The asymmetric material flow, severe plastic deformation and thermal cycle imposed on the base material during friction stir welding (FSW) result in unique microstructural development, which causes a gradient in local mechanical properties in the weld region. Micro-tensile and indentation testing were applied to determine the local mechanical properties in a friction stir welded joint. The local stress-strain curves exhibited a drastic change at the advancing side (AS) due to a steep gradient of mechanical properties. Finite Element Model (FEM) predictions of the tensile performance of the welded joints, based on the local mechanical properties measured by micro-tensile testing, were in very good agreement with the macro-tensile test data.

Residual stresses superimpose the stress field from an indentation experiment and do therefore influence the measurement of the volume of interest. The residual stress state can be very different in magnitude and biaxiality and error that may be caused in the measured hardness is difficult to estimate. A prediction of the pressure ratio for stressed and unstressed material is carried out by new model that accounts for nonlinearities caused by the von Mises flow rule. The model can also be used for the correction of the effect of a general residual stress state before further analysis of the measured indentation data towards the underlying mechanical properties, such as the stress-strain behaviour. The model does further allow estimating the measurement uncertainty when the stress ratio or the strength of the material is unknown, which is a typical scenario for hardness testing in welded steels and aluminium alloys.

The anisotropic fracture of the 2024-T351 aluminium alloy is investigated using a micromechanics-based damage model accounting for the effect of the void aspect ratio and

On characterisation of local stress strain properties in friction stir welded aluminium AA 5083 sheets using micro-tensile specimen testing and instrumented indentation technique.

In the present work, a previously developed neural network approach for analyzing spherical indentation experiments is applied to prestressed specimens to determine the effect of residual stresses on the identified stress–strain curves. Within this scope, a comparison to other measurement errors has been made, which are caused by surface preparation and anisotropy of the material. To validate the experimental and analysis approach, the effect of compressive and tensile prestresses was also simulated using a three-dimensional finite element model. The material investigated is a rolled 2024 T351, which is widely used for manufacturing airplanes. It is shown that the existing neural network approach is able to determine the stress–strain behavior in agreement with that obtained from tensile tests. The method is robust against most error sources, such as surface roughness, coarse grain structure, and anisotropy, if a sufficient number of experiments are available. The most important influencing factor can be the residual stress causing errors up to 20% in the identified stress–strain curves.