friction 8(1): 107–114 (2020)
Shi H F, Li H L, Guo Z Y, Lu H Y, Wang J, Li J S. GNBoost-based ensemble machine learning for predicting tribological properties of liquid-crystal lubricants. Langmuir 40(20): 10705–10717 (2024)
Kumar S, Singh K S K, Singh K K. Data-driven modeling for predicting tribo-performance of graphene-incorporated glass-fabric reinforced epoxy composites using machine learning algorithms. Polym Compos 43(9): 6599–6610 (2022)
Xu Y H, Meng R T, Zhao X. Research on a gas concentration prediction algorithm based on stacking. Sensors 21(5): 1597 (2021)
Cao Y S, Liu G, Luo D H, Bavirisetti D P, Xiao G. Multi-timescale photovoltaic power forecasting using an improved Stacking ensemble algorithm based LSTM–Informer model. Energy 283: 128669 (2023)
Pan T T, Zhao J H, Wu W, Yang J. Learning imbalanced datasets based on SMOTE and Gaussian distribution. Inf Sci 512: 1214–1233 (2020)
Han Y M, Du Z L, Hu X, Li Y Q, Cai D, Fan J Z, Geng Z Q. Production prediction modeling of food waste anaerobic digestion for resources saving based on SMOTE–LSTM. Appl Energy 352: 122024 (2023)
Zhang W G, Wu C Z, Zhong H Y, Li Y Q, Wang L. Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization. Geosci Front 12(1): 469–477 (2021)
Victoria A H, Maragatham G. Automatic tuning of hyperparameters using Bayesian optimization. Evol Syst 12(1): 217–223 (2021)
Anmala J, Turuganti V. Comparison of the performance of decision tree (DT) algorithms and extreme learning machine (ELM) model in the prediction of water quality of the Upper Green River watershed. Water Environ Res 93(11): 2360–2373 (2021)
Cai Z B, Li C L, You L, Chen X D, He L P, Cao Z Q, Zhang Z N. Prediction of contact resistance of electrical contact wear using different machine learning algorithms. Friction 12(6): 1250–1271 (2024)
Li Z S, Li J Q, An B, Li R. The design method for surface texture of sliding friction pairs based on machine learning under mixed lubrication. Tribol Int 194: 109563 (2024)
Yin N, Xing Z G, He K, Zhang Z N. Tribo-informatics approaches in tribology research: A review. Friction 11(1): 596 (2023)
Cheng G L, Xiang C, Guo F, Wen X H, Jia X H. Prediction of the tribological properties of a polymer surface in a wide temperature range using machine learning algorithm based on friction noise. Tribol Int 180: 108213 (2023)
Zhang K, Chen M S, Xu X, Yen G G. Multi-objective evolution strategy for multimodal multi-objective optimization. Appl Soft Comput 101: 107004 (2021)
Baghoolizadeh M, Pirmoradian M, Sajadi S M, Salahshour S, Baghaei S. Prediction and extensive analysis of MWCNT–MgO/oil SAE 50 hybrid nano-lubricant rheology utilizing machine learning and genetic algorithms to find ideal attributes. Tribol Int 195: 109582 (2024)
Yuan X F, Dai X S, Zhao J Y, He Q. On a novel multi-swarm fruit fly optimization algorithm and its application. Appl Math Comput 233: 260–271 (2014)
Shen L M, Chen H L, Yu Z, Kang W C, Zhang B Y, Li H Z, Yang B, Liu D Y. Evolving support vector machines using fruit fly optimization for medical data classification. Knowl Based Syst 96: 61–75 (2016)
364
Views
38
Downloads
1
Crossref
0
Web of Science
0
Scopus
0
CSCD
Altmetrics
This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0, http://creativecommons.org/licenses/by/4.0/).