The present doctoral thesis focusses on the development of a computational assessment of fatigue strength of induction hardened steel components. An essential basis of this scientific work is represented by the extensive characterisation of materials into two different prepared microstructure conditions out of surface hardened automotive crankshafts. The database is formed by the experimental tests of the forging steel 1.1303. In order to verify the local process-dependent static and fatigue strength, specimens are extracted from complex automotive parts and adapted to study base material and martensitic properties which represents the induction-hardened surface. The idea behind those investigations is to observe material samples exhibiting a comparably minor residual stress condition in both hardened and unhardened condition to separate the cause variables residual stress, mean stress sensitivity and strength of two material phases on fatigue. This leads to a depth-dependent fatigue strength diagram according to HAIGH corrected by residual stresses, which is the fundament of the present thesis. Furthermore, the experimental test results were combined for inductive hardening process simulation in order to receive a compressive residual stress state within the heat affected zone based on finite element analysis. The described methods were merged into computational fatigue strength assessment of an automotive crankshaft part with induction-hardened bearing areas. The final evaluation compared to common fatigue strength assessments based on technological benefit factors enables a more reliable approach in order to facilitate lightweight potential. Hence, the present thesis provides a simulation chain for fatigue strength assessment of surfaced hardened components based on the transformation of there microstructure in heat affected areas.