In general, formed components are lightweight as well as highly economic and resource efficient. However, forming-induced ductile damage, which particularly affects the formation and growth of pores, has not been considered in the design of components so far. Therefore, an evaluation of forming-induced ductile damage would enable an improved design and take better advantage of the lightweight nature as it affects the static and dynamic mechanical material properties. To quantify the amount, morphology and distribution of the pores, advanced scanning electron microscopy (SEM) methods such as scanning transmission electron microscopy (STEM) and electron channeling contrast imaging (ECCI) were used. Image segmentation using a deep learning algorithm was applied to reproducibly separate the pores from inclusions such as manganese sulfide inclusions. This was achieved via layer-by-layer ablation of the case-hardened steel 16MnCrS5 (DIN 1.7139, AISI/SAE 5115) with a focused ion beam (FIB). The resulting images were reconstructed in a 3D model to gain a mechanism-based understanding beyond the previous 2D investigations.

This research provides a method for a reliable fatigue life estimation at high testing frequencies. The investigations are based on the lifetime prediction method StressLife_HCF considering test frequencies of 80 and 260 Hz for normalized SAE 1045 (C45E, 1.1191) steel. Therefore, load increase tests and constant amplitude tests were carried out using a resonant testing rig. To ensure a mechanism-oriented lifetime prediction, the material response to dynamic loading is monitored via temperature and electrical resistance measurements. Due to the higher energy input per time unit, when the test frequency is increased, the heat dissipation also increases. For this reason, a precise differentiation between frequency- and temperature-related effects for adequate fatigue assessment is challenging. To evaluate the temperature’s influence on electrical resistance, an electrical resistance-temperature hysteresis is measured, and the frequency influence is analyzed by considering cyclic deformation curves. In addition to an extension of the fatigue life due to an increased test frequency, the lifetime prediction method was validated for high frequencies. The generated S-N curves show a reliable agreement with the data points from conventional constant amplitude tests. In this context, the temperature correction of the electrical resistance proved to be an important input variable for a reliable lifetime prediction.