Additive Manufacturing Rectangular Lattice Structure Design Automation

2017 ◽  
Author(s):  
Nagesh Chougule ◽  
Vinit Sonawane
Keyword(s):  
Additive Manufacturing ◽  
Design Automation ◽  
Lattice Structure ◽  
Structure Design ◽  
Rectangular Lattice

An improved lattice structure design optimization framework considering additive manufacturing constraints

2017 ◽  
Vol 23 (2) ◽  
pp. 305-319 ◽  
Author(s):  
Recep M. Gorguluarslan ◽  
Umesh N. Gandhi ◽  
Yuyang Song ◽  
Seung-Kyum Choi
Keyword(s):  
Additive Manufacturing ◽  
Lattice Structure ◽  
Structure Optimization ◽  
Optimization Methods ◽  
Structure Design ◽  
Second Phase ◽  
Manufacturing Constraints ◽  
Ground Structure ◽  
Content Type ◽  
Optimization Framework

Purpose Methods to optimize lattice structure design, such as ground structure optimization, have been shown to be useful when generating efficient design concepts with complex truss-like cellular structures. Unfortunately, designs suggested by lattice structure optimization methods are often infeasible because the obtained cross-sectional parameter values cannot be fabricated by additive manufacturing (AM) processes, and it is often very difficult to transform a design proposal into one that can be additively designed. This paper aims to propose an improved, two-phase lattice structure optimization framework that considers manufacturing constraints for the AM process. Design/methodology/approach The proposed framework uses a conventional ground structure optimization method in the first phase. In the second phase, the results from the ground structure optimization are modified according to the pre-determined manufacturing constraints using a second optimization procedure. To decrease the computational cost of the optimization process, an efficient gradient-based optimization algorithm, namely, the method of feasible directions (MFDs), is integrated into this framework. The developed framework is applied to three different design examples. The efficacy of the framework is compared to that of existing lattice structure optimization methods. Findings The proposed optimization framework provided designs more efficiently and with better performance than the existing optimization methods. Practical implications The proposed framework can be used effectively for optimizing complex lattice-based structures. Originality/value An improved optimization framework that efficiently considers the AM constraints was reported for the design of lattice-based structures.

Lattice Structure Design for Additive Manufacturing: Unit Cell Topology Optimization

2019 ◽  
Author(s):  
Bradley Hanks ◽  
Mary Frecker
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Lattice Structure ◽  
Optimization Method ◽  
Structure Design ◽  
Unit Cell ◽  
Lattice Structures ◽  
Design For Additive Manufacturing ◽  
Structure Topology

Abstract Additive manufacturing is a developing technology that enhances design freedom at multiple length scales, from the macroscale, or bulk geometry, to the mesoscale, such as lattice structures, and even down to tailored microstructure. At the mesoscale, lattice structures are often used to replace solid sections of material and are typically patterned after generic topologies. The mechanical properties and performance of generic unit cell topologies are being explored by many researchers but there is a lack of development of custom lattice structures, optimized for their application, with considerations for design for additive manufacturing. This work proposes a ground structure topology optimization method for systematic unit cell optimization. Two case studies are presented to demonstrate the approach. Case Study 1 results in a range of unit cell designs that transition from maximum thermal conductivity to minimization of compliance. Case Study 2 shows the opportunity for constitutive matching of the bulk lattice properties to a target constitutive matrix. Future work will include validation of unit cell modeling, testing of optimized solutions, and further development of the approach through expansion to 3D and refinement of objective, penalty, and constraint functions.

scholarly journals Lattice structure design with topology optimization and additive manufacturing

2017 ◽  
Vol 83 (855) ◽  
pp. 16-00581-16-00581
Author(s):  
Takafumi NISHIZU ◽  
Tomoya TANITSUGU ◽  
Akihiro TAKEZAWA ◽  
Kazuo YONEKURA ◽  
Osamu WATANABE ◽  
...  
Keyword(s):  
Topology Optimization ◽  
Additive Manufacturing ◽  
Lattice Structure ◽  
Structure Design

scholarly journals Lattice structure design optimization coupling anisotropy and constraints of additive manufacturing

2020 ◽  
Vol 196 ◽  
pp. 109089
Author(s):  
Yu Wang ◽  
Shuaishuai Li ◽  
Ying Yu ◽  
Yanmei Xin ◽  
Xiaoyang Zhang ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Design Optimization ◽  
Lattice Structure ◽  
Structure Design

Lattice Structure Design Advisor for Additive Manufacturing Using Gaussian Process

2017 ◽  
Author(s):  
Tsz Ling Elaine Tang ◽  
Yan Liu ◽  
Da Lu ◽  
Erhan Batuhan Arisoy ◽  
Suraj Musuvathy
Keyword(s):  
Additive Manufacturing ◽  
Structural Properties ◽  
Structural Design ◽  
Lattice Structure ◽  
Structure Design ◽  
Structural Performance ◽  
Design Parameters ◽  
Use Case ◽  
Lattice Structures ◽  
Lattice Design

Additive manufacturing (AM) exemplifies the potential of lattice structures to revolutionize structural design. It enables light weight lattice structures to be produced while maintaining the desirable structural performance. Lattice design can vary in different shapes and dimensions. Obtaining the structural performance of a particular lattice structure design is not a straight-forward process. Significant effort is required to perform mechanical testing experiments or to perform finite element analysis (FEA) to characterize the lattice design. In view of that, a guidance system to determine lattice design parameters based on desired functional performance for a specific lattice type is developed, which can be used in interactive design processes and workflows. Homogenization using FEA experiments is applied to characterize the macroscopic lattice structural properties. Mechanical properties of orthotropic cubic lattice f2ccz are estimated. It follows with a design of experiment study to characterize the effective structural properties of 39 lattices with respect to lattice design parameters (unit cell length and strut diameter). A Gaussian process is applied to develop models relating the lattice design parameter to macroscopic structural properties (forward model), and vice versa (inverse model). Both the forward and inverse models are examined and shown to be capable of modeling the FEA experimental dataset of 39 lattices. To illustrate the potential application of the lattice design advisor framework, a structural design use case including lattice part is presented. In the use case, the lattice structure design advisor is proven to be able to estimate an accurate homogenized material property of arbitrary lattice design parameter. This lattice structure design advisor can simplify and streamline the design, modeling and simulation process of lattice-filled structural designs.

Bayesian Optimization of Energy-Absorbing Lattice Structures for Additive Manufacturing

2020 ◽  
Author(s):  
Nathan Hertlein ◽  
Kumar Vemaganti ◽  
Sam Anand
Keyword(s):  
Finite Element ◽  
Additive Manufacturing ◽  
Optimization Problems ◽  
Lattice Structure ◽  
Structure Design ◽  
Bayesian Optimization ◽  
Fe Model ◽  
Lattice Structures ◽  
Element Analysis ◽  
Overhang Angle

Abstract Additive manufacturing has enabled the production of intricate lattice structures that meet stringent design requirements with minimal mass. While many methods such as lattice-based topology optimization are being developed to design lightweight structures for static loading, there is a need for design tools for achieving dynamic loading requirements. Lattice structures have shown particular promise as low-mass energy absorbers, but the computational expense of nonlinear finite element analysis and the difficulty of obtaining objective gradient information has made their optimization for impact loading particularly challenging. This study proposes a Bayesian optimization framework to determine the lattice structure design that provides the best performance under a specified impact, while managing the structure’s mass. Considering nonlinear effects such as plasticity and strain rate sensitivity, a 2D explicit finite element (FE) model is constructed for two lattice unit cell types under impact, and parameterized with respect to geometric attributes such as height, width, and strut thickness. These parameters are considered design variables in a minimization problem with an objective function that balances part volume with a common injury metric, the head injury criterion (HIC). Penalty values are assigned to designs that fail to absorb the entire impact. Design for additive manufacturing (DFAM) constraints including minimum feature thickness and maximum overhang angle are applied to ensure that the optimal design can be manufactured without subsequent manual refinement or post-processing. The best optimizer hyperparameters are then carried over into larger optimization problems involving lattice structures. Future work could include expanding this framework to allow for lattice structure designs with arbitrary boundaries.

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