ABSTR ACT
Eddy current inspection is a critical method for
assessing the health of metallic structures, but it
requires precise probe placement to avoid signal
variations caused by inconsistent liftoff. Robotic systems
enable scanning of complex geometries, maintaining
stable liftoff and ensuring accurate data collection.
This paper presents a robotic eddy current array (ECA)
inspection system that operates without CAD models,
using computer vision to reconstruct the part’s surface
for path planning. Inaccuracies in robot calibration and
the reconstructed mesh can disrupt the probe’s precise
positioning, especially in ECA scanning, where probe
tilting increases liftoff variability, particularly at greater
distances from the tool’s center. To address these
issues, we introduce a signal processing technique that
reduces the impact of mesh inaccuracies and liftoff
fluctuations on the acquired ECA data. The system is
validated on curved steel samples with corrosion pits,
approximately 50 µm deep and ranging from 1 to
10 mm² in area. The results demonstrate the system’s
effectiveness in detecting defects and its potential for
integration into the NDE 4.0 framework.
KEYWORDS: robotic NDE, eddy current array, corrosion,
computer vision, part surface reconstruction, NDE 4.0
Introduction
NDE 4.0 is emerging with advancements in technology for
managing and processing large datasets, which are crucial
for verifying artificial intelligence (AI) systems that inter-
pret these datasets to determine and predict damage (Savin
et al. 2024). Various types of NDE robotic systems are being
considered for NDE 4.0, including robotic arms and mobile
platforms. Recently, several advancements have been made
in autonomous scanning of complex-shaped objects using
robotic arm platforms. Imaging and reconstruction methods,
such as hyperspectral cameras for carbon fiber solutions
(Yan et al. 2022) and X-ray for reference-free 3D scanning of
defects (Kang et al. 2022 Herl et al. 2020), have been utilized
in these systems. In the realm of robotic NDE data acquisi-
tion, advanced systems using in-process freeform scanning
via ultrasonic testing—synchronizing NDE acquisition with
path planning—have been reported (Mineo et al. 2022).
Additionally, a freeform robotic arm platform for ultrasonic
inspection of complex-shaped carbon fiber samples was
recently developed (Hamilton et al. 2024).
Historically, techniques such as laser-based profilome-
try (Doyle 1995), ultrasonic profiling (Zhen et al. 2018), and
physical touch-probes (Lee and Cho 2012) have been employed
to assess true surface geometry and adapt scans accordingly.
These methods have proven effective over decades, provid-
ing precise surface location data for applications requiring
stringent geometric fidelity. However, these approaches often
involve additional hardware complexity, require contact or
near-contact with the surface, or are less suited for large-scale
automation in NDE 4.0 applications. An example of merging
robotic systems with AI technology is the use of gripper
robotics for inspection, employing electromagnetic acoustic
transducers (EMAT) scanning and deep learning for pipe
assessment (Hespeler et al. 2024). Computer vision (CV) offers
a noncontact, scalable, and hardware-efficient alternative for
capturing and reconstructing part geometries, particularly for
complex or variable surfaces. This makes it more adaptable to
modern NDE frameworks, such as NDE 4.0, where automation,
flexibility, and integration with AI-driven systems are essential.
Recent developments in eddy current array (ECA) robotic
systems have introduced several innovative approaches. Many
of these systems utilize a rotary axis, allowing the robot to
move the ECA probe along a rotating cylindrical sample. One
such system was used for inspecting nuclear assets (Foster
ADDRESSING CHALLENGES FOR
AUTONOMOUS ROBOTIC FREEFORM EDDY
CURRENT INSPECTION VIA COMPUTER
VISION ON COMPLEX GEOMETRIES
CIARON HAMILTON†‡, OLEKSII KARPENKO†‡‡, MAHMOOD HAQ††§§, AND YIMING DENG*†§
ME
|
TECHPAPER
*Corresponding author: dengyimi@egr.msu.edu
† Department of Electrical and Computer Engineering, Michigan State
University, East Lansing, MI 48824
†† Department of Civil and Environmental Engineering, Michigan State
University, East Lansing, MI 48824
‡ ORCID: 0000-0001-9582-4843
‡‡ ORCID: 0000-0003-1146-3177
§ ORCID: 0000-0001-5958-3683
§§ ORCID: 0000-0003-2537-6015
Materials Evaluation 83 (4): 62–73
https://doi.org/10.32548/2025.me-04483
©2025 American Society for Nondestructive Testing
62
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et al. 2022). Another approach involves a robotic system with
flex probes designed to inspect complex structures however,
this system is limited to movement along a single axis (Zhang
et al. 2020). In a more advanced example, an aircraft wing was
inspected using a CAD model for path planning (Morozov
et al. 2018). However, this method often requires reverse engi-
neering the complex structure to obtain the CAD model,
which can be time-consuming and resource intensive. Mobile
systems have also seen innovation, with repair and crawler
robots developed for inspecting power plant boilers (Shi
et al. 2021). Additionally, quadruped robotic platforms have
been introduced, offering diverse inspection capabilities for
energy-producing infrastructure (Tsenis et al. 2024). For in-line
pipe inspections, endoscopic laser profiling combined with
machine learning techniques has been used to detect deforma-
tions within pipes (Mukherjee et al. 2022).
Freeform robotic inspection methods allow for autonomous
surface scanning across a wide range of components, including
those with curved or complex geometries. By using a reconstruc-
tion device, path planning can be performed on a mesh within
a virtual environment, eliminating the need for a CAD model.
This approach supports robust scanning procedures without
the need for reverse engineering components for evaluation.
Additionally, it facilitates the alignment of the reconstructed
scanning environment with the automated surface scanning
system. With these models, a scanning path can be gener-
ated, simulated, and then implemented on the physical robot.
However, challenges arise when precision scanning is required
for specific NDE methods and applications.
In this work, sub-millimeter depth corrosion damage on
curved steel samples is detected and characterized using eddy
current testing (ECT). ECT requires maintaining a consis-
tent liftoff between the probe and the conducting surface. A
small liftoff is essential to generate sufficient eddy currents in
the test sample, ensuring a high signal-to-noise ratio (SNR).
Furthermore, maintaining a constant liftoff is crucial for min-
imizing data variability, which can introduce uncertainties
and complicate data processing. These challenges are ampli-
fied when inspecting small corrosion damage, as the ECT
probe must be finely calibrated for high-frequency precision
scanning. The tilt of the ECT sensor can also reduce defect
detectability, as the magnetic field is affected by the sensor’s
angle during field inspections. The worst-case scenario occurs
when a collision happens due to inadequate inspection
path planning for complex-shaped test samples, potentially
damaging the probe immediately or through scraping along
the rough or irregular surface.
Optimal NDE path planning can be developed in a virtual
environment before being applied in physical space. However,
discrepancies between the virtual model and the test specimen
can lead to improper probe orientation relative to the scanned
surface. For instance, if the model is not correctly aligned with
the physical specimen, it can cause variations in probe rotation
and liftoff throughout the scanning path. Additionally, geomet-
ric differences may arise between the virtual reconstruction
and the actual part. While reconstruction devices like struc-
tured light provide sub-millimeter accuracy, errors can accu-
mulate for larger specimens. CAD models may also fail to
account for small geometry variations introduced during
manufacturing. Finally, errors in calibrating or mastering the
robotic joints can result in misalignments in Cartesian space,
affecting the positional accuracy of the scan.
This work addresses key challenges and demonstrates the
capabilities of robotic ECT using array probes for surface cor-
rosion characterization on curved steel samples. The robotic
system presented here features an ECA probe that enables
rapid scanning and provides expanded surface coverage
through multiple coils or channels. It also employs CV-based
path planning to perform inspections without relying on CAD
models, representing a significant advancement in automation
and digitization within NDE 4.0. Furthermore, we introduce
the integration of robotic inspection with real-time data pro-
cessing techniques, such as detrending algorithms and syn-
chronization error mitigation, ensuring consistent detection of
surface corrosion as shallow as 50 µm, despite challenges such
as liftoff variations and probe orientation errors.
Materials and Methods
This section outlines the methodology for preparing curved
corrosion samples with simulated damage and details the
experimental setup for robotic ECA inspection.
Robotic Setup for NDE Using ECA and Computer Vision
A block diagram of the robotic ECA system is shown in
Figure 1. The system consists of an industrial robotic arm with
6 degrees of freedom (DOF). Communication between the PC
and the robot controller is established via C#, with movement
PC
Robot controller
Eddy current
controller
Robot arm
Structured light
reconstruction device
Robot tool
Scanning environment
Sample
Background
Calibration marks
Calibration laser 500 kHz 32-coil
eddy current array
Laser DAQ
Figure 1. Layout of
the autonomous
robotic eddy
current array
(ECA) system with
computer vision.
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