Recent researchers widely used nanoparticle additives for improving thermal and rheological properties of machine lubricant. In present study the effect of Al₂O₃ and CeO₂ nanoparticles on transmission oil (SAE30), hydraulic oil (HYDREX100) and gear oil (EP90) of heavy earth moving machinery is investigated. Nano-lubricant samples are prepared in 0.01–4% nanoparticle volume fraction range. Four machine learning techniques namely decision tree (DT), random forest (RF), generalized linear models and neural network (NN) have been used to predict the kinematic viscosity for Al₂O₃ and CeO₂ nanolubricants. Further, multi-criteria decision-making technique named technique for order of preference by similarity to ideal solution have been used to find the best predictive method in each category of the nanolubricants. DT, RF and NN methods are found to be most accurate in kinematic viscosity prediction of transmission oil (R² = 0.861), hydraulic oil (R² = 0.971) and gear oil (R² = 0.973), respectively.

最近的研究人员广泛使用纳米颗粒添加剂来改善机械润滑剂的热和流变性。在本研究中，研究了Al ₂ O ₃和CeO ₂纳米颗粒对重型土方机械的传动油（SAE30），液压油（HYDREX100）和齿轮油（EP90）的影响。制备纳米润滑剂样品的纳米颗粒体积分数范围为0.01–4％。四种机器学习技术，即决策树（DT），随机森林（RF），广义线性模型和神经网络（NN）已用于预测Al ₂ O ₃和CeO ₂的运动粘度纳米润滑剂。此外，已采用多标准决策技术，即通过与理想解决方案的相似性来确定优先顺序的技术，以在每种纳米润滑剂中找到最佳的预测方法。发现DT，RF和NN方法分别在变速箱油（R ²  = 0.861），液压油（R ²  = 0.971）和齿轮油（R ²  = 0.973）的运动粘度预测中最准确。