2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024): Design of Lattices and Cellular Structures
Program Organizers: Joseph Beaman, University of Texas at Austin
Monday 1:30 PM
August 12, 2024
Room: 404
Location: Hilton Austin
Session Chair: Eric MacDonald, University of Texas at El Paso
1:30 PM
Design and Additive Manufacturing Micro-lattice Structures for Mechanical Metamaterials: Keun Park1; Minjung Ji1; Jeong-Hee Yoo1; 1Seoul National University of Science & Technology
Metamaterials represent artificially engineered micro-architectures aimed at demonstrating exceptional physical properties. Recent strides in additive manufacturing (AM) technology have significantly advanced the development of mechanical metamaterials, predominantly focusing on micro-lattice structures. In this study, we developed an efficient design methodology for boundary-conformal micro-lattice structures through isoparametric mapping, based on the finite element mesh information. Subsequently, these micro-lattice structures were harnessed for the design and fabrication of various mechanical metamaterials: (i) functionally-graded-lattice structures capable of extreme compliance variation were fabricated utilizing photo-polymerization type AM, employing photo-curable polyurethane resin; (ii) 3D chiral metamaterials exhibiting compression-twist behaviour were produced through photo-polymerization type AM, utilizing acrylic polyurethane resin; and (iii) thermal metamaterials characterized by high insulation and cooling capability were fabricated via powder-bed-fusion AM, employing SUS316 powders. These diverse applications underscore the versatility and practical utility of micro-architected mechanical metamaterials, showcasing their potential in the development of functional components beyond conventional limits.
1:50 PM
A Digital Exploration of the Mechanical Property Space of Self-stabilizing Dynamic Printable Foams: Brett Emery1; Kelsey Snapp2; Daniel Revier3; Vivek Sarkar3; Masa Nakura3; Keith Brown4; Jeff Lipton1; 1Northeastern University; 2Boston University ; 3University of Washington; 4Boston University
Foams are versatile by nature, and are used ubiquitously in applications ranging from padding and insulation to acoustic dampening. Previous work established that foams additively manufactured via Viscous Thread Printing (VTP) are capable of enabling a greater degree of control over many of the key mechanical properties of conventional foams such as Young’s modulus, fracture characteristics, and toughness while eliminating the need for chemical foaming agents. However, the relationship between input parameters and output properties was only accomplished via iterative empirical testing which limits generalizability and predictive control of desired output properties. Our work addresses this by combining high-throughput automated experimentation with machine learning to identify a subspace able to predict material behavior down to the stress-strain curve level. This predictive mapping was developed utilizing data collected from thermoplastic polyurethane (TPU) specimens and validated using polylactic acid (PLA) specimens suggesting inherent compatibility with any material suitable for filament-based 3D printing.
2:10 PM
Criteria-based Evaluation of Multi-lattice Structure Design Methods: Martha Baldwin1; Christopher McComb1; 1Carnegie Mellon University
Design for additive manufacturing (DfAM) continually seeks to leverage the flexibility inherent in additive manufacturing, primarily through weight reduction. One recent advancement in this field is the introduction of multi-lattice structures, which facilitate the creation of complex structures that achieve exceptional performance through the use of multiple lattice topologies. While various methodologies have been proposed for designing multi-lattice structures, there remains a lack of clarity on how to effectively compare these methods. In this work, we investigate the key design factors necessary for evaluating multi-lattice structure design methodologies. Our analysis reveals three recurring design criteria across existing literature: 1) lattice connectivity, 2) lattice diversity, and 3) physics-based interpolation of lattices. These criteria are discussed within the context of extant research on multi-lattice structure design.
2:30 PM
Smooth 3D Transition Cell Generation based on Latent Space Arithmetic: Xiaochen Yu1; Bohan Peng1; Ajit Panesar1; 1Imperial College London
Lattice structures with multiple unit cell types have diversified the property space by offering more design freedom in adjusting geometric parameters at the microscale level. It is essential to ensure connectivity and smooth transition among different cell types to avoid pre-mature failure. In this work, we propose a framework to generate the transition cell with latent space operations. Latent embedding is a low-dimensional representation of the original microstructure and could be retrieved by training a machine learning (ML) model called variational autoencoder (VAE). Different types of triply periodic minimal surface (TPMS) lattice were chosen as the targets to demonstrate the capability of the algorithm in handling complex 3D geometries within a physically restricted transition region. Both visual and quantitative evaluations will be provided to illustrate the connectivity and geometric similarity of the generated transition.
2:50 PM
Inverse Generation of Metamaterial using Graph Neural Network: Jier Wang1; Ajit Panesar1; 1Imperial College London
In this work, a framework that applies Graph Neural Networks (GNN) in generating the metamaterials with desired mechanical and thermal properties is presented. A GNN-based inverse generator is developed to facilitate the creation of truss lattices considering the input target properties. The graph-based representation of the truss lattice enables the model to handle diverse lattice topologies simultaneously, thereby significantly enhancing design flexibility for a more generally applicable inverse generator. GNN is also superior to pixel-based convolutional neural networks as they require smaller data sizes and fewer training parameters in the model. The effectiveness of this inverse generator is validated through comparison with the conventional de-homogenisation method, highlighting its advantages in reducing computational costs and expanding design variety.
3:10 PM Break
3:40 PM
Towards an Analytical Model for Multi-Objective Optimization of Cellular Materials: Tyler Smith1; Dhruv Bhate1; 1Arizona State University
The optimization of cellular materials is of great interest due to its potential to significantly enhance the performance of components compared to traditional manufacturing methods. Previous research has focused on optimizing mechanical performance, reducing weight, improving sustainability, and increasing reliability. However, there has been a lack of research on optimizing cellular materials for multi-objective or multi-physics performance, which is essential for applications in the aerospace, automotive, and medical industries. This study proposes a multi-objective optimization method that utilizes an analytical beam model to optimize cellular parameters to maximize strength, thermal performance, and weight reduction of metallic cellular materials. For this study, a honeycomb cellular structure is optimized and results from the analytical model are validated against FEA models to determine overall performance of the model.
4:00 PM
Benchmarking the Current State of Lattice Design Software for Additive Manufacturing: Maxime Mermillod-Blondin1; Alison Olechowski2; Christopher McComb1; 1Carnegie Mellon University; 2University of Toronto
A number of lattice design software tools have emerged, aiming to enable the full power of lattice structures for additive manufacturing. However, even with these software tools, applying a lattice to a component can be a complicated task due to its high complexity and the numerous approaches available. However, these software packages do facilitate the lattice design process and also ensure accuracy and reliability. This paper aims to provide an overview of the current landscape of lattice design software, with a particular focus on eight prominent platforms. Throughout the study, each software's features and functionalities were examined, shedding light on the main shared and distinguishing characteristics. Several notable patterns emerged during the analysis, revealing significant overlaps between certain software offerings. This observation suggests a shared goal among these platforms, wherein they strive to address common challenges and meet the same objectives in lattice design.
4:20 PM Cancelled
Manufacturability-Driven Inverse Design of Architected Soft Materials using Multi-Modulus Chemistry: Daniel Revier1; Thomas Wallin2; Sijia Huang3; Jeffrey Lipton4; 1University of Washington; 2MIT; 3Lawrence Livermore National Laboratory; 4Northeastern University
Advancements in polymer science and additive manufacturing (AM) have significantly impacted the development of mechanical metamaterials, enabling the fabrication of microstructures with unique behaviors such as auxetics or mechanical cloaking. Despite these advances, a comprehensive design and fabrication framework is absent which restricts the practical application of metamaterials. Our research introduces an inverse design tool that leverages a novel, multi-modulus polymer chemistry to create modular 2D metamaterials approaching the theoretical limits of linear elasticity. We utilize topology optimization (TO) to design unit cells that serve as the building blocks for more complex metamaterials. Our TO algorithm accounts for manufacturing process effects, such as polymer diffusion, effectively turning manufacturing constraints into design advantages. We successfully demonstrate the production of metamaterials with spatially varying Poisson’s ratios and Young’s moduli. This approach not only simplifies the design and fabrication process but also makes advanced metamaterial design accessible to a broader audience.
4:40 PM
Automated Design of Optimal Plate Lattice Structures Enabled by Machine Learning: Charles Wade1; Robert MacCurdy1; 1University of Colorado Boulder
Plate lattice structures offer a promising solution for impact absorption due to their tunable geometric parameters which can be optimized to improve energy absorption across a range of impact energies. This talk covers our work on automated design synthesis and surrogate modeling for optimizing these structures. We use a multi-objective heuristic optimization process with FEA simulations to discover Pareto-optimal designs among thousands of candidates. To accelerate this process, we train a neural network to predict impact absorption profiles, significantly speeding up the discovery of optimal lattice geometries. By using a smaller set of simulations for training, the network accurately predicts performance across diverse geometries and impact scenarios. This surrogate model, combined with the optimizer, expands the search space to millions of design candidates. We benchmark our plate lattices against established impact absorbing geometries, to demonstrate how automated design synthesis and surrogate modeling enable efficient optimization, offering superior tunability and performance.
5:00 PM
Design for Metal Powder Bed Fusion: Exploring Predicted Print Time and Cost Impacts of Lattice Structures
: Jennifer Brennan1; Luke Hanft2; 1Naval Nuclear Laboratory; 2Bechtel Plant Machinery, Inc.
Effective use of AM necessitates a comprehensive approach that considers design through final processing after the print. Each step has different cost drivers that can be used to optimize production costs through thoughtful design choices. For printing, two primary cost drivers are machine time and material cost. Designers often seek to reduce those through removal of unnecessary material, utilizing techniques such as topology optimization or lattice structures. However, lattice structures have inherent complexity that can impact print time and cost. This paper explores the impact of design choices on three types of lattice structures to be manufactured via laser-based powder bed fusion (LPBF) for a case study component. The type of lattice, unit cell size, and volume fraction of the lattice are observed to influence the print time of the final designs. The impacts of lattice structure design on print time and cost through build print time simulation are discussed.