Measurement of Residual Stress Trends in Metal Additively Manufactured Components

The residual stress produced during additive manufacturing (AM) is not well characterized. Broad adoption of AM in critical components requires assessment of the component performance including the effects of residual stress. The objective of this work is to provide typical residual stress distributions in AM components. Laser powder bed fusion (L-PBF) and laser directed energy deposition (L-DED) of 300 series stainless steel are considered. Bulk, through-thickness, and near surface residual stress in AM components are measured using contour and hole-drilling methods. Residual stress distributions for open sections, closed sections, bridge structures, and parts with internal bores are reported. Replicate samples are fabricated and the near surface and bulk residual stress repeatability in the samples is assessed. Typically, large tensile residual stress, near the AM material yield strength, are found on the exterior surfaces, and lower levels of compression are found on the interior. Near surface residual stresses are generally highly tensile with their peak value just below the surface. Closed sections enable compressive residual stress on the interior surfaces of parts. While on the baseplate, bridge structures and wall-like structures often contain a net “bending moment,” which is counteracted by opposite bending in the baseplate. In AM components, measuring residual stress is challenged by the large tensile residual stress, which is shown to cause a plasticity effect during measurement. Despite plasticity effects, the mechanical measurements indicate the investigated AM process is repeatable and produces components with self-similar residual stress. The bulk and near surface residual stress are found to be repeatable in symmetric sections of the geometry within the same component, and the reproducibility between components is on the same scale. The outcomes of this work can be used in residual stress engineering efforts including process modeling, forecasting performance, and developing strategies to produce preferred residual stress for component performance.