#1: Modeling and Simulation of Next Generation 3D Printing Systems for Functionalized Materials with Machine-Learning System Design
Authors: Tarek Zohdi (zohdi@berkeley.edu)
Affiliation: UC Berkeley
Webpage: http://www.me.berkeley.edu/people/faculty/tarek-i-zohdi
Abstract: Within the last decade, several industrialized countries have stressed the importance of advanced manufacturing, such as 3D Printing (3DP), to their economies. The combination of rigorous material modeling theories and robotic control, coupled with dramatic increases of computational power, can potentially play a significant role in the analysis and design of many emerging, multistage, additive manufacturing systems. The goal of these systems is to build structures that are otherwise extremely difficult or impossible to construct using classical manufacturing methods. Ultimately, the objective of 3DP is to develop superior products, manufactured at lower overall operational costs. In many emerging 3DP systems, such processes involve attaching multi-material dispensers to robots, which then release functionalized, complex, multiphase, material mixtures in free-space. These approaches are becoming popular because they utilize preexisting widely-available, highly-programmable, robots, which have been developed for decades. However, often the release of a complex mixture in free-space is imprecise, thus electromagnetic field control has been proposed as one possible remedy to enhance the precision of such processes, by rapidly guiding the material to a desired target position. Thereafter, lasers are used to irradiate the deposited material to a desired state. The outline of this presentation is:
· To develop Machine Learning Algorithms to ascertain the appropriate combination of system parameters: robot kinematics, electromagnetic fields, laser-intensities, etc, needed to create desired complex structures.
· To present detailed modeling of the dynamic deposition of complex mixtures(Discrete Element Methods)
· To present detailed of modeling of laser processing of deposited mixtures (Computational Optics) and
· To present detailed modeling of continuum material behavior/performance (Digital-Image Computation).
Reference: Zohdi, T. I. (Book, 2018) Modeling and Simulation of Functionalized Materials for Additive Manufacturing and 3D Printing: Continuous and Discrete Media. Continuum and Discrete Element Methods. Springer International Publishing. For more information see http://cmmrl.berkeley.edu/77-2/
#2: FABRICATION AND MECHANICS OF ENGINEERED MICRO-GRANULAR CRYSTALS
Authors: Samira Chizari (samira3@ucla.edu1), Michael Porter (mikeporter87@gmail.com2), Miles Lim (mileslim@g.ucla.edu3), Sydney Austin (sydneyaustin6@gmail.com4), and Jonathan B. Hopkins (hopkins@seas.ucla.edu5)
Affiliation: Mechanical and Aerospace Engineering, University of California, Los Angeles (UCLA)
Abstract: The ability to engineer micro-granular crystals so that their constituent microspheres can be positioned within lattices as desired would enable unprecedented acoustic properties due to the nonlinear stiffness interactions between their microspheres. We propose an approach that utilizes hundreds of automated optical tweezers to simultaneously assemble hundreds of 1-4μm-diameter microspheres at their intended locations within 3D micro-granular crystals. We experimentally measure the dynamic behaviors of the fabricated crystals and use these measurements to inform a molecular dynamic simulation tool (LAMMPS) that can then be used to engineer the locations of the microspheres for achieving intended properties. Our simulation, fabrication, and characterization tools provide the most promising path toward enabling 3D micro-granular crystals that achieve advanced acoustic applications (e.g., armor that mitigates or redirects shock waves, acoustic lenses, sound scramblers, and acoustic logic gates). Our approach could also enable photonic applications for engineered crystals that consist of nano-sized spheres.
#3: MOVING FROM PROTOTYPING TO PRODUCTION – WORKFLOW CHALLENGES
Authors: Robert Yancey, (rnyancey@compforte.com)
Affiliation: Compforte, Dublin, CA
Abstract: Additive Manufacturing holds many application promises including faster design to production, lightweighting, customization, and agility. Over the 30+ years since stereolithography was first introduced as rapid prototyping, there are only a few applications which have used the technology in a production environment. Aside from some dental, medical, and aerospace use cases, the promise of additive manufacturing has not been realized. What is holding the technology back and what are the business and technical hurdles that are impeding the promise of moving from prototyping to production? This paper will explore the business and technical challenges that are affecting the additive manufacturing industry. At the heart of the technical challenges is the convergence of materials,
processing, and mechanics and the extreme number of variables that are now available to consider but also hamper the qualification and certification of additively manufactured parts. From a business
perspective, disruptions to supply chains, lack of knowledge and experience with additive manufacturing, and lack of reliable standards and inspection protocols are creating barriers. One
industrial application that can provide some guidance is the advanced composites industry for aerospace. Laminated composites emerged before additive manufacturing but there are many similarities since both technologies rely on layered manufacturing, both are influenced by material and processing choices, and both can have variability of material properties in the structure, especially between layers. A review of the laminated composite qualification and certification process will be provided with lessons learned that can be applied to the additive manufacturing industry.
#4: MACHINE LEARNING BASED MONITORING OF ADVANCED MANUFACTURING
Authors: Brian Giera(giera1@llnl.gov), Bodi Yuan (yuan11@llnl.gov), Albert Chu (chu29@llnl.gov), Phillip DePond (depond1@llnl.gov), Gabe Guss (guss2@llnl.gov), Du Nguyen (nguyen98@llnl.gov), Congwang Ye (ye4@llnl.gov), Will Smith (smith483@llnl.gov), Nik Dudukovic (dudukovic1@llnl.gov), Jennifer N. Rodriguez (rodriguez96@llnl.gov), Mitchell K. Shiflett (shiflett1@llnl.gov), James Lewicki (lewicki1@llnl.gov), Sara McMains (mcmains@berkeley.edu), and Manyalibo Matthewsmatthews11@llnl.gov)
Abstract:
As with most advanced manufacturing (AM) systems, analysis of AM sensor data currently occurs post-build, rendering process monitoring and rectification impossible. Supervised machine learning offers a route to convert sensor data into real-time assessments; however, this requires a wealth of labeled sensor data that traditionally is too time-consuming and/or expensive to assemble. In this work, we solve this critical issue in a variety of AM systems. We develop and implement machine learning (ML) algorithms for the purposes of automated quality assessment and, in some cases, rectification. We discuss ML-based algorithms capable of automated detection in a host of AM technologies such as Laser Powder Bed Fusion, Direct Ink Write, and also microfluidic platforms that are used for feedstock production. The common thread within these systems is that routinely collected sensor data (e.g. high-speed video, pyrometers, etc.) contains pertinent information about the state of the system that can be converted into actionable information in real-time via ML. Successful implementation of these machine learning algorithms will reduce time and cost during process by automating quality assessment and lead to process control.
This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.
#5: MATERIALS by DESIGN: THREE-DIMENSIONAL NANO-ARCHITECTED METAMATERIALS
Authors: Julia R. Greer (jrgreer@caltech.edu), Xiaoxing Xia (xxia@caltech.edu), Andrey Vyatskikh (avyatski@caltech.edu), Carlos Portela (cportela@caltech.edu), Daryl Yee (daryl@caltech.edu);
Affiliation: Division of Engineering and Applied Sciences, California Institute of Technology (Caltech)
Abstract: Creation of extremely strong and simultaneously ultra lightweight materials can be achieved by incorporating architecture into material design. We design and fabricate three-dimensional (3D) nano-architected materials that exhibit superior and often tunable thermal, photonic, electrochemical, and mechanical properties at extremely low mass densities (lighter than aerogels), which renders them useful and enabling in many scientific and technological pursuits. Dominant properties of such meta-materials are driven by their multi-scale nature: from characteristic material microstructure (atoms) to individual constituents (nanometers) to structural components (microns) to architectures (millimeters and above).
To harness beneficial properties of 3D nano-architected meta-materials, it is critical to assess their properties at each relevant scale while capturing overall structural complexity. Our research is focused on fabrication and synthesis of architected materials using 3D lithography, nanofabrication, and additive manufacturing (AM), as well as on investigating their mechanical, biochemical, electrochemical, electromechanical, and thermal properties as a function of architecture, constituent materials, and microstructural detail. We strive to uncover the synergy between internal atomic-level microstructure and nano-sized external dimensionality, where competing material- and structure-induced size effects govern these properties. Specific discussion topics also include their applications in chemical and biological devices, ultra lightweight energy storage systems, damage-tolerant fabrics, and smart, multi-functional materials.
#6: Modeling and Simulation of the Energy Consumption of Additive Manufacturing
Authors: Jan C. Aurich (jan.aurich@mv.uni-kl.de), Li Yi (li.yi@mv.uni-kl.de)
Affiliation: TU Kaiserslautern
Abstract: Additive manufacturing (AM) is regarded as a promising technology to increase the resource efficiency of manufacturing systems and processes, since it requires no forming tools and produces far less scrap material than machining. However, recent studies have shown that the energy efficiency of AM unit processes can be 1 or 2 orders of magnitude higher than conventional cutting or molding processes. Hence, the development of new methods, concepts, and tools for calculating and evaluating energy consumptions of AM processes in the design phase is a new research task. With that in mind, this talk first introduces a function-oriented modeling method for mapping the energy flow of AM machines. The method is based on the bond graph approach which suggests using the power flow-based viewpoint to map the physical system dynamics. Moreover, this modeling method is applied to a selective laser melting (SLM) machine at TU Kaiserslautern in order to validate the feasibility of the method. Second, a simulation tool is developed based on the bond graph of the SLM-machine using MATLAB/Simulink® as platform. The developed simulation tool can be further applied for energy performance quantification and evaluation of the SLM process.
#7: Laser Fabricated Architected Materials for Structural and Biological Applications
Authors: Zacharias Vangelatos,1,2 Vasileia Melissinaki,3 Maria Farsari,3 Kyriakos Komvopoulos,1,4 Zhen Ma,5 Costas Grigoropoulos1,2
Affiliations:
1Department of Mechanical Engineering, University of California, Berkeley, California 94720, USA
2Laser Thermal Laboratory, Department of Mechanical Engineering, University of California, Berkeley, California 94720, USA
3Institute of Electronic Structure and Laser (IESL), Foundation of Research and Technology, Hellas (FORTH), Heraklion 70013, Crete, Greece
4Surface Science and Engineering Laboratory & Computational Surface Mechanics Laboratory, Department of Mechanical Engineering, University of California, Berkeley, California 94720, USA
5Department of Biomedical & Chemical Engineering and Syracuse Biomaterials Institute, Syracuse University, New York 13244, USA
Abstract: We present recent work on the laser-aided fabrication of architected structural and biological materials. Advances in three-dimensional (3D) printing technologies have enabled the fabrication of complex architected material structures even at the microscale. We fabricated by multiphoton lithography 3D mechanical metamaterial structures with select unit cells incorporating vacancies, having as the principal objective to control failure and increase the strain energy capacity of the structure. Our findings illustrate the importance of strategically placing vacancies in the microlattices of metamaterial structures to control the overall mechanical behavior and greatly increase the strain energy dissipation. We have also introduced a new design approach for 3D mechanical metamaterials with enhanced strain hardening and energy absorption, fabricated at the microscale by multi-photon lithography. This was accomplished by intertwining simple polyhedra to create more complex geometries in 3D space. With this concept, plastic deformation could be controlled through localized buckling of select lattice members, demonstrating remarkably improved performance with respect to both strain hardening and energy dissipation of the structure. Controlling the spatial arrangement of biomaterials and living cells provides the foundation for fabricating complex biological systems. We have applied multi-photon polymerization and laser ablation to explore applications in biological science and biomedical engineering. These technologies enable the precise manipulation of in vitro cellular microenvironments and the ability to engineer functional tissue constructs, which serve in regenerative medicine, pharmacology, and basic cell biology studies.
#8: A PARTICLE SCALE MODEL AND MACHINE LEARNING APPROACHES FOR TUNING THE ELECTRO-CHEMICAL, MECHANICAL AND THERMAL PHYSICS OF BATTERY ELECTRODES
Authors: Z. Wang (wzhenlin@umich.edu), B. Martin (btmartin@umich.edu) and K. Garikipati (krishna@umich.edu)
Affiliation: University of Michigan
Abstract: We have developed a coupled continuum formulation for the electrostatic, chemical, thermal, mechanical and fluid physics in battery materials. Our treatment is at the particle scale, at which the active particles held together by carbon-binders, the porous separator, current collectors and the perfusing electrolyte are explicitly modeled. Starting with the description common to the field, in terms of reaction-transport partial differential equations for ions, variants of the classical Poisson equation for electrostatics, and the heat equation, we introduce solid-fluid interaction to the problem. Our main contribution to the forward problem is to model the electrolyte as an incompressible fluid driven by elastic, thermal and lithium intercalation strains in the active material. Our treatment is in the finite strain setting, and uses the Arbitrary Lagrangian-Eulerian (ALE) framework to account for mechanical coupling of the solid and fluid. With the forward problem we are able to carry out detailed computational studies of the influence of solid-fluid interaction and magnitude of intercalation strain upon porosity evolution, ion distribution and electrostatic potential fields in the cell. Additionally, we have developed a collection of machine learning methods, including deep and recurrent neural networks, as one step on the route to designing electrodes tuned to the desired behavior in terms of voltage response and mechanical integrity.
#9: COMPUTATIONAL APPROACHES AND EXPERIENCE WITH PART-SCALE MODELING OF METAL POWDER BED FUSION |
Authors: R.M. Ferencz, (ferencz1@llnl.gov), R.K. Ganeriwala, (ganeriwala1@llnl.gov), R.M. Vignes, (vignes2@llnl.gov) Affiliation : Lawrence Livermore National Laboratory
Abstract: Many researchers are exploring thermomechanical modeling of the laser powder bed fusion (PBF) process to gain insights into residual stresses and distortions arising during this metal additive manufacturing process. This challenging simulation encompasses a range of scales: temporally from milliseconds for local material transformation to hours and days for overall part fabrication; spatially from 100 microns for the local melt pool versus overall part dimensions. Various approaches are being pursued to identify and implement abstractions rendering the problem computationally tractable while still yielding results providing meaningful insights. We are adapting a general purpose, nonlinear thermal-mechanical finite element code to model the PBF process. First touching upon some of our earlier successes modeling PBF for a stainless steel, we survey more recent successes and challenges we have encountered as we attempt to model more general configurations and material systems. These efforts have motivated reexamination of our assumptions and approaches, pointing the way toward more general and robust modeling.
#10: 3-D AM printing induced material characteristics of 316L stainless steel prototypes and its impact on mechanical properties
Authors: Nancy Yang, Rick Karnesky, and Josh Yee
Affiliation: Sandia National Laboratories, CA
LENS & LPBF INDUCED METALLURGICAL CHARACTERISTICS
Authors: Nancy Yang (NYYANG@SANDIA.GOV)
Affiliation: Sandia National Laboratories, CA
Abstract: 3-D Additive Manufacturing (AM) provides a revolutionary potential to print complex engineering components that are impossible to fabricate by traditional techniques. 3-D AM metal printing using Laser-Engineered-Net-Shaping (LENS) or Powder Bed Fusion (PBF) induced unique metallurgical characteristics, which impact mechanical properties. The PBF/LENS printed 316L stainless steel parts possess ultra-fine solidification structures, associated with solidification induced residual stress. In this workshop, we will present thermal stability as well as thermal evolution of microstructure for 3-D LEN- and PBF- 316L stainless steel. In addition, we will discuss the correlation between microstructure on engineering behavior and properties.
#11: DESIGN, SIMULATION & PRE-PROCESSING WORKFLOWS FOR LATTICE COMPONENTS
Authors: David Macknelly (david.macknelly@awe.co.uk)
Affiliation: Atomic Weapons Establishment UK
Abstract: The design, simulation and manufacturing pre-processing of large or complex lattice structures continues to provide numerous challenges to engineers – especially when making relatively large structural parts.
This presentation will detail how the use of new commercial software packages has begun to help streamline the complex workflow for structures that make use of radially conformal lattices.
It will also detail some of the analysis techniques developed to assess the structural integrity of these parts in a computationally efficient manner.
Finally the presentation will outline some promising solutions to manufacturing pre-processing and process parameter optimization in order to create high quality, structurally robust parts.
#12: MULTISCALE MODELING OF MICROSTRUCTURE AND PERFORMANCE IN ADDITIVELY MANUFACTURED PARTS
Authors: Kyle Johnson1 (kyljohn@sandia.gov), Theron Rodgers1 (trodger@sandia.gov), John Emery1 (jmemery@sandia.gov), Joe Bishop1 (jebisho@sandia.gov), and Bradley Jared1(bhjared@sandia.gov)
Affiliation: 1Sandia National Laboratories, Albuquerque, NM
Abstract: Metal Additive Manufacturing (AM) has the potential to revolutionize manufacturing processes by offering benefits such as increased geometric complexity, rapid prototyping, and locally-tailored properties. However, barriers to widespread adoption still exist in the form of detrimental effects including high residual stresses, non-uniform grain microstructures, and variability in material properties. These phenomena often make performance and failure prediction difficult. This talk will present ongoing modeling efforts at Sandia National Laboratories in both process and performance modeling. First, process models at multiple length scales will be presented that capture accurate thermal histories, which are then used in a Monte Carlo method to simulate three-dimensional microstructure evolution. Next, both high fidelity and reduced order models for residual stress will be presented and compared to experimental measurements. Finally, performance predictions of AM parts will be presented using a viscoplastic continuum damage model accounting for as-built material properties and defects. This presentation will discuss progress to date as well as future plans along the path to accelerating part design, production, and qualification.
#13: COMPUTATIONAL DESIGN OPTIMIZATION
Authors: Daniel A. Tortorelli (Tortorelli2@llnl.gov)
Affiliation: Lawrence Livermore National Laboratory
Abstract: Our ability to manufacture now greatly exceeds our ability to design. Engineers are no longer merely inconvenienced by inefficient trial-and-error design; rather, they are nearly incapacitated by the vast space of possible designs afforded by Advanced Manufacturing (AM) technologies. There are no systematic methods to design systems with such complexity, especially those that exhibit nonlinear, transient, multiscale, and multiphysics phenomena with uncertain behavior. To address this challenge we are developing algorithms that can optimize immensely complex systems. The complexity comes from two sources, design and physics. Design complexity refers to the intricate shape and material layouts that are made possible by today’s AM technologies; it can take the form of structural composites with intricate morphologies. It also refers to the multifunctional metrics that we optimize.. And finally, it refers to constraints dictated by the AM processes to ensure manufacturable designs. Physics complexity comes from the mathematical models that are used to predict the performance of our designs. Such models require the solution of PDEs that contain complicated nonlinearities, transients, multiple scales, multiple physics, and uncertainties. Because our design and physics DOF are in excess of 100 million, we must develop efficient, large-scale HPC algorithms.
#14: MULTISCALE DESIGN TOOLS TO CONTROL WAVE PROPAGATION IN ARCHITECTURED MATERIALS
Authors: Caglar Oskay (caglar.oskay@vanderbilt.edu) and Ruize Hu (ruize.hu@vanderbilt.edu)
Affiliation: Civil and Environmental Engineering Department,
Vanderbilt University
Abstract: Architectured composites with tailored microstructures and material
properties such as phononic crystals and acoustic metamaterials exhibit
extraordinary capabilities in controlling acoustic and elastic waves.
Controlling the wave propagation characteristics is typically achieved through
manipulating the band structure of the material. Coupled with the ability to
prototype composite microstructures through additive manufacturing,
microstructural topology and property optimizations allow identification of
microstructures with tailored band structure. Controlling wave propagation characteristics
in realistic structural (i.e., macroscopic) configurations introduce additional
complications due to the interactions between the microstructure, structural
geometry, structural boundaries and the multiple modes of propagating waves.
This naturally calls for a multiscale design paradigm, largely facilitated by
the advances in additive manufacturing.
In this talk, we introduce multiscale
computational methods for wave propagation in structures made of architectured
materials with a view towards material-structure co-design. The proposed
approaches rely on the principles of homogenization and employ model order
reduction approaches to reduce the computational cost of performing structural
scale simulations. In particular, we focus on accurately capturing the wave
dispersion and band gap phenomena as a function of the microstructural
topology. The capabilities of the proposed methods are demonstrated in the
context of architectured composites subjected to multimodal wave propagation.
#15: Deformation and Damage Mechanisms in Ceramic Nano-Architected Metamaterials
Authors: Lorenzo Valdevit (valdevit@uci.edu), Jens Bauer (jens.bauer@uci.edu), Anna Guell Izard (aguelliz@uci.edu), Cameron Crook (ccrook@uci.edu)
Affiliation: University of California, Irvine
Abstract: Pyrolysis of additively manufactured polymeric preforms is quickly emerging as an ideal process for the fabrication of ceramic mechanical metamaterials and structures of complex shapes. When the polymeric precursors are fabricated at the nanoscale, virtually defect-free ceramic metamaterials ensue, which can exhibit unprecedented combinations of specific strength and strain to failure. Lattice and shell-based architected materials are fabricated by two-photon polymerization Direct Laser Writing (DLW) using a Nanoscribe Photonics Professional GT printer. Subsequently, the polymeric preforms are pyrolyzed in vacuum, resulting in a largely amorphous carbon material. The effects of printing parameters on the degree of conversion of the polymer, its mechanical properties, and the mechanical properties of pyrolytic carbon are investigated by electron microscopy and Raman spectroscopy. We demonstrate that under the right printing and pyrolysis conditions and sample dimensions, the carbon base materials can achieve exceptional combinations of ultimate strength and strain to failure. Subsequently, we fabricate and characterize ceramic mechanical metamaterials with various topologies and relative densities and show that these nano-architected materials possess unique combinations of mechanical properties. We also show that with proper choice of topology, ceramic nano-architected materials can exhibit progressive failure, dissipating large amounts of energy. Finally, opportunities for scalability are discussed.
#16: 3D PRINTABLE BIOINKS FOR SOFT TISSUE ENGINEERING
Authors: 1Grace D O’Connell (g.oconnell@berkeley.edu,),1Gabriel R. López-Marcial (gabriel_lopezmarcial@berkeley.edu), 2Jeanette M. García (jmgarcia@us.ibm.com,)
Affiliations: 1Department of Mechanical Engineering, UC Berkeley, 2IBM Research
Abstract: Hydrogels are useful materials as scaffolds for tissue engineering applications. Using hydrogels with additive manufacturing techniques has typically required the addition of techniques such as cross-linking or printing in sacrificial materials that negatively impact tissue growth to remedy inconsistencies in print fidelity. Thus, there is a need for bioinks that can directly print cell-laden constructs. In this study, agarose-based hydrogels commonly used for cartilage tissue engineering were compared to Pluronic, a hydrogel with established printing capabilities. Moreover, new material mixtures were developed for bioprinting by combining alginate and agarose. We compared mechanical and rheological properties, including yield stress, storage modulus, and shear thinning, as well as construct shape fidelity to assess their potential as a bioink for cell-based tissue engineering. The rheological properties and printability of agarose−alginate gels were statistically similar to those of Pluronic for all tests (p > 0.05). Alginate−agarose composites prepared with 5% w/v (3:2 agarose to alginate ratio) demonstrated excellent cell viability over a 28-day culture period (>∼70% cell survival at day 28) as well matrix production over the same period. Therefore, agarose−alginate mixtures showed the greatest potential as an effective bioink for additive manufacturing of biological materials for cartilage tissue engineering.
#17: Microstructure-property development during Directed Energy Deposition of Austenitic Stainless Steels
Authors: Chris San Marchi1 (cwsanma@sandia.gov), Thale R. Smith1 (trsmit@sandia.gov), Chris D’Elia2 (crdelia@ucdavis.edu), Lauren Beghini1 (llbeghi@sandia.gov), Michael Stender1 (mstende@sandia.gov), Joshua D. Sugar1 (jdsugar@sandia.gov), Michael R. Hill2 (mrhill@ucdavis.edu), Julie M. Schoenung3 (julie.schoenung@uci.edu),
Affiliations: 1Sandia National Laboratories, Livermore CA, 2University of California Davis, 3University of California Irvine
Abstract: The development of microstructure and properties during metal additive manufacturing is the result of the complex thermomechanical history imposed on the material. In the case of directed energy deposition (DED) of austenitic stainless steels, solidification and subsequent thermomechanical deformation strongly influence the microstructural evolution and resulting properties of the deposited material. While knowledge of analogous conventional processes like welding provide some insight to the microstructure-property relationships, the distinct length and time scales of DED must be considered to understand the unique multiscale microstructures that develop. Importantly, the large transient thermal gradients during deposition induce a complex elastic-plastic strain history, nonuniformity of mechanical properties (induced in part by plastic strains) and substantial residual stresses (intrinsically elastic). In this talk, DED of austenitic stainless steels is discussed with attention to microstructural evolution and strengthening mechanisms. The important strengthening mechanisms and their relative distribution in deposited structures is emphasized, along with the need for computational tools that can inform the magnitude and distribution of strengthening and residual stresses in complex DED structures. Examples of the range of strengthening that can be achieved in DED austenitic stainless steel are shown, along with benchmark fatigue and fracture behavior, demonstrating the potential for novel, high-strength components using this technology.
Sandia National Laboratories is a multi-program laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC., a wholly owned subsidiary of Honeywell International, Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA-0003525.
#18: Additive Manufacturing and Computational Design for Next Generation Lithium-ion Batteries
Author: Corie L. Cobb (clcobb@uw.edu)
Affiliation: University of Washington, Department of Mechanical Engineering
Abstract: Technology progressions in portable electronics and electric vehicles have motivated a shift in Lithium-ion (Li-ion) battery manufacturing to accommodate safer, higher power and energy materials with more efficient packaging via Additive manufacturing (AM). AM encompasses a suite of fabrication methods that are currently changing the way we design and manufacture products. This talk will review current research on AM for multi-material printing of Li-ion batteries with increased energy and power density. In addition, our research group’s initial progress in fabrication and computational modeling of non-flammable solid-state Li-ion batteries will be highlighted. Through both experimental and computational approaches, we are developing new design and AM methods for scalable 3D Li-ion battery architectures. 3D battery architectures mitigate conductivity and interfacial resistance issues which often degrade battery performance, thereby increasing potential energy and power gains over state-of-the-art Li-ion batteries. Key components that are needed to enable a robust computation and AM fabrication framework will be presented.
#19: OPTIMIZING ELECTRODES FOR ENERGY STORAGE
Authors: Victor A. Beck, Todd Weisgraber, Anna N. Ivanovskaya, Swetha Chandrasekaran, Bryan D. Moran, Seth E. Watts, Dan A. Tortorelli, Juergen Biener, Michael Stadermann, Eric B. Duoss, Marcus A. Worsley
Affiliation: Lawrence Livermore National Laboratory
Abstract: Two-dimensional (2D) nanomaterials, such as graphene and transition metal dichalcogenides, hold extraordinary promise for application in a number of electrochemical technologies. Electrochemical energy storage (EES) devices, such as lithium-ion batteries, flow batteries, and supercapacitors, in particular, have seen 2D materials integrated into various components with exciting results. In general, EES devices are emerging as primary power sources for global efforts to shift energy dependence from limited fossil fuels towards sustainable and renewable resources. These EES devices, while renowned for their high energy or power densities, portability, and long cycle life, are still facing significant performance hindrance due to manufacturing limitations. One major obstacle is the ability to engineer macroscopic components that possess designed and highly resolved microstructures with optimal performance, via controllable and scalable manufacturing techniques. 3D printing covers several additive manufacturing methods that enable well-controlled creation of functional materials with 3D architectures, representing a promising approach for fabrication of next-generation EES devices with high performance. Here, we present recent work to a) develop modeling and optimization algorithms that determine the optimal electrochemical cell geometries for various performance objectives (e.g. maximize current, minimize pressure drop, etc.) and b) fabricate 3D functional electrodes utilizing 3D printing-based methodologies. Specifically, the framework of the 3D printing techniques such as projection microstereolithography and direct ink writing are described, as well as the details of respective feedstock development efforts. Finally, characterization of the 3D-printed electrodes and their performance in various EES applications (e.g. supercapacitors and batteries) will be compared with predicted performance and discussed.
This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.
#20: COMPUTATIONAL PROCESS MODELS FOR POLYMER ADDITIVE MANUFACTURING
Authors: Todd H. Weisgraber (tweisgraber@llnl.gov)
Affiliation: Lawrence Livermore National Laboratory, Livermore, CA 94550
Abstract: Photopolymerization additive manufacturing techniques, including stereolithography, direct laser writing, and more recent volumetric approaches provide reliable printing of complex architectures suitable for structural, optical, and biological applications. Advancing the quality and resolution, as well as predictive design capabilities, requires a detailed understanding of the physical processes generating the crosslinked polymer network, and how differences in resin chemistries affect macroscopic properties. We present our computational models based on a mathematical description of polymerization chemistry and transport at the continuum level. The disparate length and time scales involved present unique modeling challenges and we demonstrate our approach to producing accurate and efficient simulations. The ability to predict the local degree of polymerization, inhibition effects, and the feasibility of part-scale computations are discussed. By coupling the simulations with mathematical optimization techniques, we can determine the appropriate build parameters to improve part quality. Beyond the continuum scale, we are also pursuing reactive coarse-grained molecular dynamics simulations with templating approaches to enable more informed resin design for these processes.
#21: OBSERVATIONS REGARDING THE POSSIBILITIES FOR HANDLING “THE TIME SITUATION” FOR PART SCALE LPBF MODELING
Authors: Neil Hodge (hodge3@llnl.gov),Rishi Ganeriwala (ganeriwala1@llnl.gov), Robert Ferencz (ferencz1@llnl.gov)
Affiliation: Lawrence Livermore National Laboratory
Abstract: Laser powder bed fusion (LPBF) is a manufacturing process which can
realize significant benefits over traditional manufacturing processes,
including significantly shortened time between design and manufacture, and the
ability to create parts with much more geometric complexity than has previously
been tenable. Many researchers have turned to modeling to predict
quantities of interest as a function of the process parameters. However,
the extreme spatio-temporal scales over which these processes operate makes it
difficult to calculate high quality model solutions in reasonable wall clock
times.
While both the spatial and temporal problems in
LPBF are clearly “multi-scale”, the temporal scales often span more
orders of magnitude than the spatial scales. Many existing finite element
codes already employ parallelism far past the point of diminishing returns for
the spatial portion of the problem. However, the temporal problem has
received much less attention. These two facts result in a situation where
there are clear performance gains to be made regarding the temporal portion of
the calculation. This presentation will discuss certain aspects of the
physical problem and the implications on model definition and performance,
comment on why this portion of the problem space has been largely neglected by
the modeling community, and present some initial results of one possible
solution to “the time situation” for part-scale LPBF modeling.
#22: FIELD-ASSISTED ASSEMBLY AND PRINTING OF FUNCTIONAL MATERIALS
Authors: Matthew R Begley (mrbegley@ucsb.edu), Daniel Gianola (gianola@ucsb.edu), Drew Melchert (dsmelchert@ucsb.edu),
Affiliation: Materials Department, University of California, Santa Barbara
Abstract: Field-assisted assembly of multi-phase materials holds promise for numerous applications, including flexible composites, patterning of cells in extracellular matrix in synthetic tissue, controlling ion transport in batteries, etc. To illustrate this, this talk will first illustrate fabrication of flexible, anisotropic conductors using acoustic assembly of conductive particles in photocurable polymer fluids. These structured materials dramatically lower the critical particle concentration associated with percolating networks, demonstrating that field-assisted assembly can be used to convert non-functional ‘inks’ into functional structures. This talk will then describe key scaling relationships governing particle motion driven by external fields, and outline key time scales that must be balanced to achieve ‘on-the-fly’ ordering during printing. These time scales can be conveniently expressed in dimensionless forms involving printing process parameters, physical properties of the ink and nozzle dimensions and used to create printing regime maps that avoid jamming and provide effective control of microstructure. A variety of in-situ and ex-situ observations of direct-write printing with acoustic focusing will then be used to illustrate the opportunities and challenges of this pathway to fabricating functional materials and structures.
#23: PROGRAMMABLE SELF-ASSEMBLY OF 3D PRINTED PARTICLES
Authors: Wendy Gu, (xwgu@stanford.edu), David Doan, (ddoan@stanford.edu)
Affiliation: Mechanical Engineering, Stanford University
Abstract: Self-assembly is the formation of complex structures from component parts without the application of external forces. The self-assembly of colloidal nano and microparticles has resulted in architected materials with unique mechanical, optical, magnetic and electronic properties. Yet, these studies are limited to simple particle shapes, such as spheres, platelets and cubes, that can be made using chemical synthesis or templating techniques. The ability to fabricate microscale particles of arbitrary shape would greatly increase the variety and complexity of the structures that can be formed, and lead to an improved understanding of the fundamental principles behind self-assembly processes.
Here, two-photon lithography is used to print arrays of 3D microparticles of arbitrary shape in microfluidic cells. Using this approach, regular polyhedra (e.g. spheres, cubes, tetrahedra, cuboctahedra) can be printed alongside irregular polyhedra (e.g. concave cube), or highly anisotropic structures. The movement of the microparticles within the cell is tracked using optical microscopy, and the diffusion behavior is related to size and shape. Heating and solvent environment are used to manipulate interparticle forces and induce self-assembly. Entropic, depletion forces govern interparticle interactions, and scale with the volume of solvent that is sandwiched between two particles.
#24: VOLUMETRIC ADDITIVE MANUFACTURING: ENABLING NEW HORIZONS IN MATERIALS AND FABRICATION
Authors: M. Shusteff1 (shusteff1@llnl.gov), C. Spadaccini1 (spadaccini2@llnl.gov) H.K. Taylor2 (hkt@berkeley.edu), H. Heidari2 (heidari@berkeley.edu) , B. E. Kelly2 (bkelly14@berkeley.edu), I. Bhattacharya2 (indrasen@berkeley.edu)
Affiliations: 1Lawrence Livermore National Laboratory, Livermore, CA, USA, 2University of California, Berkeley, CA, USA
Abstract: Most additive manufacturing (AM – also called 3D printing) processes create parts in a point-wise or layer-by-layer fashion, which leads to limited fabrication speed, inability to print certain geometries and poor surface quality. These challenges can be overcome by an AM approach that generates a complex 3D volume as its unit operation. With this goal in mind, we have developed a new AM framework that we call volumetric additive manufacturing (VAM), which leaps over the traditional limitations of layer-by-layer approaches to 3D printing. VAM produces structures “all at once” by creating 3D light patterns within photopolymer resins to concurrently cure all points within a target geometry. The structures form on timescales of seconds to minutes, with no need for support material or a substrate. We have demonstrated an implementation of VAM inspired by computed tomography that allows nearly arbitrary geometries by exposure from multiple angles. Here we discuss the key parameters of the optical system and the properties of photosensitive pre-polymer resins that enable this technology. In addition to the challenges for further development, we assess the broader opportunities in materials that become accessible within the volumetric framework.
#25: Generative Design and General Motors: The future of Automotive Vehicle Structures
Authors: Ebot Ndip-Agbor (ebot.ndip-agbor@autodesk.com)
Abstract: We will review how generative design and additive manufacturing are helping automakers, such as GM, improve the performance of automotive structures. Generative design coupled with structural and additive manufacturing simulations, can accelerate the product development workflow and lead to significantly lighter, yet strong, structures.