skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • NSF Public Access
  • Search Results
  • Multi-Objective Optimization of Predicted Magnetic Properties From Multifield Processing Conditions in Polymer Matrix Particle Composites
Title: Multi-Objective Optimization of Predicted Magnetic Properties From Multifield Processing Conditions in Polymer Matrix Particle Composites
Abstract 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.  more » « less
Award ID(s):
1762188
PAR ID:
10476817
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
Proceedings of the ASME 2022 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. ASME 2022 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. Dearborn, Michigan, USA. September 12–14, 2022. V001T02A008. ASME.
Date Published:
ISBN:
978-0-7918-8627-4
Format(s):
Medium: X
Location:
Dearborn, Michigan, USA
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; (, ASME 2020 Conference on Smart Materials, Adaptive Structures and Intelligent Systems)
    null (Ed.)
    Abstract Composites can be tailored to specific applications by adjusting process variables. These variables include those related to composition, such as volume fraction of the constituents and those associated with processing methods, methods that can affect composite topology. In the case of particle matrix composites, orientation of the inclusions affects the resulting composite properties, particularly so in instances where the particles can be oriented and arranged into structures. In this work, we study the effects of coupled electric and magnetic field processing with externally applied fields on those structures, and consequently on the resulting material properties that arise. The ability to vary these processing conditions with the goal of generating microstructures that yield target material properties adds an additional level of control to the design of composite material properties. Moreover, while analytical models allow for the prediction of resulting composite properties from constituents and composite topology, these models do not build upward from process variables to make these predictions. This work couples simulation of the formation of microscale architectures, which result from coupled electric and magnetic field processing of particulate filled polymer matrix composites, with finite element analysis of those structures to provide a direct and explicit linkages between process, structure, and properties. This work demonstrates the utility of these method as a tool for determining composite properties from constituent and processing parameters. Initial particle dynamics simulation incorporating electromagnetic responses between particles and between the particles and the applied fields, including dielectrophoresis, are used to stochastically generate representative volume elements for a given set of process variables. Next, these RVEs are analyzed as periodic structures using FEA yielding bulk material properties. The results are shown to converge for simulation size and discretization, validating the RVE as an appropriate representation of the composite volume. Calculated material properties are compared to traditional effective medium theory models. Simulations allow for mapping of composite properties with respect to not only composition, but also fundamentally from processing simulations that yield varying particle configurations, a step not present in traditional or more modern effective medium theories such as the Halpin Tsai or double-inclusion theories. 
    more » « less
  2. ; ; ; ; ; ; (, Smart Materials and Structures)
    Abstract 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. 
    more » « less
  3. ; ; ; ; ; (, ASME 2021 Conference on Smart Materials, Adaptive Structures and Intelligent Systems)
    Abstract 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. 
    more » « less
  4. ; (, Advanced Engineering Materials)
    The ability to fabricate polymer matrix composite materials with continuous or discontinuous filler material, oriented in a user‐specified direction, enables implementing designer material properties, such as anisotropic mechanical, thermal, and electrical properties. Conventional fabrication methods rely on a mold, which limits specimen geometry and is difficult to implement. In contrast, additive manufacturing, including fused filament fabrication or fused deposition modeling, direct ink writing, or stereolithography, combined with a method to align filler material such as a mechanical force or an electric, magnetic, shear force, or ultrasound wave field, enables 3D printing polymer matrix composite material specimens with complex geometry and aligned filler material, without the need for a mold. Herein, we review the combinations of fabrication and filler material alignment methods used to fabricate polymer matrix composite materials, in terms of operating and design parameters including size, resolution, print speed, filler material alignment time, polymer matrix and filler material requirements, and filler manipulation requirements. The operating envelope of each fabrication method is described and their advantages, disadvantages, and limitations are discussed. Finally, different combinations of 3D printing and filler material alignment methods in the context of important engineering applications, such as structural materials, flexible electronics, and shape‐changing materials, are illustrated. 
    more » « less
  5. ; ; ; ; (, American Society of Mechanical Engineers)
    Abstract Thermoset materials have begun to be applied in additive composite manufacturing due to their ability to withstand high temperatures without losing structural integrity. Meanwhile, the characterization of mechanical properties for additively manufactured composites is critical for ensuring material reliability and safety. However, traditional testing methods struggle to accurately and nondestructively characterize additively manufactured composites due to challenges posed by curing processes, microstructural variability, anisotropic properties of thermoset composites, and the risk of damaging these materials during evaluation. For characterizing the mechanical properties of additive-manufactured thermoset composites, this paper presents a novel method that combines a nondestructive PZT-LDV guided wave sensing system and a wavenumber analysis that fuses multidimensional Fourier transform with dispersion curve regression. For proof of concept, we performed an experiment using our method to measure a 3D-printed thermoset composite panel. Based on our nondestructive approach, two material properties (shear wave velocity and Poisson’s ratio) in multiple directions were successfully determined for the tested panel. We expect this research to introduce a non-contact and efficient method for characterizing various composites and monitoring their property changes after additive manufacturing. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email