Optimal and continuous multilattice embedding

Metaheuristic aided structural topology optimization method for heat sink design with low electromagnetic interference

This study investigates the application of the Metaheuristic Aided Structural Topology Optimization (MASTO) method as a novel approach to address the multiphysics design challenge of creating a heat sink with both high heat conductivity and minimal Electromagnetic Interference (EMI). A distinctive 2D layout with elongated fins is examined for electromagnetic traits, highlighting resonance-related EMI concerns. MASTO proves to be a valuable tool for navigating the complex design space, yielding thoughtfully optimized solutions that harmonize efficient heat dissipation with effective EMI control. By merging simulation findings with practical observations, this study underscores the potential of the MASTO method in achieving effective designs for intricate multiphysics optimization problems. Specifically, the method's capacity to address the complex interplay of heat transfer with convection and the suppression of electromagnetic emissions is showcased. Moreover, the study demonstrates the feasibility of translating these solutions into tangible outcomes through manufacturing processes.

Thermodynamically consistent concurrent material and structure optimization of elastoplastic multiphase hierarchical systems

The concept of concurrent material and structure optimization aims at alleviating the computational discovery of optimum microstructure configurations in multiphase hierarchical systems, whose macroscale behavior is governed by their microstructure composition that can evolve over multiple length scales from a few micrometers to centimeters. It is based on the split of the multiscale optimization problem into two nested sub-problems, one at the macroscale (structure) and the other at the microscales (material). In this paper, we establish a novel formulation of concurrent material and structure optimization for multiphase hierarchical systems with elastoplastic constituents at the material scales. Exploiting the thermomechanical foundations of elastoplasticity, we reformulate the material optimization problem based on the maximum plastic dissipation principle such that it assumes the format of an elastoplastic constitutive law and can be efficiently solved via modified return mapping algorithms. We integrate continuum micromechanics based estimates of the stiffness and the yield criterion into the formulation, which opens the door to a computationally feasible treatment of the material optimization problem. To demonstrate the accuracy and robustness of our framework, we define new benchmark tests with several material scales that, for the first time, become computationally feasible. We argue that our formulation naturally extends to multiscale optimization under further path-dependent effects such as viscoplasticity or multiscale fracture and damage.

Embedded 3D Printing of Multimaterial Polymer Lattices via Graph-Based Print Path Planning

Recent advances in computational design and 3D printing enable the fabrication of polymer lattices with high strength-to-weight ratio and tailored mechanics. To date, 3D lattices composed of monolithic materials have primarily been constructed due to limitations associated with most commercial 3D printing platforms. Here, freeform fabrication of multi-material polymer lattices via embedded three-dimensional (EMB3D) printing is demonstrated. An algorithm is developed first that generates print paths for each target lattice based on graph theory. The effects of ink rheology on filamentary printing and the effects of the print path on resultant mechanical properties are then investigated. By co-printing multiple materials with different mechanical properties, a broad range of periodic and stochastic lattices with tailored mechanical responses can be realized opening new avenues for constructing architected matter.

Triclinic Metamaterials by Tristable Origami with Reprogrammable Frustration

Geometrical-frustration-induced anisotropy and inhomogeneity are explored to achieve unique properties of metamaterials that set them apart from conventional materials. According to Neumann's principle, to achieve anisotropic responses, the material unit cell should possess less symmetry. Based on such guidelines, a triclinic metamaterial system of minimal symmetry is presented, which originates from a Trimorph origami pattern with a simple and insightful geometry: a basic unit cell with four tilted panels and four corresponding creases. The intrinsic geometry of the Trimorph origami, with its changing tilting angles, dictates a folding motion that varies the primitive vectors of the unit cell, couples the shear and normal strains of its extrinsic bulk, and leads to an unusual Poisson effect. Such an effect, associated with reversible auxeticity in the changing triclinic frame, is observed experimentally, and predicted theoretically by elegant mathematical formulae. The nonlinearities of the folding motions allow the unit cell to display three robust stable states, connected through snapping instabilities. When the tristable unit cells are tessellated, phenomena that resemble linear and point defects emerge as a result of geometric frustration. The frustration is reprogrammable into distinct stable and inhomogeneous states by arbitrarily selecting the location of a single or multiple point defects. The Trimorph origami demonstrates the possibility of creating origami metamaterials with symmetries that are hitherto nonexistent, leading to triclinic metamaterials with tunable anisotropy for potential applications such as wave propagation control and compliant microrobots.

Growth rules for irregular architected materials with programmable properties

Biomaterials display microstructures that are geometrically irregular and functionally efficient. Understanding the role of irregularity in determining material properties offers a new path to engineer materials with superior functionalities, such as imperfection insensitivity, enhanced impact absorption, and stress redirection. We uncover fundamental, probabilistic structure-property relationships using a growth-inspired program that evokes the formation of stochastic architectures in natural systems. This virtual growth program imposes a set of local rules on a limited number of basic elements. It generates materials that exhibit a large variation in functional properties starting from very limited initial resources, which echoes the diversity of biological systems. We identify basic rules to control mechanical properties by independently varying the microstructure's topology and geometry in a general, graph-based representation of irregular materials.

Optimally-Tailored Spinodal Architected Materials for Multiscale Design and Manufacturing

Spinodal architected materials with tunable anisotropy unify optimal design and manufacturing of multiscale structures. By locally varying the spinodal class, orientation, and porosity during topology optimization, a large portion of the anisotropic material space is exploited such that material is efficiently placed along principal stress trajectories at the microscale. Additionally, the bicontinuous, nonperiodic, unstructured, and stochastic nature of spinodal architected materials promotes mechanical and biological functions not explicitly considered during optimization (e.g., insensitivity to imperfections, fluid transport conduits). Furthermore, in contrast to laminated composites or periodic, structured architected materials (e.g., lattices), the functional representation of spinodal architected materials leads to multiscale, optimized designs with clear physical interpretation that can be manufactured directly, without special treatment at spinodal transitions. Physical models of the optimized, spinodal-embedded parts are manufactured using a scalable, voxel-based strategy to communicate with a masked stereolithography (m-SLA) 3D printer.

Inverting the structure-property map of truss metamaterials by deep learning

Inspired by crystallography, the periodic assembly of trusses into architected materials has enjoyed popularity for more than a decade and produced countless cellular structures with beneficial mechanical properties. Despite the successful and steady enrichment of the truss design space, the inverse design has remained a challenge: While predicting effective truss properties is now commonplace, efficiently identifying architectures that have homogeneous or spatially varying target properties has remained a roadblock to applications from lightweight structures to biomimetic implants. To overcome this gap, we propose a deep-learning framework, which combines neural networks with enforced physical constraints, to predict truss architectures with fully tailored anisotropic stiffness. Trained on millions of unit cells, it covers an enormous design space of topologically distinct truss lattices and accurately identifies architectures matching previously unseen stiffness responses. We demonstrate the application to patient-specific bone implants matching clinical stiffness data, and we discuss the extension to spatially graded cellular structures with locally optimal properties.