Deformation and failure of PrintCast A356/316 L composites: Digital image correlation and finite element modeling
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36 MATERIALS SCIENCE↗
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Characterizing environment-dependent fracture mechanisms of ceramic matrix composites via digital image correlation
Not provided.
Materials Science↗
Fiber Orientation Evaluation in Reinforced Composites using Digital Image Correlation
This paper presents an innovative nondestructive evaluation (NDE) approach to evaluate fiber orientation in fiber reinforced composites utilizing digital image correlation (DIC). Fiber orientation is a crucial factor that influences the mechanical and thermal properties of the composite. Conventional methods for fiber orientation evaluation and measurement are either destructive, which are typically laborious. NDE methods such as micro computed tomography are expensive and limited in sample size. In this work, we evaluate two methods of excitation of the composite, namely-quasistatic mechanical loading and thermal cycling. Digital Image Correlation (DIC) has been employed in conjunction for fiber orientation measurements. The DIC system is able to measure the fiber orientation by capturing 19x higher strain in the transverse direction of the reinforced fiber axis during excitation. The larger strain occurs in the transverse direction due to larger elongation in the surrounding polymer matrix than the fiber which resists elongation. This innovative method is within the preliminary stage and has been developed to fill the gap/limitations of other NDE methods.
Spencer, Ryan↗
Synergistic single process additive manufacturing of hydro-responsive Ag nanoparticle composites by digital visible light processing 3D printing
Single unit process fabrication of a responsive Ag nanoparticle (NP) chemical hydrogel composite is achieved through digital visible light processing (DLP) 3D printing of a self-assembled nanostructured (multilamellar) resin doped with Ag( i ).
36 MATERIALS SCIENCE↗
Digital Multiphase Composites via Additive Manufacturing
Abstract Mechanical properties of traditional engineering materials are typically coupled to each other, presenting a challenge to practitioners with multi‐dimensional material property requirements. In this work, continuous, independent control over multiple mechanical properties is demonstrated in composite materials realized using additive manufacturing. For the first time, composites additively manufactured from rigid plastic, soft elastomer, and liquid constituents are experimentally characterized, demonstrating materials which span four orders of magnitude in modulus and two orders of magnitude in toughness. By forming analytical mappings between relative concentrations of constituents at the microscale and resulting macroscale material properties, inverse material design is enabled; the method is showcased by printing artifacts with prescribed toughness and elasticity distributions. The properties of these composites are placed in the context of biological tissues, showing they have promise as mechanically plausible tissue mimics.
Smith, Lawrence T.↗
Experimental and statistical study on the effect of process parameters on the quality of continuous fiber composites made via additive manufacturing
Ongoing research in additive manufacturing towards structural and industrial application has led to the use of commingled roving as a manufacturing feedstock for printing high fiber volume fraction composites. The prospects of using this technology for high performance applications necessitates the need for a comprehensive experimental investigation into the effects of processing parameters on the quality of an additively manufactured composite printed from commingled roving feedstock. Here, in this work, transverse flexure and void fraction matrix pyrolysis testing are both performed to evaluate composite quality. The transverse flexure test is a testing approach that evaluates the quality of the interfacial fiber-matrix bond while the void fraction test estimates the void content in the printed composite. A full observational study consisting of 27 different test combinations is done to investigate the effects of three different process parameters namely, temperature, pressure, and print speed across three different levels. Composite samples were made from commingled roving of E-glass and amorphous PET using an in-house built continuous fiber composite digital manufacturing system. Least squares regression analysis is performed to study the main, interaction and quadratic effects of process parameters. A statistical regression model having an R2 adjusted value of 80.1% is generated from the transverse flexure study, which is used to explain main and interaction effects and also predict performance. Response surface plots are also generated and are used to optimize process parameters which can subsequently be of help in scaling up composite manufacturing. Results show that all three process parameters are highly statistically significant at the 0.01 level of significance. Pressure * Temperature and Pressure * Printspeed are significant interaction terms. Pressure plays a weightier role when print speed is increased or temperature is decreased as it closes more voids that would ordinarily have been introduced because of drop in polymer melt viscosity. Micrographic analysis is also performed.
36 MATERIALS SCIENCE↗
Fiber and bundle orientations, matrix rich regions, and mechanical properties of fiber reinforced composites using thermal digital image correlation
Methods for assessing fiber and bundle orientations and mechanical properties of fiber reinforced composite materials using Thermal Digital Image Correlation (TDIC) are disclosed. In some examples, the method comprises exposing the composite material to a temperature change; imaging the composite material at a plurality of time points before, during and/or after the temperature change; and assessing the characteristic of the composite material based on the imaging. In others, temperature changes naturally occur during the cooling process after manufacturing can be employed for this method such as compression molding process, injection molding process, resin transfer molding processes and its variants.
36 MATERIALS SCIENCE↗
Measuring thermomechanical response of large-format printed polymer composite structures via digital image correlation
Large-format additive manufacturing (LFAM) is a branch of additive manufacturing (AM) research with the ability to create large structures typically measuring several meters in scale. LFAM is advantageous for tooling applications, not only because it offers the ability to create complex geometries not easily made using subtractive manufacturing processes, but the cost savings of pelletized feedstock used by these systems result in larger parts printed at faster speeds than traditional AM systems. Fiber reinforced polymer (FRP) is a commonly used feedstock material in LFAM structures because it reduces the distortion experienced during printing. However, FRP introduces highly anisotropic thermomechanical properties and contributes to a nonhomogeneous microstructure that can result in critical distortion of dimensions during tooling. Measuring the global thermomechanical response of LFAM structures requires a more representative method that accounts for not only anisotropic properties but also the nonhomogeneous nature of the final part. This is where traditional techniques to measure thermomechanical response, such as thermomechanical analysis (TMA), fall short as they assume homogeneity. This study evaluated the coefficient of thermal expansion (CTE) of LFAM structures as measured by TMA as compared to a novel digital image correlation oven (DIC Oven) system. The LFAM structures were made from 20 % by weight carbon fiber reinforced acrylonitrile butadiene styrene (CF-ABS). TMA measurements showed significant variations in CTE across a single LFAM bead, confirming the need for a global technique that captures overall thermomechanical response. The CTE values measured using the DIC Oven compared well to average TMA values obtained from localized measurements across the sample. The DIC Oven was also used to quantify the effects of different layer orientations on thermomechanical properties, which cannot be easily captured using TMA. A predictive model was also developed by using localized TMA values across an LFAM bead to predict the overall thermomechanical response of an LFAM structure.
36 MATERIALS SCIENCE↗
Bayesian inference of fiber orientation and polymer properties in short fiber-reinforced polymer composites
Herein we present a Bayesian methodology to infer the elastic modulus of the constituent polymer and the fiber orientation state in a short-fiber reinforced polymer composite (SFRP). The properties are inversely determined using only a few experimental tests. Developing composite manufacturing digital twins for SFRP composite processes, including injection molding and extrusion deposition additive manufacturing (EDAM) requires extensive experimental material characterization. In particular, characterizing the composite mechanical properties is time consuming and therefore, micromechanics models are used to fully identify the elasticity tensor. Hence, the objective of this paper is to infer the fiber orientation and the effective polymer modulus and therefore, identify the elasticity tensor of the composite with minimal experimental tests. To that end, we develop a hierarchical Bayesian model coupled with a micromechanics model to infer the fiber orientation and the polymer elastic modulus simultaneously which we then use to estimate the composite elasticity tensor. We motivate and demonstrate the methodology for the EDAM process but the development is such that it is applicable to other SFRP composites processed via other methods. Our results demonstrate that the approach provides a reliable framework for the inference, with as few as three tensile tests, while accounting for epistemic and aleatory uncertainty. Posterior predictive checks show that the model is able to recreate the experimental data well. The ability of the Bayesian approach to calibrate the material properties and its associated uncertainties, make it a promising tool for enabling a probabilistic predictive framework for composites manufacturing digital twins.
36 MATERIALS SCIENCE↗
Probabilistic learning and updating of a digital twin for composite material systems
This paper presents an approach for characterizing and estimating statistical dependence between a large number of observables in a composite material system. Conditional regression is carried out using the estimated joint density function, permitting a systematic exploration of interdependence between fine scale and coarse observables that can be used for both prognosis and design of complex material systems. An example demonstrates the integration of experimental data with a computational database. The statistical approach is based on the probabilistic learning on manifolds recently developed by the authors. This approach leverages intrinsic structure detected through diffusion on graphs with projected stochastic differential equations to generate samples constrained to that structure.
42 ENGINEERING↗
MEASURING THERMALLY-INDUCED DISTORTION OF LARGE-SCALE COMPOSITE PRINTED STRUCTURES USING DIGITAL IMAGE CORRELATION
Additive manufacturing (AM) gives designers the ability to create complex part geometries without several of the limitations of traditional manufacturing. Large-Scale AM (LSAM) allows the creation of large composite structures and is a popular area of composite tooling research. Fiber-reinforced polymers are common feedstock for LSAM tooling but introduce anisotropic thermomechanical properties that can lead to the warpage of tools at autoclave conditions. Digital image correlation (DIC) has been used to measure the coefficient of thermal expansion (CTE) of LSAM parts using a novel DIC Oven design. This method was utilized to measure the CTE of LSAM structures printed using different fiber-reinforced materials and multiple geometries. Specifically, glass fiber ABS (GF-ABS) and carbon fiber ABS (CF-ABS) samples were compared as well as cube and plate geometries for each material. The temperature profile over time was also evaluated to determine how rapidly the strain of the sample increased as temperature of the DIC Oven was increased. A thermal model was developed to predict temperature profiles for various samples and identify when the samples had reached thermal equilibrium.
Corum, Tyler↗
In situ observation of the deformation and fracture of an alumina-alumina ceramic-matrix composite at elevated temperature using x-ray computed tomography
The deformation and fracture of a 0/90° Nextel 720 mullite fibre / alumina matrix ceramic-matrix composite material has been observed by in situ synchrotron x-ray computed micro-tomography at ambient temperature and at 1100 °C. A three-point loading configuration was used and samples were loaded monotonically until final fracture. Here, the flexural strength was found to be unaffected by temperature. 3D visualisation of the microstructure showed fracture occurred by propagation of internal cracks at stresses close to the flexural strength. Shear fractures propagated across fibre bundles that were oriented perpendicular to the flexural stress at both room temperature and 1100 °C. At the higher temperature, an additional failure mode of delamination was observed, that may arise from relaxation of thermal residual stresses and creep.
36 MATERIALS SCIENCE↗
Understanding the Elastic, Plastic, and Damage Features in Fracturing of Self-reinforced Thermoplastic Composites via Non-destructive Digital Imaging Correlation
This work demonstrated the utilization of non-destructive Digital Imaging Correlation (DIC) method to characterize the elastic, plastic, and damage features during the Mode I intra-laminar fracturing process of self-reinforced thermoplastic composites by using a self-reinforced polypropylene (PP) composite as an example. The DIC results clearly showed the development of huge plastic zone (PZ) and non-negligible Fracture Process Zone (FPZ) in front of the notch tip during the fracturing process, and the geometries and sizes of the foregoing zones at the peak load were further quantified. Such an interesting fracturing behavior of self-reinforced thermoplastic composites is way different from brittle materials (e.g., glass, acrylic, etc.), ductile materials (e.g., aluminum, steel, etc.), and even quasi-brittle materials (e.g., concrete, nanoparticle-reinforced composites, tough ceramics, wood, cement, carbon/glass fiber-reinforced polymers, etc.). Thus, understanding the elastic, plastic, and damage features is the first step before better characterizing the material fracture properties of self-reinforced thermoplastic composites through new analytical methods and computational modeling. These efforts are of utmost importance for wide applications of self-reinforced thermoplastic composites in various engineering fields in the future.
Lightweight Composites, Self-reinforced Thermoplas↗
Physics augmented machine learning discovery of composition-dependent constitutive laws for 3D printed digital materials
Multi-material 3D printing, particularly through polymer jetting, enables the fabrication of digital materials by mixing distinct photopolymers at the micron scale within a single build to create a composite with tunable mechanical properties. Here, this work presents an integrated experimental and computational investigation into the composition-dependent mechanical behavior of 3D printed digital materials. We experimentally characterize five formulations, combining soft and rigid UV-cured polymers under uniaxial tension and torsion across three strain and twist rates. The results reveal nonlinear and rate-dependent responses that strongly depend on composition. To model this behavior, we develop a physics-augmented neural network (PANN) that combines a partially input convex neural network (pICNN) for learning the composition-dependent hyperelastic strain energy function with a quasi-linear viscoelastic (QLV) formulation for time-dependent response. The pICNN ensures convexity with respect to strain invariants while allowing non-convex dependence on composition. To enhance interpretability, we apply $L_0$ sparsification. For the time-dependent response, we introduce a multilayer perceptron (MLP) to predict viscoelastic relaxation parameters from composition. The proposed model accurately captures the nonlinear, rate-dependent behavior of 3D printed digital materials in both uniaxial tension and torsion, achieving high predictive accuracy for interpolated material compositions. This approach provides a scalable framework for automated, composition-aware constitutive model discovery for multi-material 3D printing.
Constitutive modeling↗
Fulcrum House – Towards a Sustainable Composite Architecture
In the last decades, the accessibility to digital fabrication technologies positioned composites as an affordable, structurally efficient and low-maintenance alternative to traditional mineral-based and high-energy materials. Of special interest are carbon nanotubes (CNT) and carbon foam (CFoam) from methane and coal pyrolysis respectively, as an environmentally friendly substitute to glass and carbon fiber. This paper presents the design of a principled single-family house, as a first case study to identify the constraints of such a material system, including technology gap, techno-economic and life cycle assessment, as well as building code requirements. In turn, the investigation proposes a suitable design-to-fabrication workflow, which potentially could bring CNT and CFoam materials to the construction of buildings.
01 COAL, LIGNITE, AND PEAT↗
A machine learning approach to determine the elastic properties of printed fiber-reinforced polymers
This work focuses on the simultaneous determination of the elastic constants and the fiber orientation state for a short fiber-reinforced polymer composite by performing a minimum of experimental tests. Here we introduce a methodology that enables the inverse determination of fiber orientation state and the in-situ polymer properties by performing tensile tests at the composite coupon level. We demonstrate the approach for the extrusion deposition additive manufacturing (EDAM) process to illustrate one application of the methodology, but the development is such that it can be applied to short fiber-reinforced polymer (SFRP) systems processed via other methods. Currently, developing composites additive manufacturing digital twins require extensive material characterization. In particular, the mechanical characterization of the orthotropic elastic properties of a composite involves extensive sample preparation and testing, therefore the elasticity tensor is generally populated using a micromechanics model. This, however, requires measuring the fiber orientation state in addition to knowing the constituent material properties. Experimentally measuring the fiber orientation state can be tedious and time consuming. Further, optical methods are limited to resolving the orientation of cylindrical fibers or cluster of non-cylindrical fibers, and computed tomography (CT) methods scan regions of volume that are much smaller than a full printed bead. Therefore, we propose a methodology, accelerated by machine learning, to identify the anisotropic mechanical properties and fiber orientation state at the same time. Early results show that inference of the fiber orientation and composite properties is possible with as few as three tensile tests. Our results show that a combination of the choice of the micromechanics model and reliable set of experiments can yield the nine elastic constants, as well as, the fiber orientation state.
36 MATERIALS SCIENCE↗
An innovative digital image correlation technique for in-situ process monitoring of composite structures in large scale additive manufacturing
As additive manufacturing (AM) continues to develop and become a standardized manufacturing method, there will be a continued need to provide in-situ monitoring during the manufacturing of polymer composite printed components. Thermal residual stress is a primary cause of failures such as interlayer disbonds or delamination, micro cracking, and dimensional instability, which can occur during or after the build. Here, we report a novel digital image correlation (DIC) adaptation to monitor thermal residual stresses during the entire print process for large-scale AM. In this work, DIC has been investigated (a) by the natural speckle produced by the polymer surface for correlation, (b) to monitor AM build, and (c) to evaluate the effect of thermal residual stress on warpage of the printed component. The natural speckle pattern of the AM material resulted in a respectable 3.57% error compared to the traditional painted speckle pattern of 3.05% error. DIC measured a 190% increase in vertical displacement at the edge of the wall compared to the center, indicating warpage during AM. This work is a step towards a non-intrusive residual stress measuring technique using DIC for large-scale AM.
36 MATERIALS SCIENCE↗
Tensile Deformation of DED Graded IN718-René41 Superalloy at 650°C by in-situ Neutron Diffraction and Digital Image Correlation
A compositionally graded material comprised IN718 (718, γ'/γ" strengthened superalloy) and René41 (R41, a medium γ' strengthened superalloy) was fabricated using laser blown-powder directed energy deposition (DED/LB-M). Hot gas path components that require high and low temperature capabilities at different locations may benefit from such graded superalloy, potentially eliminating the use of welded or mechanically joined components and reducing cost. In the present work, microstructure and 650°C tensile behavior of 718-R41 graded superalloy in as-built and heat-treated states are investigated by Digital Image Correlation (DIC), in-situ neutron diffraction, coupled with conventional characterization techniques. The applied heat treatment generates static recrystallization and equiaxed grains in 718 rich compositions, while R41 rich compositions remain partially or un-recrystallized. Yield strengths of 718 and R41 sections in heat-treated state are comparable to wrought forms but the graded compositions show weakness. From neutron experiment, diffraction elastic constants and single crystal elastic constants are calculated, which show small difference between the as-built and heat-treated states and between the γ and γ' phases. Grain-level load transfer from the (220) grains to (200) grains is found to be composition dependent, and qualitatively agrees with DIC measured macroscopic yield strength. Within the (200) grains, the γ/γ' phases deform elastically until the γ phase yields and afterwards the γ' phase takes load from γ phase.
digital image correlation↗