Fuel, Vol.241, 733-743, 2019
Computational intelligence-based design of lubricant with vegetable oil blend and various nano friction modifiers
Biodegradable lubricant based on the blend of various vegetable oils with different nano friction modifier in combination is designed using computational intelligence technique and experimentally tested. A database is developed from the published literature on vegetable oil-based biodegradable lubricants with nano friction modifier as additives. The vegetable oils considered are coconut oil, castor oil and palm oil, whereas the friction modifiers taken into account are multi-walled carbon nanotubes and graphene. The database is used to develop artificial neural network models for predicting anti-wear properties of the lubricant expressed in terms of coefficient of friction measured through four-ball tester and pin-on-disk technique. The neural network model is used for data analytics to increase the understanding of such lubricant systems using simulation studies. Here the anti-wear effect of the vegetable oils and friction modifiers individually and also in combination is studied. The developed models are used for design optimization using a genetic algorithm. The optimal solutions are analyzed to study the role of various constituents for achieving superior performance of the lubricant. The developed lubricant is experimentally tested using four-ball tester and pin-on-disk techniques.