Rolling contact fatigue modelling and life prediction for aeroengine bearing steels

Main-shaft bearings are vital safety components in aeroengines made of bearing steels with outstanding combinational properties. The material is supposed to exhibit high surface hardness，high fracture toughness，high temperature resistance，fatigue resistance and corrosion resistance. However，under harsh operation conditions bearing steels are prone to rolling contact fatigue（RCF）that leads to bearing failure，severely endangering flight. Therefore，accurately predicting the RCF lives of bearing steels is key to the reliability of aeroengine. This manuscript reviews important results and progress in the RCF and life prediction of aeroengine bearing steels and suggests future research directions. Firstly，special stress state as a result of the Hertzian contact between rolling element and raceway is introduced，where shear stress components peak at the subsurface.This explains the origin of complexsubsurface-originated RCF mechanisms in bearing steels and indicates subsurface-originated RCF to be an important failure mode of bearing steels under ideal conditions. Meanwhile，with increasing contact pressure，the response of material evolves from elastic mode to plastic mode. Besides，due to the harsh service environment of aero-engine bearings，surface-originated also takes place and hence the competition between these two mechanisms is present. Next，three methodologies for RCF life prediction are summarized，being probabilistic models，mechanistic models and numerical models，with their advantages and limitations analyzed. Probabilistic models are well developed and widely employed by industry，but they are in nature a kind of statistical models without accounting for RCF mechanisms and are hence lacking scientificity; Deterministic models predict RCF life via describing physical processes，which are rich in science but poor in accuracy due to their simplification; numerical models balancing both engineering practice and scientific characteristic is a powerful tool to tackle the problem of RCF life prediction，although their accuracy requires further improvement. Finally，it is suggested that future research may focus on solving key scientific problems in RCF，modifying life prediction models via inserting RCF mechanisms and applying artificial intelligence in RCF life prediction.