Ultrasonic metal welding (UMW) is becoming a more broadly used technology for joining ductile materials, especially in the electric vehicle sector. The process, however, lacks monitoring capabilities that would improve confidence in the repeatability of welded joints. Often only destructive testing is used for quality evaluation. This method of inspection is insufficient for a production line and can lead to high scrap rates and failure to identify all poorly welded components. The work discussed in this paper aims to close the gap of weld quality evaluation for UMW through in-process monitoring. Multiple sensors were installed in line with a linear ultrasonic metal welder. Current, voltage, frequency, shear force, and displacement of the horn both laterally and vertically were monitored throughout welding trials. Parameters for expected overweld, underweld, and acceptable weld qualities were selected through screening trials and design of experiment (DOE) methods. Twenty welds from each of these three quality sets were made with all monitoring tools for several rounds of testing. Destructive analysis was used to confirm the weld quality for each experiment. This included peel testing by hand for qualitative results and mechanical peel testing for quantifiable results, as well as metallographic analysis of the cross section and weld interface. Signal analysis, performed for each set of sensor data, extracted unique features that may be correlated to the input weld parameters and resulting weld quality. Machine learning techniques were applied on these features to classify weld quality based on in-process monitoring data. Algorithms predicted weld quality with over 90% accuracy.
Laser ultrasonic line sources have been used to study the ultrasonic properties of nuclear graphites. These materials
exhibit varying degrees of porosity and texture that relate directly to the conditions imposed during material processing-extruded materials display significant texture while the anisotropy of molded materials is significantly lower. Both the
texture (related to grain orientation) and porosity impact the long term performance of graphite under service conditions
and methods are needed to assess the microstructural states of these materials during service. Laser ultrasonic
measurements can be used to assess aspects of material microstructure by measuring longitudinal and shear wavespeeds
as a function of propagation direction and polarization. While porosity-related effects are independent of propagation
direction for materials with spherical pores, material texture (related to preferred grain orientation) produces anisotropic
wave propagation effects. In particular, propagation perpendicular to extrusion directions can produce shear wave
birefringence effects that can be used to assess texture. Ultrasonic measurements in this work were made using laser
ultrasonic methods that yield waveforms that can be interpreted using elastodynamic models for wave propagation in
anisotropic materials. In particular, models for laser ultrasonic line sources in transversely isotropic materials have been
used to simulate laser sources in nuclear graphites. The effects of optical penetration (related to material porosity) have
been incorporated to produce synthetic waveforms that can be used to extract modulus information from experimental
measurements. Current results hold open the opportunity for porosity and texture assessment using limited sets of
ultrasonic measurements.
Fatigue damage sensing and measurement in aluminum alloys is critical to estimating the residual useful lifetime of a
range of aircraft structural components. In this work, we present electrical impedance and ultrasonic measurements in
aluminum alloy 2024 that has been fatigued under high cycle conditions. While ultrasonic measurements can indicate
fatigue-induced damage through changes in stiffness, the primary indicator is ultrasonic attenuation. We have used laser
ultrasonic methods to investigate changes in ultrasonic attenuation since simultaneous measurement of longitudinal and
shear properties provides opportunities to develop classification algorithms that can estimate the degree of damage.
Electrical impedance measurements are sensitive to changes in the conductivity and permittivity of materials - both are
affected by the microstructural damage processes related to fatigue. By employing spectral analysis of impedance over a
range of frequencies, resonance peaks can be identified that directly reflect the damage state in the material. In order to
compare the impedance and ultrasonic measurements for samples subjected to tension testing, we use processing and
classification tools that are matched to the time-varying spectral nature of the measurements. Specifically, we process
the measurements to extract time-frequency features and estimate stochastic variation properties to be used in robust
classification algorithms. Results are presented for fatigue damage identification in aluminum lug joint specimens.
We investigate the use of low frequency (10-70 MHz) laser ultrasound for the detection of fatigue damage.
While high frequency ultrasonics have been utilized in earlier work, unlike contacting transducers, laser-based
techniques allow for simultaneous interrogation of the longitudinal and shear moduli of the fatigued material. The
differential attenuation changes with the degree of damage, indicating the presence of plasticity. In this paper, we
describe a structural damage identification approach based on ultrasonic sensing and time-frequency techniques.
A parsimonious representation is first constructed for the ultrasonic signals using the modified matching pursuit
decomposition (MMPD) method. This decomposition is then employed to compute projections onto the various
damage classes, and classification is performed based on the magnitude of these projections. Results are presented
for the detection of fatigue damage in Al-6061 and Al-2024 plates tested under 3-point bending.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.