The bright area in Figure 4a indicates that the possible
ranges for the length and depth of the crack are 7–9 mm and
1.5–2.5 mm, respectively. The actual length and depth of the
crack introduced into TP1 are 8.6 mm and 2.2 mm, respec-
tively, which fall within this range. By contrast, the bright areas
in Figures 4b and 4c are much more elongated in the depth
direction—indicating increased uncertainty in depth estima-
tion. This is reasonable because induced eddy currents are
predominantly concentrated near the surface of the target
thus, the shallow portion of a discontinuity has a dominant
effect on eddy current signals.
It should be noted that the depths of the fatigue cracks
in TP2 and TP3 are much greater than the standard depth of
penetration. This highlights the difficulty in quantitatively eval-
uating their depths, a limitation clearly reflected in the results
shown. The reason why the red marks in Figures 4b and 4c
fall somewhat outside the bright areas is unclear. In practice,
however, it would be acceptable to overestimate the surface
length by a few millimeters.
The marginal distributions of the estimated electromag-
netic properties of the cracks are presented in Figures 5–7.
While it is difficult to analyze these results quantitatively, the
figures suggest that it is improper to model the fatigue cracks
as an air region, like artificial slits. Notably, the multimodal
characteristics observed in Figures 5a, 5b, and 6b imply non-
unique inverse solutions, where multiple combinations of
discontinuity parameters can reproduce the measured signals.
In other words, this finding challenges the conventional
single-value deterministic approach to discontinuity parameter
evaluation and underscores the need for probabilistic assess-
ment, as implemented in this study.
To confirm the necessity of considering the electromag-
netic properties of a crack, additional sizing was performed
assuming that the conductivity and relative permeability
of the discontinuity were zero and unity, respectively, and
the results are presented in Figure 8. The bright areas in
the figure are much more localized compared with those
in Figure 4. Although this implies that the results are highly
reliable, the estimated dimensions—especially the depths of
the deeper cracks—are largely different from the actual ones.
This result indicates that improper discontinuity modeling
can lead not only to an error in the sizing (Yusa et al. 2003)
but also to a significant misevaluation of the uncertainty of
the results.
1 2 3 4 5 6 7 8 9 10
μr (–)
σ/σ0 (%)
0.45
0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
0 1 2 3 4 5
0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
Figure 6. Marginal distribution of the estimated electromagnetic
properties of the fatigue crack in TP2: (a) conductivity (b) relative
permeability.
1 2 3 4 5 6 7 8 9 10
μr (–)
σ/σ0 (%)
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
0 1 2 3 4 5
0.6
0.5
0.4
0.3
0.2
0.1
0.0
Figure 7. Marginal distribution of the estimated electromagnetic
properties of the fatigue crack in TP3: (a) conductivity (b) relative
permeability.
ME
|
FATIGUECRACKS
78
M AT E R I A L S E V A L U AT I O N A U G U S T 2 0 2 5
Probability
density
(–)
Probability
density
(–)
Probability
density
(–)
Probability
density
(–)
4. Conclusion
This study probabilistically evaluated the size of fatigue cracks
in type 304 stainless steel plates using eddy current signals. The
fatigue cracks were artificially introduced using a cyclic four-
point bending test, and eddy current tests to gather the signals
were performed using a differential-type plus point probe
driven at 200 kHz. The fatigue crack was modeled as a rectan-
gular domain with a constant width of 0.5 mm and uniform
electromagnetic properties.
Because fatigue damage changes the austenitic phase of
type 304 stainless steel into the martensitic phase, both the
electrical conductivity and magnetic properties of the discon-
tinuity were explicitly considered. The depth and length of
the crack, together with their predicted uncertainty, were esti-
mated using a Bayesian-based inverse algorithm.
The size evaluated by assuming the crack was equivalent
to air differed from the actual crack size, even though the algo-
rithm indicated the results were reliable. In contrast, assuming
the electromagnetic properties to be unknown produced better
evaluations with quantified uncertainty.
ACKNOWLEDGMENTS
This work was supported in part by a Grant-in-Aid for Japan Society for
the Promotion of Science (JSPS) Fellows (grant number 22KJ0223).
REFERENCES
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10
8
6
4
2
0
9
8
7
6
5
4
3
2
1
0
0 5 10 15 20
Length (mm)
10
8
6
4
2
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6
5
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Length (mm)
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Length (mm)
Figure 8. Estimated depths and lengths of the fatigue cracks when the
cracks were modeled as an air domain (no conductivity or magnetic
property): (a) TP1 (b) TP2 (c) TP3.
A U G U S T 2 0 2 5 M AT E R I A L S E V A L U AT I O N 79
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density
(mm
–2 )
Probability
density
(mm
–2 )
Probability
density
(mm
–2 )
Depth
(mm)
Depth
(mm)
Depth
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