Effects of physical aging on long-term behavior of composites

The HSCT plane, envisioned to have a lifetime of over 60,000 flight hours and to travel at speeds in excess of Mach 2, is the source of intensive study at NASA. In particular, polymer matrix composites are being strongly considered for use in primary and secondary structures due to their high strength to weight ratio and the options of property tailoring. However, an added difficulty in the use of polymer based materials is that their properties change significantly over time, especially at the elevated temperatures that will be experienced during flight, and prediction of properties based on irregular thermal and mechanical loading is extremely difficult. This study focused on one aspect of long-term polymer composite behavior: physical aging. When a polymer is cooled to below its glass transition temperature, the material is not in thermodynamic equilibrium and the free volume and enthalpy evolve over time to approach their equilibrium values. During this time, the mechanical properties change significantly and this change is termed physical aging. This work begins with a review of the concepts of physical aging on a pure polymer system. The effective time theory, which can be used to predict long term behavior based on short term data, is mathematically formalized. The effects of aging to equilibrium are proven and discussed. The theory developed for polymers is then applied first to a unidirectional composite, then to a general laminate. Comparison to experimental data is excellent. It is shown that the effects of aging on the long-term properties of composites can be counter-intuitive, stressing the importance of the development and use of a predictive theory to analyze structures

Effects of physical aging on long-term creep of polymers and polymer matrix composites

For many polymeric materials in use below the glass transition temperature, the long term viscoelastic behavior is greatly affected by physical aging. To use polymer matrix composites as critical structural components in existing and novel technological applications, this long term behavior of the material system must be understood. Towards that end, this study applied the concepts governing the mechanics of physical aging in a consistent manner to the study of laminated composite systems. Even in fiber-dominated lay-ups the effects of physical aging are found to be important in the long-term behavior of the composite. The basic concepts describing physical aging of polymers are discussed. Several aspects of physical aging which have not been previously documented are also explored in this study, namely the effects of aging into equilibrium and a relationship to the time-temperature shift factor. The physical aging theory is then extended to develop the long-term compliance/modulus of a single lamina with varying fiber orientation. The latter is then built into classical lamination theory to predict long-time response of general oriented lamina and laminates. It is illustrated that the long term response can be counterintuitive, stressing the need for consistent modeling efforts to make long term predictions of laminates to be used in structural situations

A Comparison of Tension and Compression Creep in a Polymeric Composite and the Effects of Physical Aging on Creep Behavior

Experimental and analytical methods were used to investigate the similarities and differences of the effects of physical aging on creep compliance of IM7/K3B composite loaded in tension and compression. Two matrix dominated loading modes, shear and transverse, were investigated for two load cases, tension and compression. The tests, run over a range of sub-glass transition temperatures, provided material constants, material master curves and aging related parameters. Comparing results from the short-term data indicated that although trends in the data with respect to aging time and aging temperature are similar, differences exist due to load direction and mode. The analytical model used for predicting long-term behavior using short-term data as input worked equally as well for the tension or compression loaded cases. Comparison of the loading modes indicated that the predictive model provided more accurate long term predictions for the shear mode as compared to the transverse mode. Parametric studies showed the usefulness of the predictive model as a tool for investigating long-term performance and compliance acceleration due to temperature

How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning

Metamaterials are composite materials with engineered geometrical micro- and meso-structures that can lead to uncommon physical properties, like negative Poisson's ratio or ultra-low shear resistance. Periodic metamaterials are composed of repeating unit-cells, and geometrical patterns within these unit-cells influence the propagation of elastic or acoustic waves and control dispersion. In this work, we develop a new interpretable, multi-resolution machine learning framework for finding patterns in the unit-cells of materials that reveal their dynamic properties. Specifically, we propose two new interpretable representations of metamaterials, called shape-frequency features and unit-cell templates. Machine learning models built using these feature classes can accurately predict dynamic material properties. These feature representations (particularly the unit-cell templates) have a useful property: they can operate on designs of higher resolutions. By learning key coarse scale patterns that can be reliably transferred to finer resolution design space via the shape-frequency features or unit-cell templates, we can almost freely design the fine resolution features of the unit-cell without changing coarse scale physics. Through this multi-resolution approach, we are able to design materials that possess target frequency ranges in which waves are allowed or disallowed to propagate (frequency bandgaps). Our approach yields major benefits: (1) unlike typical machine learning approaches to materials science, our models are interpretable, (2) our approaches leverage multi-resolution properties, and (3) our approach provides design flexibility.Comment: Under revie

Graphene oxide based functional hierarchical materials

Current synthetic composite structural materials typically exhibit a trade-off between mechanical properties, sacrificing one property for the enhancement of another. Comparatively, natural materials have been shown to optimize several properties simultaneously. The origin of this remarkable capacity is believed to be in large part due to the hierarchical structure observed in natural materials that span length scales over several orders of magnitude. Limitations in currently available processing methods and materials have restricted the ability to reproducibly and cost-effectively manufacture hierarchical, biomimetic materials. However, graphene oxide (GO) has proven to be an excellent candidate for the facile fabrication of such materials. In earlier studies, we have demonstrated four levels of the hierarchical structure of GO papers formed by vacuum filtration of aqueous GO dispersions – the nanometer thick graphene oxide sheets, ~75-nm thick lamellae of stacked nanosheets, ~400-nm thick superlamellae, and finally the paper itself on the micron scale. By incorporating various polymer materials into the GO papers with controlled ordering, we are able to tune the interactions of the intermediate length scale structures. The ability to fuse GO papers further allows for the creation of novel materials where properties vary in the direction of stacking. In addition, these materials can be rendered multifunctional, by means of postprocessing, to induce properties such as electrical conductivity. In this study, we provide an overview of this design process and demonstrate a system that replicates the structure of fish armor plating while adding electrical functionality. Earlier studies on the organization of fish armor plating reveal the complex structure of the individual scales, where each individual layer serves a discrete function in resisting puncture attacks to the fish. The uppermost layer, for example, would be of maximum stiffness to prevent penetration, whereas the underlying layers are more compliant, serving to dissipate energy. This example is one a realization of a toolbox for the fabrication of complex hierarchical structures with the ability to control mechanical, electrical, thermal, and transport properties

Uncertainty Quantification of Bandgaps in Acoustic Metamaterials with Stochastic Geometric Defects and Material Properties

This paper studies the utility of techniques within uncertainty quantification, namely spectral projection and polynomial chaos expansion, in reducing sampling needs for characterizing acoustic metamaterial dispersion band responses given stochastic material properties and geometric defects. A novel method of encoding geometric defects in an interpretable, resolution independent is showcased in the formation of input space probability distributions. Orders of magnitude sampling reductions down to $\sim10^0$ and $\sim10^1$ are achieved in the 1D and 7D input space scenarios respectively while maintaining accurate output space probability distributions through combining Monte Carlo, quadrature rule, and sparse grid sampling with surrogate model fitting

Application of finite element modeling and viscoelasticity theory in characterization and prediction of dielectric relaxation process in polymer nanodielectrics

Nanodielectrics, typically defined as polymer composites with nanosized ceramic fillers, have demonstrated significant improvements in electrical endurance, breakdown strength and dielectric constant relative to their constituent materials, which leads to enhanced energy storage capabilities. The key role played by the large interfacial area surrounding nanofillers proves to be essential to the enhancement, yet quantitative models to predict the altered dielectric properties in the interfacial area are rarely seen. In this presentation, we apply a finite element modeling approach, originally developed for viscoelasticity analysis, to predict the frequency and temperature dependence of dielectric permittivity spectra in polymer nanodielectrics containing functionalized silica fillers. The dispersion state of nanofillers in the finite element model is determined from descriptor-based analysis of scanning electron micrographs, and the interfacial area surrounding the fillers is explicitly configured into the geometry. The dielectric permittivity spectra of the polymer matrix are imported into the model using a series of Debye relaxation functions. The analogy between dielectric permittivity and viscoelastic modulus allows for a simple mathematical conversion between the two physically distinct quantities, which enables the usage of Prony Series when fitting the dielectric spectrum. With the assistance of a earlier developed algorithm to fit the viscoelastic modulus, the parameters of Debye relaxation series function are obtained. Using the above morphology and physical property inputs, dielectric spectroscopy experiments over a range of frequencies and temperatures can be simulated. Properties of the interfacial region are obtained through an iterative comparison between model output and experimental results. It is observed that the distribution of dielectric relaxation times of the interface could be expressed using those of the polymer matrix multiplied by frequency shift factors that vary with different functionalization of the silica filler surfaces. Our results indicate that surface energy parameters of the filler and the polymer matrix can vary the dielectric response of the composites, which is consistent with earlier observations of the viscoelastic properties of polymer nanocomposites. Further discussion on the results also provides insight into the underlying dielectric relaxation mechanism in the interfacial area