Raiyan Seede (1) (2), Kyle Johnson (1), Philip J. Noell (1)
Fatigue and Fracture of Engineering Materials and Structures. Volume 46, Issue 3, pages 886-894. (March 2023). DOI: https://doi.org/10.1111/ffe.13904

Keywords

Aluminum 6061, damage initiation, machine learning, void nucleation, x-ray computed tomography

Abstract

Damage initiation and localization in uniaxial tension tests of an Al6061-T6 alloy are evaluated and quantified using in situ X-ray computed tomography and a neural network segmentation strategy. This segmentation strategy offers significant advantages over traditional thresholding in quantifying microstructural and damage evolution metrics. Void nucleation was observed to occur throughout the deformation process, whereas void volume did not grow significantly until later stages of deformation. Localized analysis determined that void nucleation begins in and around the necked region during the early stages of deformation, whereas limited damage nucleation occurs outside of the neck until late stages of deformation. Lastly, inhomogeneities in void and particle spacing in the initial undeformed state of the material are observed to correlate well with the location of damage localization and necking during deformation. These results provide new insights into the ductile failure of Al alloys and how pre existing defects influence damage and rupture.

How Our Software Was Used

Data segmentation was conducted in Dragonfly's Segmentation Wizard, utilizing a neural network model originally purposed for sequential segmentation of organs in three dimensions (Sensor 3D). Trained models, one for each strain step, were then used to segment the entire 3D dataset and void statistics were extracted from the segmented data.

Author Affiliation

(1) Material, Physical, and Chemical Sciences, Sandia National Laboratories, Albuquerque, New Mexico, USA
(2) Materials Science Division, Lawrence Livermore National Laboratory, Livermore, California, USA