scholarly journals Integrating Geometric Data into Topology Optimization via Neural Style Transfer

Materials ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4551
Author(s):  
Praveen S. Vulimiri ◽  
Hao Deng ◽  
Florian Dugast ◽  
Xiaoli Zhang ◽  
Albert C. To
Keyword(s):  
Topology Optimization ◽  
Performance Metrics ◽  
Mechanical Performance ◽  
Optimization Method ◽  
Structural Performance ◽  
Style Transfer ◽  
Geometric Similarity ◽  
Reference Design ◽  
Characteristic Features ◽  
Trained Neural Network

This research proposes a novel topology optimization method using neural style transfer to simultaneously optimize both structural performance for a given loading condition and geometric similarity for a reference design. For the neural style transfer, the convolutional layers of a pre-trained neural network extract and quantify characteristic features from the reference and input designs for optimization. The optimization analysis is evaluated as a single weighted objective function with the ability for the user to control the influence of the neural style transfer with the structural performance. As seen in architecture and consumer-facing products, the visual appeal of a design contributes to its overall value along with mechanical performance metrics. Using this method, a designer allows the tool to find the ideal compromise of these metrics. Three case studies are included to demonstrate the capabilities of this method with various loading conditions and reference designs. The structural performances of the novel designs are within 10% of the baseline without geometric reference, and the designs incorporate features in the given reference such as member size or meshed features. The performance of the proposed optimizer is compared against other optimizers without the geometric similarity constraint.

scholarly journals Multiscale, thermomechanical topology optimization of self-supporting cellular structures for porous injection molds

2019 ◽  
Vol 25 (9) ◽  
pp. 1482-1492
Author(s):  
Tong Wu ◽  
Andres Tovar
Keyword(s):  
Topology Optimization ◽  
Mechanical Performance ◽  
Mechanical Load ◽  
Design Method ◽  
Three Dimensional ◽  
Optimization Method ◽  
Cellular Structures ◽  
Injection Mold ◽  
Computationally Efficient ◽  
Content Type

Purpose This paper aims to establish a multiscale topology optimization method for the optimal design of non-periodic, self-supporting cellular structures subjected to thermo-mechanical loads. The result is a hierarchically complex design that is thermally efficient, mechanically stable and suitable for additive manufacturing (AM). Design/methodology/approach The proposed method seeks to maximize thermo-mechanical performance at the macroscale in a conceptual design while obtaining maximum shear modulus for each unit cell at the mesoscale. Then, the macroscale performance is re-estimated, and the mesoscale design is updated until the macroscale performance is satisfied. Findings A two-dimensional Messerschmitt Bolkow Bolhm (MBB) beam withstanding thermo-mechanical load is presented to illustrate the proposed design method. Furthermore, the method is implemented to optimize a three-dimensional injection mold, which is successfully prototyped using 420 stainless steel infiltrated with bronze. Originality/value By developing a computationally efficient and manufacturing friendly inverse homogenization approach, the novel multiscale design could generate porous molds which can save up to 30 per cent material compared to their solid counterpart without decreasing thermo-mechanical performance. Practical implications This study is a useful tool for the designer in molding industries to reduce the cost of the injection mold and take full advantage of AM.

Mechanical Performance of Tubular Microtruss Materials Reinforced With Nanocrystalline Sleeves

2011 ◽  
Vol 1304 ◽  
Author(s):  
Eral Bele ◽  
Mishaal Azhar ◽  
Glenn D. Hibbard
Keyword(s):  
Mechanical Performance ◽  
Three Dimensional ◽  
Spatial Arrangement ◽  
Structural Performance ◽  
Element Analysis ◽  
Cross Sectional ◽  
Polymeric Scaffolds ◽  
Characteristic Features ◽  
Inherent Strength ◽  
Material Grain Size

ABSTRACTMicrotruss cellular materials are assemblies of struts with characteristic features in the μm to mm scale, arranged in a periodic, three-dimensional architecture. Compared to conventional cellular architectures (e.g. stochastic foams and honeycombs), they can possess improved structural efficiency, because externally applied loads are resolved axially along the constituent struts. We have recently fabricated composite microtruss materials by electrodepositing reinforcing nanocrystalline sleeves on tubular polymeric scaffolds. These materials can offer enhanced structural performance by exploiting advantageous properties along three length scales: the inherent strength of the electrodeposited material (grain size reduction to the nm scale), its location away from the bending axis of the struts (cross-sectional efficiency in the μm scale), and the spatial arrangement of the struts (architectural efficiency in the mm scale). This study uses finite element analysis and experimental methods to characterize the mechanical properties of these composite materials.

scholarly journals A novel lattice structure topology optimization method with extreme anisotropic lattice properties

2021 ◽  
Vol 8 (5) ◽  
pp. 1367-1390
Author(s):  
Chenghu Zhang ◽  
Jikai Liu ◽  
Zhiling Yuan ◽  
Shuzhi Xu ◽  
Bin Zou ◽  
...  
Keyword(s):  
Topology Optimization ◽  
Design Space ◽  
Mechanical Performance ◽  
Volume Fraction ◽  
Lattice Structure ◽  
Optimization Method ◽  
Structural Topology ◽  
Structure Topology ◽  
Elementary Lattice ◽  
Anisotropic Lattice

Abstract This research presents a lattice structure topology optimization (LSTO) method that significantly expands the design space by creating a novel candidate lattice that assesses an extremely large range of effective material properties. About the details, topology optimization is employed to design lattices with extreme directional tensile or shear properties subject to different volume fraction limits and the optimized lattices are categorized into groups according to their dominating properties. The novel candidate lattice is developed by combining the optimized elementary lattices, by picking up one from each group, and then parametrized with the elementary lattice relative densities. In this way, the LSTO design space is greatly expanded for the ever increased accessible material property range. Moreover, the effective material constitutive model of the candidate lattice subject to different elementary lattice combinations is pre-established so as to eliminate the tedious in-process repetitive homogenization. Finally, a few numerical examples and experiments are explored to validate the effectiveness of the proposed method. The superiority of the proposed method is proved through comparing with a few existing LSTO methods. The options of concurrent structural topology and lattice optimization are also explored for further enhancement of the mechanical performance.

An Analytical Technique for Optimization of Mechanical Performance of Foam Core Sandwich Structures

2012 ◽  
Vol 706-709 ◽  
pp. 1373-1378
Author(s):  
Hanif Montazeri ◽  
Fardad Azarmi ◽  
Tom W. Coyle ◽  
Javad Mostaghimi
Keyword(s):  
Mechanical Performance ◽  
Sandwich Structures ◽  
Optimization Method ◽  
Lagrangian Method ◽  
Structural Performance ◽  
Optimization Approach ◽  
Design Problems ◽  
Analytical Technique ◽  
Selection Of ◽  
Physical And Mechanical Characteristics

Sandwich structures are widely used, especially in areas where the performance of conventional materials is simply not adequate. Sandwich components achieve the same structural performance as conventional materials with weight savings of up to 75 %. They are basically made from two thin skins (faces) and a lightweight thicker core. Their structural, physical, and mechanical characteristics can be tailored based on service requirements by selection of different materials and manufacturing processes. In this study, the geometry and property of each separate component is utilized to the structural advantage of the whole assembly. Although Lagrangian method has been widely applied in other engineering disciplines, it has received less attention for optimization of sandwich components. The Lagrangian method is therefore introduced and expanded to find solutions for multipurpose design problems. This new optimization approach will enable us to find analytical solutions for complicated design problems which were conventionally solved by utilizing graphical methods. This paper aims to present a generic optimization method which can be used in the variety of applications in this field.

scholarly journals Topology Optimization of Hard-Coating Thin Plate for Maximizing Modal Loss Factors

Coatings ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 774
Author(s):  
Haitao Luo ◽  
Rong Chen ◽  
Siwei Guo ◽  
Jia Fu
Keyword(s):  
Topology Optimization ◽  
Objective Function ◽  
Composite Plates ◽  
Optimization Method ◽  
Hard Coating ◽  
Design Variables ◽  
Coating Composite ◽  
Damping Materials ◽  
Topology Optimization Method ◽  
Loss Factors

At present, hard coating structures are widely studied as a new passive damping method. Generally, the hard coating material is completely covered on the surface of the thin-walled structure, but the local coverage cannot only achieve better vibration reduction effect, but also save the material and processing costs. In this paper, a topology optimization method for hard coated composite plates is proposed to maximize the modal loss factors. The finite element dynamic model of hard coating composite plate is established. The topology optimization model is established with the energy ratio of hard coating layer to base layer as the objective function and the amount of damping material as the constraint condition. The sensitivity expression of the objective function to the design variables is derived, and the iteration of the design variables is realized by the Method of Moving Asymptote (MMA). Several numerical examples are provided to demonstrate that this method can obtain the optimal layout of damping materials for hard coating composite plates. The results show that the damping materials are mainly distributed in the area where the stored modal strain energy is large, which is consistent with the traditional design method. Finally, based on the numerical results, the experimental study of local hard coating composites plate is carried out. The results show that the topology optimization method can significantly reduce the frequency response amplitude while reducing the amount of damping materials, which shows the feasibility and effectiveness of the method.

scholarly journals A Two-Stage Mono- and Multi-Objective Method for the Optimization of General UPS Parallel Manipulators

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 543
Author(s):  
Alejandra Ríos ◽  
Eusebio E. Hernández ◽  
S. Ivvan Valdez
Keyword(s):  
Genetic Algorithm ◽  
Performance Metrics ◽  
Tracking Error ◽  
Parallel Manipulators ◽  
Optimization Method ◽  
Statistical Hypothesis ◽  
Two Stage ◽  
Multi Objective ◽  
Second Stage ◽  
Conflicting Objectives

This paper introduces a two-stage method based on bio-inspired algorithms for the design optimization of a class of general Stewart platforms. The first stage performs a mono-objective optimization in order to reach, with sufficient dexterity, a regular target workspace while minimizing the elements’ lengths. For this optimization problem, we compare three bio-inspired algorithms: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), and the Boltzman Univariate Marginal Distribution Algorithm (BUMDA). The second stage looks for the most suitable gains of a Proportional Integral Derivative (PID) control via the minimization of two conflicting objectives: one based on energy consumption and the tracking error of a target trajectory. To this effect, we compare two multi-objective algorithms: the Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) and Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The main contributions lie in the optimization model, the proposal of a two-stage optimization method, and the findings of the performance of different bio-inspired algorithms for each stage. Furthermore, we show optimized designs delivered by the proposed method and provide directions for the best-performing algorithms through performance metrics and statistical hypothesis tests.

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