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Properties of particulate-filled polymer matrix composites are highly dependent on the spatial position, orientation and assembly of the particles throughout the matrix. External fields such as electric and magnetic have been individually used to orient, position and assemble micro and nanoparticles in polymer solutions and their resulting material properties were investigated, but the combined effect of using more than one external field on the material properties has not been studied in detail. Applying different configurations of electric and magnetic fields on geometrically and magnetically anisotropic particulates can produce varying microarchitectures with a range of material properties. Experimentally and with simulations, we systematically probe the effect of combined electric and magnetic fields on the microstructure formation of geometrically and magnetically anisotropic barium hexaferrite (BHF) in polydimethylsiloxane (PDMS). The magnetic and dielectric properties resulting from different microstructures are characterized and microstructure-property relationships are analyzed. Our results demonstrate that a variety of microarchitectures can be produced using multi-field processing depending on the nature of the applied external field. For example, the application of an electric field creates macro-chains where the orientation of the BHF stacks inside the macro-chains is random. On the other hand, application of a magnetic field rotates the BHF stacks within the macro-chain in the direction dictated by the magnetic field. In simulations, the dielectrophoretic, magnetic, and viscous forces and torques acting on the particles show that particle anisotropies are central to the ability to control orientation along the orthogonal magnetic and geometric axes, mirroring experimental results. The authors refer to the ability to manipulate particle orientation along orthogonal axes as ‘orthogonal control’. Using this technique, not only are a variety of microstructures possible, but also a range of dielectric and magnetic properties can result. For example, for 1 vol% BHF-PDMS composites, the experimental dielectric permittivity is found to vary from 2.84 to 5.12 and the squareness ratio (remnant magnetization over saturation magnetization) is found to vary from 0.55 to 0.92 (from 0.52 to 0.99 in simulations) depending on the applied external stimuli. The ability to predict and produce a variety of microstructures with a range of properties from a single material set will be particularly beneficial for resin pool based additive manufacturing and 3D printing.

Additive manufacturing, no longer reserved exclusively for prototyping components, can create parts with complex geometries and locally tailored properties. For example, multiple homogenous material sources can be used in different regions of a print or be mixed during printing to define properties locally. Additionally, heterogeneous composites provide an opportunity for another level of tuning properties through processing. For example, within particulate-filled polymer matrix composites before curing, the presence of an applied electric and/or magnetic fields can reorient filler particles and form hierarchical structures depending on the fields applied. Control of particle organization is important because effective material properties are highly dependent on the distribution of filler material within composites once cured. While previous work in homogenization and effective medium theories have determined properties based upon ideal analytic distributions of particle orientations and spatial location, this work expands upon these methods generating discrete distributions from quasi-Monte Carlo simulations of the electromagnetic processing event. Results of simulations provide predicted microarchitectures from which effective properties are determined via computational homogenization.

These particle dynamics simulations account for dielectric and magnetic forces and torques in addition to hydrodynamic forces and hard particle separation. As such, the distributions generated are processing field dependent. The effective properties for a composite represented by this distribution are determined via computational homogenization using finite element analysis (FEA). This provides a path from constituents, through processing parameters to effective material properties. In this work, we use these simulations in conjunction with a multi-objective optimization scheme to resolve the relationships between processing conditions and effective properties, to inform field-assisted additive manufacturing processes.

The constituent set providing the largest range of properties can be found using optimization techniques applied to the aforementioned simulation framework. This key information provides a recipe for tailoring properties for additive manufacturing design and production. For example, our simulation results show that stiffness for a 10% filler volume fraction can increase by 34% when aligned by an electric field as compared to a randomly distributed composite. The stiffness of this aligned sample is also 29% higher in the direction of the alignment than perpendicular to it, which only differs by 5% from the random case [1]. Understanding this behavior and accurately predicting composite properties is key to producing field processed composites and prints. Material property predictions compare favorably to effective medium theory and experimentation with trends in elastic and magnetic effective properties demonstrating the same anisotropic behavior as a result of applied field processing. This work will address the high computational expense of physics simulation based objective functions by using efficient algorithms and data structures. We will present an optimization framework using nested gradient searches for micro barium hexaferrite particles in a PDMS matrix, optimizing on composite magnetization to determine the volume fraction of filler that will provide the largest range of properties by varying the applied electric and magnetic fields.

In this study, we discuss the characterization and quantification of composite microstructures formed by the external field manipulation of high aspect ratio magnetic particles in an elastomeric matrix. In our prior work, we have demonstrated that the simultaneous application of electric and magnetic fields on hard magnetic particles with geometric anisotropy can create a hierarchy of structures at different length scales, which can be used to achieve a wide range of properties. We aim to characterize these hierarchical structures and relate them to final composite properties so we can achieve our ultimate goal of designing a material for a prescribed performance. The complex particle structures are formed during processing by using electric and magnetic fields, and they are then locked-in by curing the polymer matrix around the particles. The model materials used in the study are barium hexaferrite (BHF) particles and polydimethylsiloxane (PDMS) elastomer. BHF was selected for its hard magnetic properties and high geometric anisotropy. PDMS was selected for its good mechanical properties and its tunable curing kinetics. The resulting BHF-PDMS composites are magnetoactive, i.e., they will deform and actuate in response to magnetic fields. In order to investigate the resulting particle orientation, distribution and alignment and to predict the composite’s macro scale properties, we developed techniques to quantify the particle structures.

The general framework we developed allows us to quantify and directly compare the microstructures created within the composites. To identify structures at the different length scales, images of the composite are taken using both optical microscopy and scanning electron microscopy. We then use ImageJ to analyze them and gather data on particle size, location, and orientation angle. The data is then exported to MATLAB, and is used to run a Minimum Spanning Tree Algorithm to classify the particle structures, and von Mises Distributions to quantify the alignment of said structures.

Important findings show 1) the ability to control structure using a combination of external electric, magnetic and thermal fields; 2) that electric fields promote long range order while magnetic fields promote short-range order; and 3) the resulting hierarchical structure greatly influence bulk material properties. Manipulating particles in a composite material is technologically important because changes in microstructure can alter the properties of the bulk material. The multifield processing we investigate here can form the basis for next generation additive manufacturing platforms where desired properties are tailored locally through in-situ hierarchical control of particle arrangements.