journal article·2014-12

Microstresses and crack formation in AlSi7MgCu and AlSi17Cu4 alloys for engine components

M. Schöbel, G. Baumgartner, S. Gerth, J. Bernardi, M. Hofmann

Acta Materialia· Volume 81
Publisher page

Abstract

Abstract The increasing demand for light weight and efficiency of modern combustion engines requires sophisticated light alloys with improved high-temperature strength and creep resistance. Nowadays AlSi alloys are used for structural parts as these materials offer increased wear resistance and long-term stability under operating conditions. The heterogeneous microstructure of AlSi combines the thermal properties of stiff Si particles with ductile α-Al into a composite with sophisticated thermomechanical strength. However, the different Young’s moduli and coefficients of thermal expansion cause large microstress gradients and microcrack formation in service. The micromechanical deformation mechanisms in AlSi systems, responsible for crack initiation and growth, are unknown so far. The current work describes an experimental approach that combines non-destructive diffraction and imaging techniques to investigate the elastoplastic deformation behavior of two engineering alloys, i.e. the hypoeutectic AlSi7MgCu standard alloy for castings and the hypereutectic AlSi17Cu4 piston alloy for improved surface wear resistance. Neutron diffraction is applied for microstress measurements under external load and during thermal cycling. Complementary synchrotron tomography is performed for imaging of microcrack formation and damage within the microstructure. Stress-induced micromechanical deformation mechanisms could be revealed and correlated to damage mechanisms critical for engine components under operating conditions.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Aluminum Alloy Microstructure Properties · Aluminum Alloys Composites Properties · Microstructure and mechanical properties

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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