David Abram Jack

Additive manufacturing (AM) deposits material layer-by-layer, allowing for more geometric freedom than traditional manufacturing methods and reducing waste material. One barrier to industrially using AM components for structural applications is the ability to quantify defects and qualify components post-manufacture. This paper aims to demonstrate a feature identification and geometric quantification method from multiple ultrasonic waves captured in a raster pattern for AM components with multiple closely packed, similarly shaped features extruded on a component’s interior surface. This approach’s uniqueness is a means to quantify different feature dimensions, specifically the hexagonal longitudinal diagonal and extrusion height, within a dense field of features using a semi-automated image processing method. This approach is demonstrated on stainless steel components manufactured via the fused filament fabrication process utilizing a polymer carrier that is removed during post-processing. Results are validated by characterizing the as-manufactured geometric dimensions utilizing optical microscopy for features with nominal long diagonals of 4.06 mm and nominal extrusion heights ranging from 1 mm to 2 mm. The presented image processing technique has a planar measurement quantification error ranging from −0.1 mm to −0.7 mm and a feature extrusion height quantification error ranging from −0.30 mm to 1.24 mm across a range of sample thicknesses from 2 mm to 7 mm.

This study presents an automated ultrasonic inspection method for detecting and quantifying interface defects in honeycomb composite laminate sandwich panels. Using immersion-based pulse-echo ultrasonic testing with full RF-waveform capture, ultrasonic data was acquired from Carbon Fiber Reinforced Polymer (CFRP) panels with a Nomex honeycomb core. Typical interface defects are created, such as missing adhesive, embedded Kapton and polytetrafluorethylene (PTFE) films, and missing core sections, with defects ranging in size from 4.7 mm to 40.8 mm. Two automation strategies to quantify the planar dimensions of the defect were developed: the first using the energy of the interface reflection and the second utilizing an effective facesheet thickness measurement based on the front and back wall reflections. Defect areas were extracted through image thresholding, and their shape was extracted and quantified via a custom algorithm implemented in MATLAB. Results show that the interface energy based detection method (EBD) achieved a consistently higher accuracy, with defect sizing errors typically below 1 mm with a maximum of 1.9 mm over all specimens investigated. In contrast, the thickness-based detection method (TBM) provided excellent accuracy for Kapton inserts (errors less than 0.65 mm) but exhibited larger deviations, up to 4.2 mm, for holes and PTFE defects due to a poor backwall signal. Results are presented that suggest that the backwall echo can be used for defect identification type. The automated techniques for dimensionalization demonstrated good performance across a wide defect size range, with a reliable detection and quantification of features as small as 4.7 mm and provide a reliable alternative to manual signal analysis.

Skin breakdown occurs commonly within patients with lower-limb prostheses where the current clinical practice to diagnose skin breakdown is visual inspection by a clinician. Thermal imaging and optical coherence tomography angiography (OCT-A) have been proposed as early detection methods for skin breakdown and hold great promise. OCT-A is a noninvasive imaging modality that outputs a near-infrared optical signal and captures the reflective depth signal. The recorded transient behavior of the vessel area density (VAD), after repetitive dermal loading conditions, has been correlated to skin breakdown. Thermal imaging tracks the temperature profile of the skin surface after repetitive loading conditions and utilizes the temperature time-to-peak (TTP) as a metric of dermal health like VAD. Both methods are sensitive to imaging artifacts, and current resolutions are presented in this work, along with a discussion of the need to standardize data collection and advance image processing methods. Current approaches typically rely on thresholding that produces varying results based on user-selected limits followed by smoothing without any basis of the underlying physics. This review paper presents a need for a constitutive expression that captures the expected physics, thereby strengthening confidence in the use of either thermography or OCT-A, and enabling true clinical applications to aid patient health.

Fiber-reinforced laminates composed of a thermoset matrix have seen widespread use in industries such as the aerospace, wind power, and automotive industries, due to their strength-to-weight ratios and ease of formability. For optimal performance, the instantaneous cure state must be sufficient such that the component will not deform during or after molding, a state that can vary based on many manufacturing-related factors. Thus, monitoring the cure process non-destructively in situ is key to manufacturing composite laminates to achieve the as-designed properties while balancing the cycle time reduction. The current work presents a pulse-echo ultrasound method to correlate the acoustic waveform to the thermoset resin cure state and the instantaneous structural properties, specifically the resin storage and loss moduli. This latter information provides a fabricator knowledge of when a part can be successfully demolded, allowing for optimizing part cycle times. The present paper provides the results for the neat resin specimen and fiberglass specimen impregnated with the same resin system. The results provide a direct correlation between the acoustic and the viscoelastic properties. Interestingly, it is noted that there is a direct correlation between the peak signal attenuation and the peak gelation of the material, thus providing a means to predictively schedule the demolding time while maintaining proper curing cycles.

This study investigates ultrasonic wave propagation in carbon fiber reinforced polymer (CFRP) composites containing foreign object debris (FOD) by introducing a novel method to characterize the depth and size of FOD, from a single captured waveform generated by an out-of-focus spherically focused transducer. Current methods of inspection utilize a raster approach to both detect and quantify FOD, which is limited to identifying FOD smaller than 4 mm. The method introduced in the present paper allows for a single point scan to detect and quantify FOD, as small as 0.5 mm, with the highest error in the depth estimation being less than 8%. This paper presents experimental testing to inform a finite element analysis of a full waveform simulation of an immersion tank inspection environment and compares waveforms between testing and simulation. A transient pressure acoustic model is developed in the COMSOL Multiphysics environment to simulate wave propagations. Simulation results provide waveform reflection and transmission at material interfaces, which will occur when there is an acoustic mismatch between materials. The transmitted ultrasonic wave is partially reflected toward the transducer upon encountering material interfaces between the water, CFRP laminate, and the FOD. Simulation results show that the acoustic profile and pressure of the reflected wave captured by the transducer allows an accurate identification of FOD depth and size within the composite structure, suggesting an alternative method of inspection to quantify FOD characteristics faster than through conventional approaches. Results show an increase in captured signal pressure of over 125% between the 0.5 mm FOD and the 1 mm FOD located on the mid-plane of the laminate, and 500% between the same 0.5 mm FOD and 1 mm FOD placed near the front wall. These results suggest the potential sensitivity limits for physical component. This work demonstrates that small FOD, which are often difficult to resolve and quantify under conventional raster-based inspection, can be reliably identified by intentionally positioning the specimen within the defocused region of a spherically focused transducer. Results are presented to correlate the reflected acoustic pressure amplitude to defect depth, transducer–specimen distance, and FOD size, providing an approach to quantitatively discriminate small defects that would otherwise produce ambiguous signals.

Multidirectional laminated composites are essential for load‐bearing structures, especially under frequent impact loading. While existing modeling techniques simulate various impact damage modes, our understanding of the underlying mechanisms, specifically the initiation and progression of intralaminar distal cracks and interlaminar delamination, needs improvement. Validating these mechanisms is crucial for enhancing modeling accuracy and expediting the design process. In this work, we propose a three‐dimensional finite element modeling (3D FEM) approach aimed at elucidating the impact damage mechanisms in multidirectional carbon fiber reinforced polymer (CFRP). Our model incorporates cohesive zone models (CZM) to simulate the behavior of intralaminar cracks at distal points and assess their impact on interlaminar delamination at ply interfaces. Using high‐resolution pulse‐echo ultrasonic testing (UT) and an advanced algorithm, we provide a detailed layer‐by‐layer quantification of impact damage. Our modeling strategy effectively predicts both intralaminar and interlaminar damage modes across various laminate configurations and thicknesses, yielding results that align with experimental data. The strategy can be a significant step forward in understanding the low‐velocity impact damage in composite laminates. By enabling visualization of through‐thickness damage, the strategy may enhance the safety and efficiency of composite structures.

This work investigates detection and evaluation of barely visible impact damage (BVID) on a carbon fiber reinforced polymer composite using non‐destructive evaluation. Specifically, this paper presents a novel method to analyze full waveform from ultrasonic data captured from a new field portable inspection station. Forty‐eight samples are impacted between 8 and 16 J representing a spectrum of surface damage below the visual threshold to barely visible surface damage. The samples are conditioned at cold and dry (5°C and 10%RH) to hot and wet (50°C and 90%RH) environmental conditions prior to inspection. Data collection for ultrasonic inspection is performed on a novel portable inspection system outside of an immersion tank, and the captured full waveform data is analyzed. The waveforms are analyzed using a new automated analysis approach presented in the current manuscript, and the effective diameter of the subsurface damage as a function of depth is extracted from the data set. The extracted three‐dimensional damage zone from the analysis of the ultrasound data shows damage increasing from the surface that is at or below the visual inspection threshold to a circular like damage zone with an effective diameter subsurface that is more than 20 mm. These damage zones are compared to the analyzed μCT data sets, and the average error is nominally 1 mm in characterizing the effective diameter at any depth. There is little correlation between the error and the impact energy or the environmental conditions studied, with the error ranging from 4% to 6% over the samples investigated. Highlights: Out of immersion tank, field‐portable ultrasound inspection station, to detect BVID. Automated procedure to characterize three dimensionally the damage from a barely visible impact damage event. Sensitivity to environmental conditioning is investigated for its impact on inspections. Results validated with x‐ray CT with a typical accuracy of 1.32 mm.