Ultrasonic Response Research Articles

Advancing Carbon Fiber Composite Inspection: Deep Learning-Enabled Defect Localization and Sizing via 3-D U-Net Segmentation of Ultrasonic Data.

In nondestructive evaluation (NDE), accurately characterizing defects within components relies on accurate sizing and localization to evaluate the severity or criticality of defects. This study presents for the first time a deep learning (DL) methodology using 3-D U-Net to localize and size defects in carbon fiber reinforced polymer (CFRP) composites through volumetric segmentation of ultrasonic testing (UT) data. Using a previously developed approach, synthetic training data, closely representative of experimental data, was used for the automatic generation of ground truth segmentation masks. The model's performance was compared to the conventional amplitude 6 dB drop analysis method used in the industry against ultrasonic defect responses from 40 defects fabricated in CFRP components. The results showed good agreement with the 6 dB drop method for in-plane localization and excellent through-thickness localization, with mean absolute errors (MAEs) of 0.57 and 0.08 mm, respectively. Initial sizing results consistently oversized defects with a 55% higher mean average error than the 6 dB drop method. However, when a correction factor was applied to account for variation between the experimental and synthetic domains, the final sizing accuracy resulted in a 35% reduction in MAE compared to the 6 dB drop technique. By working with volumetric ultrasonic data (as opposed to 2-D images), this approach reduces preprocessing (such as signal gating) and allows for the generation of 3-D defect masks which can be used for the generation of computer-aided design files; greatly reducing the qualification reporting burden of NDE operators.

Far-end delamination characterization of CFRP laminates using nonlinear ultrasonic spectra of scaling subtracted vibration response

The application of nonlinear ultrasonic techniques for the near-end damage characterization of carbon fiber reinforced polymers (CFRP) has proven to be successful and extensive. However, the nonlinear ultrasonic response is weak and may be overwhelmed by the linear component of signals if the damage is located at the far-end. In this paper, the scaling subtraction method (SSM) is adopted to filter out the nonlinear ultrasonic signature aimed at the better characterization of far-end delaminations in CFRP laminates. A three-dimensional finite element model is constructed first to validate the feasibility of separation effect of SSM. Then the experimental study is conducted for a delaminated CFRP plate using laser scanning Doppler vibrometer (LSDV) response measurements. Results show that the far-end delamination can be detected and localized clearly since the nonlinear features can be accurately separated from the surface vibration response by using SSM. Moreover, the far-end delamination size is qualitatively analyzed by using the maximum response amplitude (MRA) of the higher harmonics of SSM signals, and a positively correlated trend is shown as the delamination size increases. This work demonstrates the applicability and potential of SSM technique for the comparative assessment of far-end defect information in composites.

Research on multiple damage localisation based on fusion of the Lamb wave ellipse algorithm and RAPID algorithm

Current damage localisation methods often require many sensors and complex signal processing methods. This paper proposes a fusion algorithm based on elliptical localisation and the reconstruction algorithm for probabilistic inspection of damage (RAPID) to locate and image multiple damages. Experimental verification of the damage algorithm was conducted. An ultrasonic probe was used to excite Lamb signals on an aluminium alloy plate, the ultrasonic response signals at different positions within the plate under multiple damages were measured and the constructed algorithm was employed to image the damage location. In the experiment, this method improved localisation efficiency by excluding invalid sensing paths in the sensor network, saving 31.32% of computational time. When some sensors in the sensor network were damaged, this algorithm ensured a positioning accuracy with a positioning error of 5.83 mm. Finally, the algorithm was used to locate multiple damages in the sensor network and the results showed the good robustness of the algorithm.

A review of the microstructural contributions to the acoustic nonlinearity parameter measured with longitudinal and Rayleigh wave second harmonic generation in metals

Due to their sensitivities to a specimen's microstructural evolution during processes including heat treatment and plastic deformation, ultrasonic measurements of the acoustic nonlinearity parameter (β) are useful for monitoring microstructural changes and detecting incipient damage in metals. Despite its diagnostic capabilities, the entanglement of different microstructural contributions to the cumulative ultrasonic response renders the interpretation of β a nontrivial task, particularly in the absence of first-hand microstructural characterization. Here, a review of longitudinal and Rayleigh wave second harmonic generation research connecting microstructural features of metals to β is presented, organized by experiments and models of specific features such as dislocations and precipitates. Substantial attention is directed to any available microstructural characterization as it pertains to trends in β. Connections across the literature are discussed extensively, contradictions and anomalous results are highlighted, and commentary on the future directions and challenges of modern second harmonic generation research is dispersed throughout.

I Am an Earphone and I Can Hear My User’s Face: Facial Landmark Tracking Using Smart Earphones

This article presents EARFace , a system that shows the feasibility of tracking facial landmarks for 3D facial reconstruction using in-ear acoustic sensors embedded within smart earphones. This enables a number of applications in the areas of facial expression tracking, user interfaces, AR/VR applications, affective computing, and accessibility, among others. Although conventional vision-based solutions break down under poor lighting and occlusions, and also suffer from privacy concerns, earphone platforms are robust to ambient conditions while being privacy-preserving. In contrast to prior work on earable platforms that perform outer-ear sensing for facial motion tracking, EARFace shows the feasibility of completely in-ear sensing with a natural earphone form factor, thus enhancing the comfort levels of wearing. The core intuition exploited by EARFace is that the shape of the ear canal changes due to the movement of facial muscles during facial motion. EARFace tracks the changes in shape of the ear canal by measuring ultrasonic channel frequency response of the inner ear, ultimately resulting in tracking of the facial motion. A transformer-based machine learning model is designed to exploit spectral and temporal relationships in the ultrasonic channel frequency response data to predict the facial landmarks of the user with an accuracy of 1.83 mm. Using these predicted landmarks, a 3D graphical model of the face that replicates the precise facial motion of the user is then reconstructed. Domain adaptation is further performed by adapting the weights of layers using a group-wise and differential learning rate. This decreases the training overhead in EARFace . The transformer-based machine learning model runs on smart phone devices with a processing latency of 13 ms and an overall low power consumption profile. Finally, usability studies indicate higher levels of comforts of wearing EARFace ’s earphone platform in comparison with alternative form factors.

Acoustic characterization for creep behaviors of marine sandy hydrate-bearing sediment

Marine natural gas hydrate (NGH) is a promising substitutive low-carbon energy resource, whereas NGH-production induced geoengineering concerns remain challenging. Advanced forecast of possible geoengineering risks is the fundamental for eco-friendly NGH exploitation. Reservoir creep deformation is an early symptom of the geoengineering risks. However, whether the creep deformation behaviors of the NGH-bearing strata is predictable remains controversial. In this study, a series of multi-step loading creep test are conducted for sandy gas hydrate bearing sediment (GHBS) samples, during which the ultrasonic responses are recorded simultaneously. The acoustic velocity, compression-to-shear velocity ratio, Poission’s ratio, main frequency, and main frequency amplitude are used to characterize creep failures of the GHBS for the first time. Combining analyses of the creep behaviors and acoustic responses yield the following conclusions. Firstly, the long-term strength derived from creeping test is 0.45–0.60 times of the shear strength derived from triaxial shearing. Ignoring the creep effect might underestimate the scale and intensity of possible geoengineering risks during long-term NGH exploitation. Secondly, the acoustic velocity increases gently and then decreases continuously during creeping. Once the accelerated creep appears, the acoustic velocity plummets significantly, together with a sudden decrease in the compression-to-shear velocity ratio, and fluctuations in the main frequency and its amplitude. Furthermore, the main frequency and its amplitude shall fluctuate abruptly prior to the emergence of the accelerated creep. Therefore, we anticipate that the combination of abnormal fluctuations of main frequency and its amplitude can be used as early-warning indicators for possible creep failure of the GHBS. The results might have great significance for in-situ detection and prediction of possible reservoir failure during long-term NGH exploitation.

High temperature AlN thin film ultrasonic transducers with dual mode wave excitation

AlN thin film ultrasonic transducers with dual mode wave (transverse and longitudinal) excitation were deposited by reactive RF magnetron sputtering technique. The transducers deposited at radial distances of 0 and 50 mm in the sample plane exhibited well-developed straight and inclined columnar structures with dominating (002) and (103) X-ray diffraction peaks, respectively. The transducers deposited at radial distances of 0, 30, and 50 mm demonstrated excitation of longitudinal, transverse, and longitudinal-transverse waves, respectively. Short term heat treatment was conducted in air within a temperature range of 20–900 °C to investigate the high temperature ultrasonic response. Temperature dependencies of the time-of-flight and pulse-echo amplitude were analyzed. Long term heat treatment in air at 700 °C for 192 h revealed a degradation of ultrasonic response due to the oxidation of the AlN layer.

Dynamic ultrasonic response modeling and accurate state of charge estimation for lithium ion batteries under various load profiles and temperatures

Accurate estimation of the State of Charge (SoC) plays a vital role in ensuring the efficient and safe operation of lithium iron phosphate (LFP) batteries. However, the flat open circuit voltage (OCV) curve of the LFP battery implies a low sensitivity to SoC, which results in large SoC estimation errors in the presence of noisy terminal voltage measurements. To address this challenge, an SoC estimation methodology utilizing an ultrasonic reflection response model is proposed, which is the first methodology regarding highly accurate and robust ultrasonic model-based SoC estimation under dynamic load profiles. Since ultrasound waves enable non-destructive acquisition of battery internal physical property changes directly associated with SoC, the ultrasonic battery near-surface reflection feature is extracted and demonstrated to exhibit a highly linear correlation with and higher sensitivity to SoC. We pioneeringly construct an empirical differential ultrasonic model to describe how the ultrasonic feature depends on the SoC and dynamic current. The advantage of such an ultrasonic model is demonstrated by theoretical and experimental results of an Adaptive Extend Kalman Filter (AEKF) and an Adaptive H-infinity Filter (AHIF) under various dynamic load profiles and temperatures. The Root Mean Square Error (RMSE) for ultrasonic model-based SoC estimation remains at approximately 1% across all tests, reducing by 36.7% compared to the voltage model, which shows its great potential in accurate SoC estimation.

Ultrasonic measurement of temperature distributions in extreme environments: Electrical power plants testing in utility-scale steam generators

Thermal heterogeneities within energy conversion and storage, material processing, nuclear processes, aerospace, and military applications are often inaccessible to characterization by insertion sensors. When sensor deployment is possible, conventional pointwise temperature probes quickly degrade when inserted into harsh environments typical of such processes. We developed spatially-resolved ultrasonic thermometry to noninvasively measure the spatial distributions of thermal properties in such applications, even when sizable thermal gradients are present. Our method divides the path of ultrasonic propagation into segments bound by echogenic features, which create echoes in pulse-echo mode, encoding the information about interior temperature distributions. We use the acquired ultrasonic responses to estimate the internal temperature distributions by solving an inverse problem or concatenating segmental estimates. This work describes the implementation and industrial testing of the developed method at a coal-fired electrical power generation plant. We inserted an echogenically segmented Inconel 625 waveguide into the combustion zone of the utility-scale boiler and continuously acquired ultrasonic data while keeping sensitive components away from the damaging combustion environment. The accuracy of the time-dependent temperature distributions reconstructed from the ultrasonic measurements was comparable to that of thermocouples. The resiliency of ultrasonic thermometry to harsh combustion conditions was far superior to conventional insertion sensors. The measurements obtained during plant operation captured daily steam generation cycles in response to changing customer demand and intermittent contributions of renewable power sources to the power grid. These measurements have revealed new insights into the relationship between the dynamic power generation load and the conditions inside the steam generator. The successful industrial testing of spatially-resolved ultrasonic thermometry in solids indicates that the developed technology has matured to become an attractive alternative to conventional sensing in solving challenging problems of long-term thermal characterizations in extreme environments.

Phase/frequency analysis of diffuse Lamb-wave field for fatigue-crack detection in an aluminium multi-riveted strap joint aircraft panel

A diffuse Lamb-wave field is utilised for fatigue-crack detection in a multi-riveted strap-joint aircraft panel. The work involves an extensive experimental programme of fatigue testing. Ultrasonic Lamb-wave responses are captured using a network of low-profile, surface-bonded piezoceramic transducers. Various phase/frequency characteristics of Lamb waves are used to detect and monitor fatigue cracks. An outlier statistical analysis – based on these characteristics - is also performed for damage detection. The results show that phase analysis can be used successfully for damage detection in such structures. In contrast, frequency analysis does not offer reliable damage detection. The results demonstrate the potential and limitations of the method for reliable fatigue-crack detection in complex aircraft structures.

Impurity gas monitoring using ultrasonic sensing and neural networks: forward and inverse problems

Ultrasonic sensing is a non-invasive technique for monitoring impurity gas composition in various industrial applications where safety and regulatory compliance are crucial. In this study, ultrasonic sensing and neural networks were used to analyze impurity gases (i.e., air and argon) in helium. An experimental platform was established to acquire ultrasonic data. In the forward problem, an artificial neural network (ANN) model was used to forecast the response and time-of-flight (TOF) based on the excitation, and argon and air concentrations. The inverse problem was solved using a convolutional neural network (CNN) to predict the argon and air concentrations given the ultrasonic response and excitation. The results showed that the ANN accurately predicted the ultrasonic response and the change in TOF with concentration. As the air concentration was increased from 0 to 9.8%, the TOF sensitivity to detect argon decreased by 39.8% and 16.1% from ANN and sound speed theory, respectively. The CNN demonstrated high accuracy in predicting concentrations for inputs in the testing dataset. The application of the trained CNN indicated that it over-predicts air concentration while under-predicting the argon concentration. To improve accuracy, the predicted air and argon concentrations should be corrected by -0.992% and 1.027% bias, respectively.

Voice Warning to Identify Distance to Obstacles on Smart Cane for Blind People

Blind people are often hampered in their mobility, while currently available assistive technology includes manual sticks and electronic mobility devices. Currently, the development of smart canes is still being carried out in many studies. Smart cane works by detecting the distance of the cane to obstacles in front of you. From detecting this distance, the response the user gets is generally in the form of vibrations and sounds from the buzzer. The sound provided by the buzzer cannot differentiate the distance between obstacles. Therefore, this study proposes the existence of additional sound playback responses to differentiate far, middle, and near obstacles. Apart from that, the design of the prototype refers to the shape of a flashlight. The smart cane is designed using an Arduini UNO microcontroller with a rechargeable battery power supply with three response components, namely two ISD 1820 modules connected to a speaker to detect long distances (201-300 cm) with a "Hati-hati" sound and medium distances (101-200 cm) with an "Awas" sound, as well as a buzzer and piezoelectric component in the form of a "beep" sound and vibration for close distances (1-100 cm) to obstacles. The result is that both the ultrasonic sensor and response components work according to design. Apart from that, the rechargeable battery supply feature and the implementation of the prototype design make it easier for users to store and use for daily activities.

Feasibility study of pore-pressure prediction in carbonate rocks

In the recent past, the applicability and uncertainty of pore-pressure prediction methods that work based on seismic signals have been serious challenges in carbonate reservoirs. In this context, enormous research has been conducted on the prediction of pore pressure in these rocks, some of which have ended with failure due to the complexity of carbonates. To address the main cause of such failures, we experimentally simulate the pore fluid in carbonates under reservoir conditions to interpret and classify the factors contributing to the pore pressure. The results are then built into a novel hybrid experimental model for evaluating the feasibility of pore pressure prediction — one that could bridge the gap between laboratory and empirical approaches. For this purpose, 34 core samples are collected from different geologic formations at different wells penetrating reservoir intervals. The mechanical properties of the samples are investigated by conducting experiments in a triaxial cell where pore pressure is applied under different regimes while simultaneously acquiring the ultrasonic response of the sample. Sensitivity analyses of pore pressure are carried out under drained and undrained conditions and the results are used to evaluate our methodology. Pore pressure coefficients of P- and S-wave velocities are introduced in two different conditions, namely constant differential stress and constant pore pressure, with the same dimension as compressibility. Results are interpreted statistically and effective parameters are identified from a pool of many variables; these include the static bulk modulus, Young’s modulus, Biot’s coefficients, porosity, permeability, and sedimentary textures (e.g., micritization, neomorphism, cementation, fracture, blocky calcite, and dissolution). A feasibility study template is further introduced for investigating pore pressure detectability based on commonly available data in carbonate reservoirs by incorporating the main controlling factors. Our template and findings of this research are important for pore pressure prediction before performing further research and implementing operation programs. They also can decrease the risk of underestimating the pore pressure, thereby improving the well planning performance and mitigating the drilling hazards.

Machine learning framework for extracting micro-viscoelastic and micro-structural properties of compressed oral solid dosage forms

A compressed pharmaceutical oral solid dosage (OSD) form is a strongly micro-viscoelastic material composite arranged as a network of agglomerated particles due to its constituent powders and their bonding and fractural mechanical properties. An OSD product’s Critical Quality Attributes, such as disintegration, drug release (dissolution) profile, and structural strength (“hardness”), are influenced by its micro-scale properties. Ultrasonic evaluation is direct, non-destructive, rapid, and cost-effective. However, for practical process control applications, the simultaneous extraction of the micro-viscoelastic and scattering properties from a tablet’s ultrasonic response requires a unique solution to a challenging inverse mathematical wave propagation problem. While the spatial progression of a pulse traveling in a composite medium with known micro-scale properties is a straightforward computational task when its dispersion relation is known, extracting such properties from the experimentally acquired waveforms is often non-trivial. In this work, a novel Machine Learning (ML)-based micro-property extraction technique directly from waveforms, based on Multi-Output Regression models and Neural Networks, is introduced and demonstrated. Synthetic waveforms with a given set of micro-properties of virtual tablets are computationally generated to train, validate, and test the developed ML models for their effectiveness in the inverse problem of recovering specified micro-scale properties. The effectiveness of these ML models is then tested and demonstrated for a set of physical OSD tablets. The micro-viscoelastic and micro-structural properties of physical tablets with known properties have been extracted through experimentally acquired waveforms to exhibit their consistency with the generated ML-based attenuation results.

Ultrasonic Monitoring of Ash Contents and MBTS Accelerator Mixed Nitrile Rubber during the Vulcanization Process

Nitrile-butadiene rubber, commonly known as nitrile rubber, is one of the most usable synthetic rubbers that exhibits excellent heat and oil resistance. Nitrile rubber has a variety of applications, such as manufacturing of gaskets, footwear, sponges, and so on, where natural rubbers cannot be used. This paper presents a unique method of monitoring the vulcanization process in real time for nitrile rubber mixed with varied ash content (china clay and whiting) and an MBTS (benzothiazyldisulfide) accelerator using ultrasonic nondestructive evaluation (NDE). An experimental setup includes a fully automated system for acquiring ultrasonic data that has been developed in-house. It has been found that ultrasonic velocity can distinguish between china clay and whiting content in rubber, along with monitoring the curing rate and completion of curing of rubber. The cross-link density of every test sample is calculated using the equilibrium swelling method, followed by its evaluation using ultrasonic responses.

Localization and classification of scattered nonlinear ultrasonic signatures in bio-mechanical media using time reversal approach.

This paper deals with the time reversal approach along with signal classification using ϕ-divergences in biomedical applications for localization and statistical classification of ultrasonic nonlinearities. The time reversal (TR) approach in combination with nonlinear elastic wave spectroscopy (NEWS) is used to obtain the nonlinear signature of air bubbles with different sizes and ultrasound contrast agents in a liquid. An optimized chirp-coded signal in the range of 0.6-3 MHz is used as a compression coding. The signal classification is performed using the fuzzy classification method and the divergence decision tree algorithm using specific ϕ-divergence spectral measures extracted from the received ultrasonic response containing acoustic nonlinearities. The classification results prove that different types of nonlinearities extracted with classical "pulse inversion" based coding methods can be identified. Simultaneously, the different positions of scattered sources are distinguished by ϕ-divergence methods. The potential of time reversal nonlinear elastic wave spectroscopy methods for understanding of ultrasonic wave propagation in complex media is clearly exhibited.

Ultrasound-responsive copolymeric micelles prepared by RAFT polymerization using a difunctional spiropyran-based RAFT agent: Investigation of mechanochromism kinetics, responsivities, and enhanced spiropyran mechanoactivation within micelles

An in-depth investigation of the ultrasonic response of mechanophores containing weak covalent bonds within nanosized aggregates is vital for ultrasound imaging, drug delivery, and sensors. In this work, well-defined amphiphilic block SP-[poly(methyl methacrylate-b-N-isopropylacrylamide)]2SP-[p(MMA-b-NIPAM)]2 (P1) and amphiphilic random, SP-[poly(methyl methacrylate-r-N-isopropylacrylamide)]2SP-[p(MMA-r-NIPAM)]2 (P2) copolymers with SP mechanophore embedded at midpoint polymer chain are prepared by RAFT (reversible addition-fragmentation chain transfer) polymerization using a pre-synthesized novel functional bi (trithiocarbonate) and spiropyran (SP)-based RAFT reagent. Both P1 and P2 copolymers self-assemble in spherical micelles in AcCN/water mixed solvents, and several assemblies with different water contents are fabricated. The structural differentiation between block P1 and random P2 dominates the inverse influence of water content on the aggregate sizes of micelles. Under ultrasound irradiation, the SP mechanophore is activated within both micelles, and the amount of activated SP mechanophore increases progressively with prolonged sonication time. For P1, the mechanochemical reaction (mechanochromism) kinetics in an aggregated state and a single chain state are different, while that of P2 micelles and liner are similar, inferred to be associated with the structure difference between both micelles. The mechanoactivation of SP mechanophore within micelles in AcCN/water mixed solvents is higher than in dissolved state due to the micellization of P1 and P2, entangling the hydrophobic PMMA segments and partially swelling in the micellar core. Moreover, an increase in the medium's dielectric constant around the core's SP is also observed, promoting the SP mechanoactivation. This approach may provide a new strategy for preparing novel mechanophore-centered amphiphilic copolymers with colorful structures and high hydrophilicity. These can self-aggregate into nanoparticles in aqueous or mixed solutions, exhibiting effective effects of ultrasonic responsiveness. We expect this work to push forward the in-depth studies on the ultrasonic responsive mechanism of assembly and help open new research avenues into the mechanochemical reactions within nanosized aggregates.

Detection of barely visible impact damage in composite plates using non-linear pump-probe technique

In the last decades, composite materials have been increasingly used in aircraft structures as a mean to reduce the weight. For safety reasons, periodic checks of impact-induced damage need to be performed. In that framework, ultrasound non destructive testing has proven its ability to detect macro-defects such as delamination. However, at early stages, Barely Visible Impact Damage (BVID) may not be detected via linear ultrasound techniques. In recent years, non-linear ultrasound has gained traction for the inspection of such defects. However, detection of the weak non-linear defect signature buried in ambient noise remains challenging, and depends on an empirical choice of excitation frequencies. This study focuses on the evaluation of a non-linear pump probe ultrasound inspection technique applied to BVID in multilayer carbon fibre reinforced composites used in aeronautics. In particular, the influence of various experimental parameters on the measured non-linear response is studied. A low frequency pump wave (via a shaker) and a high frequency wave (via a piezoelectric transducer) are transmitted to the medium while a second transducer records the ultrasonic response. Both pump and probe waves are sent in the form of a few secondslong frequency sweep, in the audible and ultrasonic range respectively. Appropriate post processing is then applied to the recorded signals to retrieve the non-linear response of the defect. The procedure is repeated on several composite plates with and without impact damages. The nonlinear response appears in the form of modulation sidebands in the frequency spectrum of the post-processed signals. The relative amplitudes of the side bands obtained for the various samples with various defect sizes and excitation amplitude or frequency content are studied. Optimal experimental parameters were obtained and led to a good detection of defects. The limits of the method are also discussed.

Characterizing the As-Fabricated State of Additively Fabricated IN718 Using Ultrasonic Nondestructive Evaluation

This article reports on the characterization of the “as-fabricated” state of Inconel 718 samples fabricated using laser directed energy deposition (DED). Laser-DED is known to produce complex metastable microstructures that can significantly influence the baseline ultrasonic response compared to conventional processing methods. The present work uses three parameters to characterize the samples: (a) ultrasonic velocity, (b) an attenuation coefficient, and (c) a backscatter coefficient. The baseline ultrasonic response from the DED sample was compared against the ultrasonic properties of conventional IN718 samples reported in the literature. The results suggest that strong grain boundary scattering from large macrograins can lead to attenuation and backscatter values that are significantly higher than conventional samples. Additionally, the results including velocities, attenuation and backscatter coefficients were found to be dependent on the fabrication direction, with the build direction being different from the transverse directions. Finally, destructive analysis was used to develop conjectures to explain the experimentally observed ultrasonic response.

Quantifying the anisotropic elasticity of 3D printed phononic artifacts with ultrasound for process monitoring

The layer-wise additive building technique utilized by 3D Printing (3DP)/Additive Manufacturing (AM) processes results in builds and parts with strong anisotropy. Also, with 3DP/AM, the intentional creation of intricate internal geometries and regulated compositions is possible, leading to exciting opportunities for generating anisotropic structures at the material level with optimized properties and superior performance. Accurate characterization of these anisotropic properties for specific applications is essential for optimizing their design and performance, as well as monitoring their quality in real-time during production. The current study introduces a new monitoring mechanism to evaluate the anisotropic characteristics of a build by examining the ultrasonic responses of Phononic Crystal Artifacts (PCAs), which are specially designed calibration pieces that represent the geometric and mechanical complexities of the build. But the PCAs are much simpler and smaller in size, making them easier and more cost-effective to manufacture, monitor, and assess compared to the actual build. Based on the Christoffel equation, the proposed monitoring approach extracts the elastic coefficients from the ultrasonically acquired waveforms and their corresponding speeds in three principal (one build-up and two lateral) and two additional (diagonal) wave propagation directions. The printed Polylactic Acid (PLA) material exhibits anisotropy of 3.07 % to 8.91 % in the reported experiments. The extracted directional elastic parameters are determined as the directional Young's moduli E1 = E2 = 2.59 GPa, E3 = 2.22 GPa; shear modulus, G13 = 0.94 GPa, and Poisson's ratios, υ12 = 0.29, and υ13 = 0.34. The obtained elastic parameters show that the lateral directions dominate the elasticity of the printed material in the build-up direction. The presented approach for non-destructive ultrasonic characterization, which is used to investigate the directional mechanical properties, can also be beneficial in developing a real-time quality monitoring system for 3DP/AM.