Use case: Life cycle assessment of compressor impellers made of aluminum alloys

© Fraunhofer IWM
Microstructural characterization of aluminum radial compressor impellers

Radial compressor impellers, such as those used in exhaust gas turbochargers for internal combustion engines, are subject to stringent requirements regarding their structural durability. The aluminum alloy EN AW-2618A is the predominant material for these components due to its good creep and fatigue resistance. However, aging during high-temperature operation leads to a change in the microstructure, which is associated with a loss of strength and thus a reduced service life. This aging must be taken into account in the service life assessment to ensure a reliable prediction of operational safety. During aging, the metastable rod-shaped microstructure of the Al2CuMg type, which was optimized by heat treatment, changes. During operation, the larger rods grow at the expense of the smaller ones, a process known as materials aging that reduces strength.

To accurately quantify this degradation and its influence on the mechanical properties, a modified materials model is used in the finite element method (FEM). In this approach, a Chaboche model is extended to include internal variables for the rod radius and the accumulated viscoplastic strain. These parameters describe materials aging and cyclic softening and are interpolated in the model to capture the stress-strain behavior as a function of time. A cycle-jump approach enables the extrapolation of these internal variables, thereby ensuring a realistic simulation of long-term aging over the service life.

Our research and development services for predicting fatigue and the service life of aluminum components

  • Experimental characterization of alloys in various aging states with regard to fatigue and creep at operating temperatures,
  • Development and validation of advanced materials models,
  • Determination of application-specific load sets,
  • Integration of the models into finite element calculations for service life prediction.

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