—40
—42
—44
—46
—48
—50
—52
—54
—56
—58
1030 1035 1040
—38
x (mm)
ECA
—40
—42
—44
—46
—48
—50
—52
—54
—56
—58
1030 1035 1040
—38
x (mm)
Microscope
0
—0.1
—0.2
—0.3
—0.4
—0.5
0.1
–5
—10
—15
—20
0
5
10
Defect int.
No int.
Background int.
15
20
0.2
0.3
0.4
0.5
—40
D4
D3
D2
D1
—42
—44
—46
—48
—50
—52
—54
—56
—58
1030 1035 1040
—38
x (mm)
Cross correlation
Max val: 0.539 (normalized units)
Pos [1035.8.-48.1] (mm)
—40
—42
—44
—46
—48
—50
—52
—54
—56
—58 1030 1035 1040
—38
x (mm)
Defect and background
interaction: 75.3895%
0
0.2
0.1
0.3
0.4
0.5
0.6
Figure 10. Experimental data for Defect D4 on corrosion Sample A: (a) locations of scanned regions D1–D4 on Sample A (b) ECA scan of D4
(c) corrosion depth profile of D4 from the microscope (d) the cross-correlation map between the ECA and microscopic images (e) intersection
between the ECA and microscopic images.
0
10
A B1 B2 B3 B4
Defects per sample
C1 C2 C3 C4
20
30
40
50
60
70
80
90
100
0
10
A B1 B2 B3 B4
Defects per sample
C1 C2 C3 C4
20
30
40
50
60
70
80
90
100
D1 D2 D3 D4 Not found
Figure 11. Intersection of the ECA and microscopic images in selected regions D1–D4 on corroded steel samples: (a) full scan results (ECA image
obtained using one coil in the array) (b) fast scan results (ECA image obtained using the full array).
A P R I L 2 0 2 5 • M AT E R I A L S E V A L U AT I O N 71
y
(mm)
y
(mm)
y
(mm)
y
(mm)
Intersection
percent
(%)
Intersection
percent
(%)
V
(V)
Depth
(μm)
Normalized
units

The gray regions in Figure 10e, indicating a lack of
overlap between the ECA and microscopic datasets, result
from the inherent blurriness of the ECA image compared
to the high-resolution microscopic data. This blurriness
occurs because the ECA sensor coils integrate magnetic field
responses over their sensing area, leading to a less sharply
defined defect boundary.
Figure 11 summarizes the validation of ECA results for
all test samples with corrosion damage. The intersections
between post-processed ECA scans and corresponding micro-
scopic images were calculated for randomly selected regions
(D1–D4) in all corroded samples, including the flat Sample A
and the curved sample sets B1–B4 and C1–C4. A comparison
was also made between full scans (single-coil raster scans) and
fast scans (full-array, single-pass along the sample’s long side).
The results showed strong agreement between ECA and
microscopic images across all scans, including those on curved
surfaces, highlighting the robustness of the signal processing
approach used in this study. As shown in Figure 11a, full scans
exhibited a high degree of intersection with microscopic data,
indicating strong correlation. However, in fast scans, some
randomly selected regions (D1–D4) were not effectively covered
due to the single-pass nature of the scan, which followed the
centerline of the sample. Consequently, for these regions,
intersection evaluation was not possible. Instances where the
array probe was out of bounds relative to the microscopic
measurement regions are labeled as “not found” in Figures
11a and 11b.
Conclusions and Future Work
This work presents a robotic eddy current array (ECA) system
developed for detecting corrosion on curved metallic surfaces,
addressing key challenges such as inconsistent probe liftoff, tilt,
and environmental factors. A post-processing workflow, includ-
ing array subtraction and detrending techniques, was imple-
mented to correct these errors and improve defect detection
accuracy. The system showed promising results when tested
on samples with varying curvatures, correlating well with
ground truth data from a digital microscope. While liftoff and
tilt caused some inaccuracies, the system was able to detect
corrosion defects effectively. The integration of computer
vision–based path planning and precise sensor alignment
were essential to optimize measurements, demonstrating the
system’s potential for use in complex geometries and real-
world scenarios.
Future work will focus on improving the robotic ECA
system by addressing liftoff variations and enhancing robot
calibration. More automated and accurate surface reconstruc-
tion methods, such as iterative closest point (ICP) algorithms
or AprilTags (Olson 2011), will replace the current point-pair
picking method. Adaptive path planning will also be explored,
enabling the robot to adjust its scanning path based on real-
time ECA data, which will require further development of
voltage-to-depth calibration techniques. Additionally, the
system will be extended to mobile and aerial platforms,
enabling autonomous inspection of large-scale or remote
structures. These improvements will support NDE 4.0’s goals of
increasing automation, precision, and scalability in real-world
inspection scenarios.
DATA AVAILABILITY
A detailed description of the post-processing procedure used in this study
for the eddy current array image data is available from the corresponding
author upon reasonable request.
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