Page 77 - MSAM-1-1

Materials Science in Additive Manufacturing
Figure 1. Schematic summary of the workflow to build-up S-P linkage for the machining behavior in microstructures.
Figure 2. Machining experiment set up.
Figure 3. Machining feed direction.
Volume 1 Issue 1 (2022)
A ML model for AM PSP of Ti64
2.2. Material structure data extraction
2.2.1. SEM data extraction
To develop a robust PSP model, it is critical to statistically
represent grain morphology that can accurately represent
AM material heterogeneity. In this study, a large number
of microstructures (e.g., 200 SEM images per material
surface) were captured. The rationale for this data-intensive
approach is to capture a statistically representative set
of local material structures for a given material surface.
Previous research has shown that the representative volume
element (RVE) represents a range where the material
properties would not be sensitive to the bulk material
properties. Przybyla and Mcdowell (2012) indicated that
smaller statistical volume elements (SVEs) that capture
material properties could be used to achieve a feasible
computational cost and time . The volume requirement
[29]
of SVEs is effective in achieving the key features of a
given grain morphology. To achieve high efficiency and
low computational cost, SVE sets were collected from all
material samples in this study.
To statistically represent quantitative descriptors of
each microstructure, low-order spatial correlations such as
2-point correlation functions can be developed to capture
the structural variability. In this study, 2-point correlation
functions f(m,m’ |r) represent the conditional probability
density of finding the same phase features m and m’ at
the head and tail of a vector r randomly placed in the
microstructure and is formally expressed as:
)
) (
)
'
f ( , m m ' |r = 1 ∫ ( pm x pm x r dx (I)
,
, +
( )
Vol Ω Ω
5 https://doi.org/10.18063/msam.v1i1.6