Artificial neural network and multi-criterion decision making approach of designing a blend of biodegradable lubricants and investigating its tribological properties

Various blends containing glycerol, castor oil (NCO) and cashew nut shell liquid (CNSL) were made following soft computational techniques and the blend consisting 60% glycerol and 40% NCO was proposed, which exhibited 37% less coefficient of friction (CoF) than NCO and CNSL and 50% less CoF and comparable extreme pressure properties to non-biodegradable commercial mineral oil (CMO). Accelerated wear was indicated by particle quantifier index for CMO, NCO and CNSL samples while normal wear was observed in glycerol and the proposed blend. SEM and 3-D profilometer images exhibited more damaged surfaces in NCO and CNSL than other lubricants. Raman spectra indicated the presence of FeOOH, OH, HOH and fatty acids on the wear tracks of the proposed blend.

Automated summary

The study found that the newly proposed blend showed a reduced coefficient of friction and comparable extreme pressure performance to commercial mineral oil. Compared to other lubricants, the proposed blend exhibited less wear and surface damage.