Fatigue test results of surface hardened components to evaluate a two layer approach for strength assessment

In the automotive industry, forged steels are commonly used for powertrain components such as gears, axles or crankshafts. In order to minimize wear and increase fatigue strength, these mechanical engineering parts are usually surface-hardened. The surface heat treatment leads to a significant change of the local material properties in the heat effected zone of the surface area. This paper addresses an effective two-layer model for evaluating fatigue strength of surface hardened components based on local stresses. Hence, one layer represents the induction-hardened surface and the second characterizes the base material. The aim of this elaborated method is a more reliable computational estimation of fatigue life among other assessments based on technological benefit factors for components designed for the high cycle fatigue (HCF) regime by taking into account local material properties, defects and residual stresses. In order to verify the presented method and to determine the local manufacturing process-dependent fatigue strength, specimens are extracted from highly stressed component areas considering forged grain structure. The sample notch shape represents typical notch types in mechanical engineering parts regarding form factor, stress gradient and highly stressed volume. The idea behind the fatigue tests is to study material samples exhibiting a comparably minor residual stress condition in both hardened and unhardened condition to separate the cause variables residual stresses, strength of base material and martensitic phase on fatigue strength. Compared to the unhardened base material, the fatigue tests at different stress ratios revealed higher fatigue strength within low and high cycle fatigue of the martensitic material. Considering these input data in combination with the fatigue strength of each material section, the layer approach enables a more reliable local fatigue assessment among other fatigue evaluation methods based on technological benefit factors.