Purpose

The amount, type and addition conditions of additives of lubricants should be continuously adjusted to obtain appealing performance. To obtain the optimal pretreatment parameters and reduce the cost of time-consuming experiments, the purpose of this paper is to establish an optimal back propagation neural network (BPNN) model combined with genetic algorithm (GA) in this work.

Design/methodology/approach

Using trimethylolpropane trioleate as the base oil and three types of phosphorus compounds as additives, 25 sets of lubricant formulas were designed regarding lubricant performances of average friction coefficient, average spot diameter, disk wear volume and extreme pressure. The data set was used for training and learning of BPNN and then combined with GA to optimize BPNN with continuously optimization by adjusting various parameters.

Findings

Comparing prediction data of BPNN with actual test data, correlation coefficients were above 90%, indicating that the model could accurately predict the performance of lubricants. When combined with GA, all performance errors were less than 5%, indicating that BPNN could be optimized by GA to obtain an accurate combined model for prediction of lubricant performance. The best additive formula with excellent performances was obtained from the BPNN–GA model.

Originality/value

This work developed a new method to study lubricant compounding. The combined model was expected to provide a theoretical basis and guidance for the compounding optimization of lubricant additives with high efficiency and low cost and to expand the scope to practical applications.

Peer review

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2020-0165/

中文翻译：

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包含反向传播神经网络和遗传算法的复合模型，用于预测生物基润滑油与功能性添加剂的混合性能

目的

润滑剂添加剂的量，类型和添加条件应不断调整以获得令人印象深刻的性能。为了获得最佳的预处理参数并减少费时的实验成本，本文的目的是建立结合遗传算法（GA）的最佳反向传播神经网络（BPNN）模型。

设计/方法/方法

以三羟甲基丙烷三油酸酯为基础油，以三种磷化合物为添加剂，针对平均摩擦系数，平均点径，圆盘磨损量和极压下的润滑性能设计了25套润滑配方。该数据集用于BPNN的训练和学习，然后与GA结合使用，通过调整各种参数进行连续优化，以优化BPNN。

发现

将BPNN的预测数据与实际测试数据进行比较，相关系数均在90％以上，表明该模型可以准确预测润滑油的性能。当与GA结合使用时，所有性能误差均小于5％，这表明GA可以优化BPNN以获得用于预测润滑油性能的准确组合模型。从BPNN-GA模型获得了性能优异的最佳添加剂配方。

创意/价值

这项工作开发了一种研究润滑剂混合的新方法。该组合模型有望为高效，低成本的润滑油添加剂配方优化提供理论依据和指导，并将其扩展到实际应用范围。

同行评审

本文的同行评审历史记录可在以下网址获得：https://publons.com/publon/10.1108/ILT-05-2020-0165/