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Peter Spaeth

Publications and source records attributed to Peter Spaeth.

16 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Thermal Inspection of a Composite Fuselage Section Using theMethod of Proper Generalized Decomposition

Proper Generalized Decomposition (PGD) is a reduced order modeling technique for the simulation of physical systems whose governing equations depend on boundary conditions, initial conditions, material properties, and geometric parameters. It uses separated representations of system covariates combined with an iterative approximation method known as successive enrichment in order to compute an accurate parameter-dependent approximation to the full governing equations. PGD can also be used as an alternative to the Singular Value Decomposition (SVD) of a matrix and therefore as an alternative to PCA thermography. In this paper PGD was used to analyze data derived from the inspection of a composite fuselage forward section using flash thermography, and the results were compared against the standard PCA approach.

Nondestructive Evaluation↗
NASA NTRSDate not supplied

A Markov Random Field Model for Texture-based Segmentation of Small Cracks in Thin InconelTubes

Markov random fields have been used for image segmentation since their introduction in the1980s. This work applies a method from Principal Component Thermography to enhance thecontrast in damage regions in 3D images derived from X-ray computed tomography (CT)inspections. The developed method is applied to sizing of small cracks in thin Inconel tubes designed as probability of detection (POD) samples for radiographic inspection.Misclassification errors arising from artifacts due to beam-hardening are reduced by fitting the boundary of the segmented damage region with the arc of an ellipse. Results are comparedagainst those obtained through manual inspection.

Image segmentation↗
NASA NTRSDate not supplied

Additive Manufacturing Model-Based Process Metrics: Reduced Order Modeling of the Laser Powder Bed Fusion Process

The multi-scale and complex process of printing additively manufactured (AM) parts can have unexpected, but predictable, build conditions that result in material microstructure variability. In this work, we describe a fully parallel reduced order modeling approach that has been developed to evaluate the evolution of AM processes, termed the AM moment measure method. This method couples the known sequence of the AM process with a physically informed nearest neighbors’ calculation to map the conditions of a part-scale build. The result is a map of the build that is derived directly from build files or in-situ process monitoring sensors. The methodology and terminology of the approach will be described, and computed build maps will be calculated and compared for various laser powder bed fusion (LPBF) builds of Ti-6Al-4V. Such comparative results develop understanding of how the sequential process actions can affect the LPBF-AM build quality and microstructure variability.

Laser Powder Bed Fusion↗
NASA NTRSDate not supplied

Composite Bond Line Measurements Based on a Bayesian Analysis of Flash Thermography Data

For bonded composite materials, an accurate characterization of the adhesive bond line is needed to predict failure modes and fracture toughness. In this paper, bond line thickness was estimated from data obtained using through transmission flash thermography. The forward model that predicts back surface temperature is based on a three layer heat diffusion equation with varying diffusivity and flux boundary conditions. The corresponding inverse problem of estimating bond line thickness from measurement data was solved using a Bayesian approach that assumed Gaussian priors for the bond line thickness and thermal diffusivity of the adherends. Finally, the outputs of the thermography based method were compared to measurements that were collected using a micrometer and ultrasound testing.

Flash thermography↗
NASA NTRSDate not supplied

Porosity Area Fraction Analysis of Bonded Borosilicate Specimens From in-Situ Optical Imagery

Image segmentation methods are routinely used to analyze data derived in nondestructive testing scenarios. A typical problem in this area is to estimate porosity from X-ray Computed Tomography (CT) inspections. This investigation aims to understand the effectiveness of open-source image segmentation methods for porosity estimation from optical images in the context of strength analysis of bonded materials and provides a benchmark of performance against a standard commercial software package. The rank ordering of the specimens by porosity area fraction is consistent across both methods.

Porosity area fraction↗
NASA NTRSDate not supplied

Sources of Uncertainty in Ultrasonic Phase-Based Assessment of Interfacial Stiffness in Adhesive Joints

Quantitative nondestructive evaluation (NDE) of adhesive joint strength is critical for certifying fastener-free bonded aerospace structures. Unfortunately, available adhesive NDE approaches are only suitable for detecting gross defects and delamination and cannot reliably detect variations in strength in intact bonds. Intact (i.e., nominally defect free) bonds are challenging to inspect because they, by definition, contain no macroscopic defects that would produce a strong NDE signature. Even in the absence of obvious defects, however, bond strength can vary substantially due to factors such as contamination, variations in the adhesive cure processes, and environmental exposure. For ultrasonic bond inspections, the effect of interactions from an imperfect adhesive interface are subtle and may include changes in the frequency content or phase of the ultrasonic signal. The bond condition can be deduced from these subtle changes in the acoustic response using an accompanying physics-based model of adhesive joint interaction. For example, researchers have used the spring network adhesion model of Baik and Thompson [1] to determine the interfacial stiffness of the joint from its ultrasonic response (e.g., [2]). This interfacial stiffness metric describes the bonds resistance to separation between the adhesive and adherent and has shown a strong correlation with joint strength. In this study, interfacial stiffness is assessed using the ultrasonic phase change associated with bond interaction in varying bond conditions. The phase change is measured precisely using a phase-locked-loop instrument over a swept range of frequencies. The phase transition at resonance, where the bond thickness is half the acoustic wavelength, produces a unique signature with which to fit a physics-based model. Interfacial stiffness is determined by minimizing the difference between the theoretical and experimental phase response. Experimental results are presented for glass specimens bonded with aerospace adhesives and a sensitivity analysis is performed to examine the effects of parameters that may be uncertain in the industrial inspection environment – namely bond thickness, adhesive and adherent properties, and porosity. The results are a first step toward an uncertainty quantified version of the measurement technique, show correlation between interfacial stiffness and bond strength, and are another key step toward maturing this approach for use in complex materials and joint geometries. [1] Baik, J.M. and Thompson, R.B., 1984. “Ultrasonic scattering from imperfect interfaces: a quasi-static model”. Journal of Nondestructive Evaluation, 4. [2] Haldren, H.A., Perey, D.F., Yost, W.T., Cramer, K.E., and Gupta, M.C., 2019. “Swept-frequency ultrasonic phase evaluation of adhesive bonding in tri-layer structures”. Journal of the Acoustical Society of America, 145.

Matt Webster↗
NASA NTRSDate not supplied

Convolution-Based Numerical Solutions of Transient Temperature Fields during Powder Bed Fusion Additive Manufacturing: Theory, Accuracy, and Computational Cost

Powder bed fusion (PBF) additive manufacturing has found numerous applications in the aerospace domain. However, components fabricated via PBF have a complex time-temperature history that significantly impacts subsequent mechanical performance. This study examines convolution-based numerical solutions of transient temperature fields that support simulations involving arbitrary beam shapes and paths during PBF. The convolutional approach is verified through comparisons with analytical solutions of the temperature field. The computational speed and accuracy of the method are assessed through comparisons with other explicit and implicit numerical techniques. In addition, the straightforward translation of the approach from a CPU to a GPU implementation and the resultant performance improvement are presented. The role of the technique in predicting microstructure evolution during PBF (for a greater process-structure-property-performance framework) is also demonstrated. This work supports the development of computational materials methods for understanding and controlling the time-temperature history during PBF.

powder bed fusion↗
NASA NTRSDate not supplied

Development of Computational Materials Workflows for Additively Manufactured Metallic Materials to Enable Accelerated Prediction of Fatigue Performance

The maturation of computational materials approaches for fatigue performance prediction in a qualification and certification process is stifled by the ability to validate complex, microstructure-based simulations. Such a validation strategy bears immediate challenges including generating accurate virtual microstructures, efficiently solving physics-based mechanical simulations over relevant spatial and temporal scales, and acquiring high-fidelity calibration and validation data at the appropriate length scale. This presentation will overview these common challenges and present a case study to demonstrate a computational materials workflow for additively manufactured metallic materials. In this study, process-specific defects are characterized using segmented X-Ray micro-computed tomography measurements and overlaid on virtual microstructures. Accelerated crystal plasticity-based fatigue simulations are performed to demonstrate cyclic evolution and localization of mechanical fields in the vicinity of defects in response to their precise spatial configuration. An example of how this computational materials workflow may support next-generation qualification is discussed.

computational materials↗