A BPNN-QSTR Model for Friction-Reducing Performance of Organic Liquid Lubricants on SiC/PI Friction Pair

In this study, a systematic test of 36 organic liquid compounds as lubricants in the SiC/PI friction pair was conducted to investigate their friction-reducing performance. The back propagation neural network (BPNN) method was employed to establish a quantitative structure tribo-ability relationship (QSTR) model for the friction performance of these lubricants. The developed BPNN-QSTR model exhibited excellent fitting and predictive accuracy, withR2= 0.9700,R2(LOO) = 0.6570, andq2= 0.8606. The impact of different descriptors in the model on the friction-reducing performance of the lubricants was explored. The results provide valuable guidance for the design and optimization of lubricants in SiC/PI friction systems, contributing to the development of high-performance lubrication systems.Keywords:organic liquid lubricants;quantitative structure tribo-ability relationship;back propagation neural network;friction-reducing performance