PATENTSROUNDUP
|
SCANNER
DETECTING DEFECTS IN RAILROADS
The rapid development of high-speed and heavy-haul railways motivates the
need for advanced railroad inspection techniques for real-time defect detection.
This month, we discuss nondestructive evaluation (NDE) technologies relevant for
detecting defects in railroads.
US9752993B1 NONDESTRUCTIVE
EVALUATION OF
RAILROAD RAILS,
WHEELS, AND AXLES
(Jeffrey G. Thompson, John R. Hull,
Morteza Safai, Barry A. Fetzer, Gary E.
Georgeson, and Steven K. Brady)
This patent describes NDE of railroads that
includes a carriage and plurality of wheels.
A source of vibration is connected to trans-
mitted vibrations at preselected frequen-
cies to inspect test regions of a wheel. An
infrared detector records temperature
changes due to the vibrations. A controller
is utilized to actuate the infrared detector
and record the thermal images of the
vibrations impacting the test regions, and
then store the thermal images recorded
by the infrared detector. The NDE system
may be mounted on a movable vehicle so
that the railroad rails may be tested on a
continuous basis, thus reducing the time
and expense for NDE of long railroad rails.
WO2004035368A1 TRANSVERSE CRACK
DETECTION IN
RAIL HEAD USING
LOW-FREQUENCY
EDDY CURRENTS
(Gopichand Katragadda, Douglas Earnest,
Gregory Anthony Garcia, and Richard Paul Reiff)
This patent describes a low-frequency
electromagnetic NDE approach to detect
transverse cracks in railroads. While
horizontal cracks can be detected by
conventional NDE approaches, transverse
cracks are difficult to detect below the
horizontal cracks. Here, the inventors utilize
a Hall element eddy current sensor to
distinguish low-frequency signatures from
nonrelevant indications such as thermite
welds, rail, and joints. The novel sensor
utilizes a toroidal-shaped DC magnet to
generate a saturated magnetic field into and
across the railhead, inductively coupling the
opposing pole ends of the magnet of the
sensor. The sensor follows the wear pattern
of the railroad and controls the liftoff of
the probe using a transporter that moves
along the track. A protective material on the
probe protects the probe from the defect
as it is moved along the railroad.
PATENTS EDITOR
Saptarshi Mukherjee, PhD: Lawrence
Livermore National Laboratory,
mukherjee5@llnl.gov
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J A N U A R Y 2 0 2 4 • M A T E R I A L S E V A L U A T I O N 21
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SCANNER
|
RNDEABSTRACTS
UTILIZING AN ULTRASONIC
INSPECTION SYSTEM
OPERATING INSIDE AN
AUTOCLAVE AND MACHINE
LEARNING TO QUANTIFY
POROSITY WITHIN
COMPOSITES DURING CURE
Tyler B. Hudson, Gavin R. Chung, Joseph J.
Pinakidis, Patrick J. Follis, Thammaia
Sreekantamurthy, and Frank L. Palmieri
Composite materials are increasingly being utilized
in aerospace applications for their high stiffness,
strength-to-weight ratios, and fatigue resistance.
However, defects in the composite may arise during
cure (e.g., porosity, delamination, fiber waviness),
and current technology only allows for post-cure
evaluation (e.g., microscopy, ultrasonic inspection).
A high-temperature ultrasonic scanning system was
developed for deployment in an autoclave, which
can detect porosity in composites during the cure
process. This study focused on the implementation
of machine learning techniques to help generate
a model that can quantify porosity, in addition to
detection and localization that has previously been
demonstrated. Two 6 h long experiments were
conducted on curing of 762 × 305 mm (30 × 12 in.)
composite panels with a [0/45/90/–45]4s layup
and varying regions of high and low pressure due
to its tapered geometry in contact with a flat caul
plate. The first experiment utilized a thick (12.7 mm)
caul plate and the second utilized a thin (3.2 mm)
caul plate. During experimentation, within the
scan area (406 × 13 mm), data was recorded and
stored for ultrasonic amplitude. Additional variables
were measured or predicted including tempera-
ture, autoclave pressure, number of plies, slope of
the composite panel surface with respect to the
transducer, viscosity, and glass transition tempera-
ture. The preprocessed data was entered into the
Regression Learner Application in MATLAB® and a
rational quadratic Gaussian process regression was
chosen for the machine learning algorithm. The
model was then trained on a larger dataset to make
it more robust and capable of predictions using a
function callout. The result was a machine learning
algorithm that can reliably quantify porosity in a
composite panel during cure based on measured
amplitude response and generate images for
intuitive visualization. This tool can be further
trained with more experimentation and potentially
employed for real-time porosity detection and quan-
tification of composite components during cure in
an autoclave. Practical use of this technology is the
potential to dynamically control processing parame-
ters (e.g., autoclave pressure) in real time to reduce
the level of porosity within the laminate to accept-
able limits (e.g., 2% by volume).
KEYWORDS: defect detection inspection during
cure machine learning (ML) porosity process
monitoring ultrasonic testing (UT)
https://doi.org/10.1080/09349847.2023.2277424
COMPARISON OF
IN-SITU NONDESTRUCTIVE
TESTING AND EX-SITU
METHODS IN ADDITIVE
MANUFACTURED
SPECIMENS FOR INTERNAL
FEATURE DETECTION
Youssef AbouelNour and Nikhil Gupta
The effectiveness and repeatability of additive
manufacturing (AM) technologies has been often
measured through rigorous testing, both in situ
and ex situ. In situ nondestructive testing (NDT) has
been used to understand the AM process and detect
anomalies during the build. Ex situ testing has
been used for material characterization and internal
defect detection. In this work, in situ NDT and ex situ
testing methods are compared as applied to a fused
filament fabrication (FFF) process to detect defects
that have formed internally in the part. Intentionally
embedded features and defects are monitored
and analyzed in situ and ex situ. Results from an
automated image-based real-time defect detection
system, an ultrasound ex situ system, and a tensile
testing system are compared to detect defects and
their effects on mechanical properties. Real-time
image-based defect detection is found to effectively
spot defects as they form and allow the potential for
implementing defect correction methods, while ex
situ tensile testing can be used as a form of valida-
tion to in situ results.
KEYWORDS: additive manufacturing in situ
monitoring defect detection optical imaging
thermal imaging ultrasound
https://doi.org/10.1080/09349847.2023.2280650
EVALUATION OF DEFECT
DEPTH OF X70 PIPELINE
STEEL BASED ON THE
PIEZOMAGNETIC SIGNALS
Sheng Bao and Pengfei Jin
In this research, the correlation between the defect
depth and the tangential piezomagnetic field of X70
steel was investigated. Tensile tests were carried out
to measure the piezomagnetic fields on the surface
of X70 specimens with different defect depths. The
variations of piezomagnetic fields were compared
to the simulation results of a theoretical model. The
influences of loads and defect depths on magnetic
parameters were discussed. This research demon-
strates the potential possibility of evaluating the
defect depth of ferromagnetic steels using piezom-
agnetic fields.
KEYWORDS: piezomagnetic field defect depth
magnetic parameters pipeline steel tangential
component
https://doi.org/10.1080/09349847.2023.2283512
RNDE Abstracts presents papers
recently accepted to ASNT’s journal
Research in Nondestructive
Evaluation. To subscribe to RNDE
visit asnt.org/rnde.
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M A T E R I A L S E V A L U A T I O N • J A N U A R Y 2 0 2 4
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