Abstract

Abstract It is nowadays widely recognized that good and profound knowledge of the deformation behavior and/or crack propagation behavior is a vital part of high performance simulation for advanced high stress fatigue life prediction. Concerning of those two different approaches, one must, however, be aware that additional and detailed information of fatigue induced crack nucleation, its further propagation path and the associated crack growth rate is important for the intrinsic understanding of the material damage process. This paper investigates how the fatigue process works in high stressed Al–Si–Mg and Al–Si–Cu fatigue samples starting from crack nucleation to sample rupture by rotating bending loads at room temperature. The samples themselves were taken directly from serial casted and heat treated cylinder head components and were processed to conventional hourglass specimens. Because no suitable test method which fulfils all the necessary test requirements was commercially available, a new rotating bending testing machine which directly operates in a confocal light microscope was developed. This test method allowed us to easily compare every change of the crack growth rate (especially for microstructural small cracks) with the microstructure and its crack path. At high stress regimes eutectic phases became very important for fatigue induced crack nucleation and early crack propagation. In the first third of the lifetime, both alloys cracked on multiple spots by interface cracking between hard phase particles (β-phase) and eutectic matrix. Depending on the crack length and stress intensity factor Δ K proceeding cracks were also able to move into the dentritic matrix (α-phase). At the end of the lifetime only a fraction of all nucleated cracks of each specimen became large enough to exert influence for fatigue failure. Furthermore, using this test method as an integral element of stress gradient fatigue investigation makes it possible to identify and define major microstructural crack relevant objects like particles during material fatigue. Hence, it not only increases the knowledge for high performance fatigue simulation but also provides a tool for material optimization by fatigue processes themselves.

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