Investigation of stress corrosion cracking in CMSX-4 turbine blade alloys using Deep Learning assisted X-ray microscopy and using 3D images for finite element modelling
applications due to their unique properties. Recently, incidents of failure due to increased
temperature has caused Type II hot corrosion leading to cracking in blade roots resulting in
catastrophic failure. Understanding the failure mechanism and crack characterization is vital
in solving this issue.
After exposing a salted C-ring specimen to 500°C air for 92 hours, we demonstrate a novel
high resolution X-ray microscopy (XRM) workflow using deep-learning based algorithms for
data reconstruction and segmentation, combined with scanning electron microscopy in order
to study cracks, crack tips and crack arrest points developed during stress corrosion
cracking.
By extracting the fracture tip, both crystal plasticity and crystal deformity can be studied in
detail resulting in orientation tomography of the corroded region. Using this correlative
workflow we are able to identify structural defects and fracture mechanisms not visible using
typical microscopy techniques.
Furthermore, deep learning reconstruction datasets have enabled the integration of XRM with
finite element models (FEM) to enable mapping of real-life cracks that are translated to
realistic model meshes. Computational modelling can complement experimental efforts by
providing estimations of attributes (e.g., stress) concurrent with the material characterisation
offered by deep-learning enhanced XRM We will demonstrate that the approach has been
instrumental to uni-vocally discover the damage mechanism involved in stress corrosion
cracking. We will further present an autonomous integration approach between XRM and
FEM that can be implemented concurrently with the material characterisation. The seamless
XRM-FEM exchange has the potential to control experiments based on magnitudes that are
not measured but modelled.