scholarly journals Optimal and continuous multilattice embedding

2021 ◽  
Vol 7 (16) ◽  
pp. eabf4838
Author(s):  
E. D. Sanders ◽  
A. Pereira ◽  
G. H. Paulino
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Hierarchical Structures ◽  
Free Form ◽  
Structural Hierarchy ◽  
Design And Manufacturing ◽  
Surface Representations ◽  
Material Space ◽  
Spatially Varying ◽  
Biomimetic Structures

Because of increased geometric freedom at a widening range of length scales and access to a growing material space, additive manufacturing has spurred renewed interest in topology optimization of parts with spatially varying material properties and structural hierarchy. Simultaneously, a surge of micro/nanoarchitected materials have been demonstrated. Nevertheless, multiscale design and micro/nanoscale additive manufacturing have yet to be sufficiently integrated to achieve free-form, multiscale, biomimetic structures. We unify design and manufacturing of spatially varying, hierarchical structures through a multimicrostructure topology optimization formulation with continuous multimicrostructure embedding. The approach leads to an optimized layout of multiple microstructural materials within an optimized macrostructure geometry, manufactured with continuously graded interfaces. To make the process modular and controllable and to avoid prohibitively expensive surface representations, we embed the microstructures directly into the 3D printer slices. The ideas provide a critical, interdisciplinary link at the convergence of material and structure in optimal design and manufacturing.

Parametric Topology Optimization Toward Rational Design and Efficient Prefabrication for Additive Manufacturing

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Long Jiang ◽  
Hang Ye ◽  
Chi Zhou ◽  
Shikui Chen
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Level Set ◽  
Rational Design ◽  
Computational Cost ◽  
Significant Advance ◽  
Free Form ◽  
Geometric Complexity ◽  
Original Design ◽  
Design And Manufacturing

The significant advance in the boosted fabrication speed and printing resolution of additive manufacturing (AM) technology has considerably increased the capability of achieving product designs with high geometric complexity and provided tremendous potential for mass customization. However, it is also because of geometric complexity and large quantity of mass-customized products that the prefabrication (layer slicing, path planning, and support generation) is becoming the bottleneck of the AM process due to the ever-increasing computational cost. In this paper, the authors devise an integrated computational framework by synthesizing the parametric level set-based topology optimization method with the stereolithography (SLA)-based AM process for intelligent design and manufacturing of multiscale structures. The topology of the design is optimized with a distance-regularized parametric level set method considering the prefabrication computation. With the proposed framework, the structural topology optimization not only can create single material structure designs but also can generate multiscale, multimaterial structures, offering the flexibility and robustness of the structural design that the conventional methods could not provide. The output of the framework is a set of mask images that can be directly used in the AM process. The proposed approach seamlessly integrates the rational design and manufacturing to reduce the numerical complexity of the computationally expensive prefabrication process. More specifically, the prefabrication-friendly design and optimization procedure are devised to drastically eliminate the redundant computations in the traditional framework. Two test examples, including a free-form 3D cantilever beam and a multiscale meta-structure, are utilized to demonstrate the performance of the proposed approach. Both the simulation and experimental results verified that the new rational design could significantly reduce the prefabrication computation cost without affecting the original design intent or sacrificing the original functionality.

Computational Design and Additive Manufacturing of Conformal Metasurfaces by Combining Topology Optimization With Riemann Mapping Theorem

2017 ◽  
Author(s):  
Panagiotis Vogiatzis ◽  
Ming Ma ◽  
Shikui Chen ◽  
Xianfeng David Gu
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Conformal Mapping ◽  
Level Set ◽  
Computational Design ◽  
Unit Cell ◽  
Free Form ◽  
Computational Framework ◽  
Riemann Mapping Theorem ◽  
Free Form Surfaces

In this paper, we present a computational framework for computational design and additive manufacturing of spatial free-form periodic metasurfaces. The proposed scheme rests on the level-set based topology approach and the conformal mapping theory. A 2D unit cell of metamaterial with tailored effective properties is created using the level-set based topology optimization method. The achieved unit cell is further mapped to the 3D quad meshes on a free-form surface by applying the conformal mapping method which can preserve the local shape and angle when mapping the 2D design to a 3D surface. The proposed level-set based optimization methods not only can act as a motivator for design synthesis, but also can be seamlessly hooked with additive manufacturing with no need of CAD reconstructions. The proposed computational framework provides a solution to increasing applications involving innovative metamaterial designs on free-form surfaces in different fields of interest. The performance of the proposed scheme is illustrated through a benchmark example where a negative-Poisson’s-ratio unit cell pattern is mapped to a 3D human face and fabricated through additive manufacturing.

Design and Topology Optimization of Lattice Structures Using Deformable Implicit Surfaces for Additive Manufacturing

2015 ◽  
Author(s):  
Erhan Batuhan Arisoy ◽  
Suraj Musuvathy ◽  
Lucia Mirabella ◽  
Edward Slavin
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Structural Characteristics ◽  
Product Lifecycle Management ◽  
Lattice Structures ◽  
Cad System ◽  
Computer Aided ◽  
Rapid Generation ◽  
Freeform Deformation ◽  
Surface Representations

Additive manufacturing (AM) enables creation of objects with complex internal lattice structures for functional, aesthetic, structural and fabrication considerations. Several approaches for lattice generation and optimization, and their implementations in commercial systems exist. However, these commercial systems are typically independent from a CAD system, and therefore introduces workflow complexities for product lifecycle management. In this paper, we present a unified computer-aided framework for design, computer-aided engineering analysis (CAE) of solids with lattice structures, and freeform topology optimization within the CAD system that enables a seamless workflow. The proposed framework takes as input a solid CAD model and enables rapid generation of different lattice structures as repeated arrangements of lattice template shapes that replace input solid volume. Generated internal patterns are further optimized through freeform modifications to improve structural characteristics of the input model. Lattice modeling and optimization is performed using discrete implicit surface representations for the ease in representing complex topologies and performing modeling and freeform deformation operations. The output of the proposed framework is a polygonal represenatation of the lattified model ready for 3D printing. We have implemented our framework as a plugin to the Siemens PLM NX software system and examples are demonstrated for typical products in aerospace, medical and automotive industries.

scholarly journals A Sequential Approach for Aerodynamic Shape Optimization with Topology Optimization of Airfoils

2021 ◽  
Vol 26 (2) ◽  
pp. 34
Author(s):  
Isaac Gibert Martínez ◽  
Frederico Afonso ◽  
Simão Rodrigues ◽  
Fernando Lau
Keyword(s):  
Topology Optimization ◽  
Shape Optimization ◽  
Volume Fraction ◽  
Optimization Techniques ◽  
Free Form ◽  
Aerodynamic Shape Optimization ◽  
Sequential Approach ◽  
Airfoil Surface ◽  
Aerodynamic Shape ◽  
Design Variables

The objective of this work is to study the coupling of two efficient optimization techniques, Aerodynamic Shape Optimization (ASO) and Topology Optimization (TO), in 2D airfoils. To achieve such goal two open-source codes, SU2 and Calculix, are employed for ASO and TO, respectively, using the Sequential Least SQuares Programming (SLSQP) and the Bi-directional Evolutionary Structural Optimization (BESO) algorithms; the latter is well-known for allowing the addition of material in the TO which constitutes, as far as our knowledge, a novelty for this kind of application. These codes are linked by means of a script capable of reading the geometry and pressure distribution obtained from the ASO and defining the boundary conditions to be applied in the TO. The Free-Form Deformation technique is chosen for the definition of the design variables to be used in the ASO, while the densities of the inner elements are defined as design variables of the TO. As a test case, a widely used benchmark transonic airfoil, the RAE2822, is chosen here with an internal geometric constraint to simulate the wing-box of a transonic wing. First, the two optimization procedures are tested separately to gain insight and then are run in a sequential way for two test cases with available experimental data: (i) Mach 0.729 at α=2.31°; and (ii) Mach 0.730 at α=2.79°. In the ASO problem, the lift is fixed and the drag is minimized; while in the TO problem, compliance minimization is set as the objective for a prescribed volume fraction. Improvements in both aerodynamic and structural performance are found, as expected: the ASO reduced the total pressure on the airfoil surface in order to minimize drag, which resulted in lower stress values experienced by the structure.

scholarly journals Additive Manufacturing for Effective Smart Structures: The Idea of 6D Printing

2021 ◽  
Vol 5 (5) ◽  
pp. 119
Author(s):  
Stelios K. Georgantzinos ◽  
Georgios I. Giannopoulos ◽  
Panteleimon A. Bakalis
Keyword(s):  
Additive Manufacturing ◽  
Degrees Of Freedom ◽  
Smart Structures ◽  
Interaction Mechanism ◽  
Modeling And Simulations ◽  
Environmental Stimulus ◽  
Material Component ◽  
Design And Manufacturing ◽  
First Time ◽  
Printing Techniques

This paper aims to establish six-dimensional (6D) printing as a new branch of additive manufacturing investigating its benefits, advantages as well as possible limitations concerning the design and manufacturing of effective smart structures. The concept of 6D printing, to the authors’ best knowledge, is introduced for the first time. The new method combines the four-dimensional (4D) and five-dimensional (5D) printing techniques. This means that the printing process is going to use five degrees of freedom for creating the final object while the final produced material component will be a smart/intelligent one (i.e., will be capable of changing its shape or properties due to its interaction with an environmental stimulus). A 6D printed structure can be stronger and more effective than a corresponding 4D printed structure, can be manufactured using less material, can perform movements by being exposed to an external stimulus through an interaction mechanism, and it may learn how to reconfigure itself suitably, based on predictions via mathematical modeling and simulations.

Design of spatially-varying orthotropic infill structures using multiscale topology optimization and explicit de-homogenization

2021 ◽  
Vol 40 ◽  
pp. 101920
Author(s):  
Jaewook Lee ◽  
Chiyoung Kwon ◽  
Jeonghoon Yoo ◽  
Seungjae Min ◽  
Tsuyoshi Nomura ◽  
...  
Keyword(s):  
Topology Optimization ◽  
Spatially Varying
Sign in / Sign up

Export Citation Format

Share Document