Estimating antiwear properties of lubricant additives using a quantitative structure tribo-ability relationship model with back propagation neural network
To be able to predict tribological properties of new lubricant additives as well as clarify lubricating mechanisms, one needs to study the relationship between structures of lubricant additives and their lubricating properties. With a focus on estimating antiwear properties of some heterocyclic additives, we use the quantitative structure tribo-ability relationship (QSTR) model to predict tribological data, which introduces the idea of computer-aided design into tribology. This is combined with back propagation neural network (BPNN), a machine-learning method that offers simplicity and robustn...
