Numerical parameter sensitivity analysis of residual stresses induced by deep rolling for a 34CrNiMo6 steel railway axle

Abstract

To optimise the benefits of the deep-rolling process in the service life context of treated components, the process application must be investigated. In addition to the reduction in surface roughness and near-surface material strengthening, compressive residual stresses are introduced, which are primarily responsible for the increase in service life for components, especially in the case of high-strength steel materials. A numerical parameter sensitivity analysis is performed in order to investigate the introduced residual stresses in detail. For this purpose, a validated deep-rolling simulation model is used, which replicates the deep rolling of a railway axle made of the high-strength steel material 34CrNiMo6. The model is based on an elastic-plastic Chaboche material model parameterised on uniaxial tensile and LCF test results and validated with residual stress measurements. Using this model as a basis, the effect of the main process parameters deep-rolling force, feed rate, friction coefficient, number of overruns, tool geometry, and shaft geometry on the resulting residual stress state are investigated. The results reveal that the deep-rolling force has the most significant influence on the introduced residual stress state and should therefore be highlighted. In the case of applying a deep-rolling force of more than 10 kN, maximum compressive residual stresses of around − 1000 MPa are introduced, and a strong saturating behaviour is shown. Maximum compensating tensile residual stresses of + 100 MPa occur below the surface. The main influence of the deep-rolling force is the effective depth achieved, which is determined by the depth of the zero crossing. This varies from 1 mm with an applied force of 2 kN to more than 3.5 mm with 20 kN. Furthermore, the results are analysed to conclude suggestions for the process’s applicability, and a proposal for an optimised deep-rolling treatment is presented. There multiple deep rolling with decreased deep-rolling forces is used to achieve a comparably optimised residual stress state. In summary, with the presented results, a contribution to a deeper understanding of the deep-rolling process can be achieved; the influence of the most important process parameters on the residual stress in-depth profiles is established; an optimisation proposal is presented; and correlations are found. Thus, the base work for further fatigue strength assessments and the optimisation of the deep-rolling process regarding the increase of service is laid.