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A new thermal fatigue rig using High Frequency induction heating is developed to test automotive Diesel engine pistons. An adapted test piston is internally cooled by permanent water flow while its bowl sensitive to thermo-mechanical fatigue is subjected to cyclic induction heating. The temperature is measured in depth by thermocouples and in surface by a thermal infrared camera or a pyrometer. The crack initiation and propagation and the local deformations are provided by optical means. Thermo-mechanical loadings are calibrated by thermal measurements on the piston during engine operation and the entire test is modelled by finite elements. A constitutive model and a fatigue criterion for aluminium alloys are proposed to estimate the piston lifetime under severe cyclic loading. The proposed fatigue bench allows loading the piston in thermal fatigue scheme very similar to that encountered in engine operation conditions. Crack detection is facilitated by numerical modelling that helps to detect the most critical areas and also to reliably estimate the number of cycles for initiate cracks. [ABSTRACT FROM AUTHOR]

ABSTRACT The purpose of this paper is to define a thermo-mechanical fatigue criterion in order to predict the failure of aluminum alloys components issued with the lost foam casting process and used in particular in the automotive industry. The microstructure of the studied materials (A356-A319 aluminum alloys) is clearly affected by the lost foam casting process which can directly affect the mechanical properties, the damage mechanisms and the fatigue failure of specimens and components. The major problem in defining a predictive fatigue criterion in this case is the fact that it should be applicable for the component which is submitted to complex multiaxial thermo-mechanical loadings. Since many years, energy-based criteria have been used to predict fatigue failure of this class of materials. Then, different energy-based criteria are tested in order to take into account different types of triaxiality and mean stress effects corrections. The fatigue lifetime results predicted by both of them show a good agreement with experimental results. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose a novel method to establish a probabilistic fatigue lifetime prediction. [•] The method is based on an observed statistical distribution of defects and a growth law. [•] We discuss real defect observations and lifetime data for aluminum alloys and automotive applications. [•] The method provides similar match quality with observations as standard fatigue criteria. [•] The resulting probability distribution is an input for reliability methods such as the stress–strength interference method. [ABSTRACT FROM AUTHOR]