基于灰狼算法优化GRNN的润滑油摩擦磨损性能预测  

Prediction of Friction and Wear Performance of Lubricating Oil Based on GRNN Optimized by GWO

作　　者：夏延秋 王春丽 冯欣[1] 蔡美荣[2] XIA Yanqiu;WANG Chunli;FENG Xin;CAI Meirong(School of Energy Power and Mechanical Engineering,North China Electric Power University,Beijing 102206,China;State Key Laboratory of Solid Lubrication,Lanzhou Institute of Chemical Physics,Chinese Academy of Sciences,Gansu Lanzhou 730000,China)

机构地区：[1]华北电力大学能源动力与机械工程学院,北京102206 [2]中国科学院兰州化学物理研究所固体润滑国家重点实验室,甘肃兰州730000

出　　处：《摩擦学学报》2023年第8期947-955,共9页Tribology

基　　金：国家重点研究与发展计划(2018YFB0703802);固体润滑国家重点实验室开放课题(LSL-1814)资助.

摘　　要：针对齿轮油极压抗磨添加剂的复配问题,提出基于灰狼算法优化的广义回归神经网络(GWO-GRNN)摩擦学性能参数优化模型.选用齿轮油常用的硫化异丁烯(T321)、磷酸三甲酚酯(T306)、异辛基酸性硫磷脂十八胺(T308)和二烷基二硫代氨基甲酸钼(MoDTC)这4种材料为添加剂,设计正交试验制备齿轮油并使用MFT-R4000往复摩擦磨损试验机测试其摩擦学性能,分别建立平均摩擦系数和磨损体积性能预测模型并对模型参数进行优化,提高模型预测的准确性,采用留出法和留一交叉验证法评估模型在数据集上的泛化能力,降低模型过拟合的风险.研究结果表明:在引入灰狼算法(GWO)优化广义回归神经网络(GRNN)的平滑参数σ后,预测模型的决定系数R2得到明显提升,GWO-GRNN平均摩擦系数预测模型的R2达到96%,磨损体积预测模型的R2达到91%;表明该模型能够在小样本情况下较为准确预测出齿轮油极压抗磨添加剂的摩擦学性能,为齿轮油极压抗磨添加剂的复配研究提供了新方法.Aiming at the compounding problem of extreme pressure and anti-wear additives in gear oil,a prediction model of lubricating oil friction and wear performance based on Generalized Regression Neural Network optimized by Grey Wolf Optimizer was proposed.The four materials sulfurized isobutylene(T321),tricresyl phosphate(T306),isooctyl acid thiophospholipid octadecylamine(T308)and molybdenum dialkyl dithiocarbamate(MoDTC)commonly used in gear oil were selected as additives.Design the experimental scheme by orthogonal experiment method,add additives to base oil PAO at different mass fractions to prepare lubricating oil samples to be tested.Use MFT-R4000 reciprocating friction and wear tester to test the tribological properties of the mixed oil sample on the steel-steel friction pair.The Generalized Regression Neural Network proposed by American scholar Specht is composed of four parts:input layer,parameter layer,summation layer and output layer,it had strong nonlinear mapping ability,high fault tolerance and robustness,and had good prediction effect on nonlinear problems and small sample datasets,at the same time,compared with Radial Basis Function neural network and Back Propagation neural network,it had more advantages in computing speed and learning ability,and was widely used in prediction,control,system identification and other fields.By establishing a GRNN model,the effects of different ratios of the four additives on the mean friction coefficient and wear volume performance were predicted,and a Support Vector Machine model was established to compare and analyze the accuracy of the prediction results of the Generalized Regression Neural Network model.Grey Wolf Optimizer is a swarm intelligence optimization algorithm inspired by the predatory activities of gray wolves in nature.Compared with other intelligent optimization algorithms,this algorithm has obvious advantages such as simple operation,few parameters,and easy programming.Therefore,Grey Wolf Optimizer is used to optimize the spread value of Generalized Re

关 键 词：添加剂 摩擦磨损 灰狼算法 广义回归神经网络 润滑性能预测 

分 类 号：TH117.1[机械工程—机械设计及理论]