Application of a neural network model in estimation of frictional features of tribofilms derived from multiple lubricant additives.

In the field of tribology, many studies now use machine learning (ML). However, ML models have not yet been used to evaluate the relationship between the friction coefficient and the elemental distribution of a tribofilm formed from multiple lubricant additives. This study proposed the possibility of using ML to evaluate that relationship.

In-Liquid Lateral Force Microscopy of Micropatterned Surfaces in a Fatty Acid Solution under Boundary Lubrication.

This study aims to investigate the influence of surface morphology on boundary-lubricated friction in a stearic acid solution. The surface morphology was controlled by fabricating submicrometer line-and-space patterns on Si(100) surface via photolithography. The boundary-lubricated friction on the patterns was measured by in-liquid lateral force microscopy for both transverse and longitudinal ridges, with respect to the sliding direction; the highest friction was observed on longitudinal ridges and grooves, which is in agreement with the tendency observed in our previous friction studies on steel surfaces.