skip to main content

Explore Research Products in the NSF-PAR

This content will become publicly available on October 1, 2024

Title: Hybrid Geometry/Property Autoencoders for Multi-Lattice Transitions
Additive manufacturing has revolutionized structural optimization by enhancing component strength and reducing material requirements. One approach used to achieve these improvements is the application of multi-lattice structures. The performance of these structures heavily relies on the detailed design of mesostructural elements. Many current approaches use data-driven design to generate multi-lattice transition regions, making use of models that jointly address the geometry and properties of the mesostructures. However, it remains unclear whether the integration of mechanical properties into the data set for generating multi-lattice interpolations is beneficial beyond geometry alone. To address this issue, this work implements and evaluates a hybrid geometry/property machine learning model for generating multi-lattice transition regions. We compare the results of this hybrid model to results obtained using a geometry-only model. Our research determined that incorporating physical properties decreased the number of variables to address in the latent space, and therefore improves the ability of generative models for developing transition regions of multi-lattice structures.  more » « less
Award ID(s):
2309250
NSF-PAR ID:
10496755
Author(s) / Creator(s):
; ;
Publisher / Repository:
Solid Freeform Fabrication Symposium
Date Published:
Journal Name:
2023 International Solid Freeform Fabrication Symposium
Format(s):
Medium: X
Location:
Austin, TX, USA
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ( , 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.)
    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
  2. ( , Proceedings of the IASS Annual Symposium 2023)
    In this research, we propose a Multi-Filament Fused Deposit Modelling (MFFMD) printer and a respective generator that can be used to produce structural parts with locally tailored functional properties. 3D-printed structural components can highly benefit from multi-material printing with tuneable functional properties. Currently, multi-material printing is mainly achieved using multiple separate nozzles, leading to discontinuous flow in switching materials. This limitation results in material interface delamination, minimal control in the continuous transition of material properties, and longer production time.To address this, we first design and build an MFFMD printer with a single customized nozzle allowing seamless switching between multiple filaments. We then develop a method that generates a continuous toolpath of a given geometry and differentiates materials based on various stress conditions at particular regions. To illustrate, we fabricate a Pratt truss as an example of a tension-compression structure as a case study. In one go, the MFFMD printer deposits resistant filament, respectively, at tension- or compression-concentrated regions based on local stress conditions. Comparative load tests are conducted to validate the performance enhancement of multi-filament prints against single-filament prints. Our proposed method is a prototypical study conducted on a small scale. While it mainly uses thermal plastic filaments, it can be expanded to other construction materials and scales in the future. 
    more » « less
  3. ; ; ( , 3D Printing and Additive Manufacturing)
    Additive manufacturing is advantageous for producing lightweight components while addressing complex design requirements. This capability has been bolstered by the introduction of unit lattice cells and the gradation of those cells. In cases where loading varies throughout a part, it may be beneficial to use multiple, distinct lattice cell types, resulting in multi-lattice structures. In such structures, abrupt transitions between unit cell topologies may cause stress concentrations, making the boundary between unit cell types a primary failure point. Thus, these regions require careful design to ensure the overall functionality of the part. Although computational design approaches have been proposed, smooth transition regions are still difficult to achieve, especially between lattices of drastically different topologies. This work demonstrates and assesses a method for using variational autoencoders to automate the creation of transitional lattice cells, examining the factors that contribute to smooth transitions. Through computational experimentation, it was found that the smoothness of transition regions was strongly predicted by how closely the endpoints were in the latent space, whereas the number of transition intervals was not a sole predictor. 
    more » « less
  4. ; ; ; ; ; ( , Small)
    Abstract

    Next‐generation electronics and energy technologies can now be developed as a result of the design, discovery, and development of novel, environmental friendly lead (Pb)‐free ferroelectric materials with improved characteristics and performance. However, there have only been a few reports of such complex materials’ design with multi‐phase interfacial chemistry, which can facilitate enhanced properties and performance. In this context, herein, novel lead‐free piezoelectric materials (1‐x)Ba0.95Ca0.05Ti0.95Zr0.05O3‐(x)Ba0.95Ca0.05Ti0.95Sn0.05O3, are reported, which are represented as (1‐x)BCZT‐(x)BCST, with demonstrated excellent properties and energy harvesting performance. The (1‐x)BCZT‐(x)BCST materials are synthesized by high‐temperature solid‐state ceramic reaction method by varyingxin the full range (x= 0.00–1.00). In‐depth exploration research is performed on the structural, dielectric, ferroelectric, and electro‐mechanical properties of (1‐x)BCZT‐(x)BCST ceramics. The formation of perovskite structure for all ceramics without the presence of any impurity phases is confirmed by X‐ray diffraction (XRD) analyses, which also reveals that the Ca2+, Zr4+, and Sn4+are well dispersed within the BaTiO3lattice. For all (1‐x)BCZT‐(x)BCST ceramics, thorough investigation of phase formation and phase‐stability using XRD, Rietveld refinement, Raman spectroscopy, high‐resolution transmission electron microscopy (HRTEM), and temperature‐dependent dielectric measurements provide conclusive evidence for the coexistence of orthorhombic + tetragonal (Amm2+P4mm) phases at room temperature. The steady transition ofAmm2crystal symmetry toP4mmcrystal symmetry with increasingxcontent is also demonstrated by Rietveld refinement data and related analyses. The phase transition temperatures, rhombohedral‐orthorhombic (TR‐O), orthorhombic‐ tetragonal (TO‐T), and tetragonal‐cubic (TC), gradually shift toward lower temperature with increasingxcontent. For (1‐x)BCZT‐(x)BCST ceramics, significantly improved dielectric and ferroelectric properties are observed, including relatively high dielectric constantεr≈ 1900–3300 (near room temperature),εr≈ 8800–12 900 (near Curie temperature), dielectric loss, tanδ≈ 0.01–0.02, remanent polarizationPr≈ 9.4–14 µC cm−2, coercive electric fieldEc≈ 2.5–3.6 kV cm−1. Further, high electric field‐induced strainS≈ 0.12–0.175%, piezoelectric charge coefficientd33≈ 296–360 pC N−1, converse piezoelectric coefficient ≈ 240–340 pm V−1, planar electromechanical coupling coefficientkp≈ 0.34–0.45, and electrostrictive coefficient (Q33)avg≈ 0.026–0.038 m4C−2are attained. Output performance with respect to mechanical energy demonstrates that the (0.6)BCZT‐(0.4)BCST composition (x= 0.4) displays better efficiency for generating electrical energy and, thus, the synthesized lead‐free piezoelectric (1‐x)BCZT‐(x)BCST samples are suitable for energy harvesting applications. The results and analyses point to the outcome that the (1‐x)BCZT‐(x)BCST ceramics as a potentially strong contender within the family of Pb‐free piezoelectric materials for future electronics and energy harvesting device technologies.

     
    more » « less
  5. ; ; ( , 2022 International Solid Freeform Fabrication Symposium)
    Additive manufacturing is advantageous for producing lightweight components while maintaining function and form. This ability has been bolstered by the introduction of unit lattice cells and the gradation of those cells. In cases where loading varies throughout a part, it may be necessary to use multiple lattice cell types, also known as multi-lattice structures. In such structures, abrupt transitions between geometries may cause stress concentrations, making the boundary a primary failure point; thus, transition regions should be created between each lattice cell type. Although computational approaches have been proposed, smooth transition regions are still difficult to intuit and design, especially between lattices of drastically different geometries. This work demonstrates and assesses a method for using variational autoencoders to automate the creation of transitional lattice cells. In particular, the work focuses on identifying the relationships that exist within the latent space produced by the variational autoencoder. Through computational experimentation, it was found that the smoothness of transition regions was higher when the endpoints were located closer together in the latent space. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email