Topology Optimization of Wing Structure with Manufacturing Constraints

2015 ◽  
Vol 789-790 ◽  
pp. 209-212
Author(s):  
Dan He ◽  
Chun Jing Lin ◽  
Yang Chen Deng
Keyword(s):  
Topology Optimization ◽  
Size Control ◽  
Manufacturing Constraints ◽  
Initial Design ◽  
Optimal Procedure ◽  
Draw Direction ◽  
Wing Structure ◽  
Member Size

A methodology and a procedure for topology optimization of wing structure based on manufacturing constraints were proposed. By the methodology and procedure proposed, topology of thin-box wing structure which is easier manufactured than traditional topology result can be obtained. The manufacturing constraints of draw direction, member size control and inexcusable initial design were introduced into the optimal procedure and executed in Optistruct. Effect and efficiency were analyzed and compared with traditional topology optimization.

Predicting the Benefits of Topology Optimization

2015 ◽  
Author(s):  
Shiguang Deng ◽  
Krishnan Suresh
Keyword(s):  
Finite Element ◽  
Topology Optimization ◽  
Shape Optimization ◽  
Systematic Method ◽  
Topological Sensitivity ◽  
Numerical Examples ◽  
Initial Design

Topology optimization is a systematic method of generating designs that maximize specific objectives. While it offers significant benefits over traditional shape optimization, topology optimization can be computationally demanding and laborious. Even a simple 3D compliance optimization can take several hours. Further, the optimized topology must typically be manually interpreted and translated into a CAD-friendly and manufacturing friendly design. This poses a predicament: given an initial design, should one optimize its topology? In this paper, we propose a simple metric for predicting the benefits of topology optimization. The metric is derived by exploiting the concept of topological sensitivity, and is computed via a finite element swapping method. The efficacy of the metric is illustrated through numerical examples.

scholarly journals Topology optimization of compact wideband coaxial-to-waveguide transitions with minimum-size control

2017 ◽  
Vol 57 (4) ◽  
pp. 1765-1777 ◽  
Author(s):  
Emadeldeen Hassan ◽  
Eddie Wadbro ◽  
Linus Hägg ◽  
Martin Berggren
Keyword(s):  
Topology Optimization ◽  
Size Control ◽  
Minimum Size ◽  
Waveguide Transitions

Misalignment topology optimization with manufacturing constraints

2020 ◽  
Vol 61 (6) ◽  
pp. 2467-2480
Author(s):  
Simon Bauduin ◽  
Pablo Alarcon ◽  
Eduardo Fernandez ◽  
Pierre Duysinx
Keyword(s):  
Topology Optimization ◽  
Manufacturing Constraints

A new development of a shadow density filter for manufacturing constraint and its applications to multiphysics topology optimization

2020 ◽  
pp. 1-53
Author(s):  
Gilho Yoon ◽  
Seon Il Ha
Keyword(s):  
Topology Optimization ◽  
Transfer Problem ◽  
Manufacturing Constraints ◽  
Structural Topology ◽  
Geometrical Features ◽  
Manufacturing Constraint ◽  
New Development ◽  
Molded Parts ◽  
Density Filter ◽  
Interaction Problem

Abstract The present research develops a new shadow filter and presents its usage for structural topology optimization (TO) considering the molding manufacturability. It is important to consider manufacturing methods in designing products. Some geometrical features not allowing molded parts should be removed. In addition, it has been an important issue to efficiently impose these manufacturing constraints in TO. For this purpose, the present research emulates implementation of shadowing of products and applies the shadow images as pseudodensity variables in TO. The use of this shadow density filter ensures that the optimized layouts comply with the conditions of the manufacturing constraints. Various manufacturing conditions can be imposed depending on the direction and the position of the light. Several numerical examples of compliance minimization problem, conjugate heat transfer problem and fluid-structure interaction problem are solved to demonstrate the validity and effectiveness of the present shadow density filters, and their performances are compared.

Generative Adversarial Networks With Synthetic Training Data for Enforcing Manufacturing Constraints on Topology Optimization

2020 ◽  
Author(s):  
Michael Greminger
Keyword(s):  
Topology Optimization ◽  
Vector Space ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Manufacturing Constraints ◽  
Cnc Milling ◽  
Manufacturing Method ◽  
Latent Vector ◽  
Adversarial Networks ◽  
Manufacturing Methods

Abstract Topology optimization is a powerful tool to generate mechanical designs that use minimal mass to achieve their function. However, the designs obtained using topology optimization are often not manufacturable using a given manufacturing process. There exist some modifications to the traditional topology optimization algorithm that are able to impose manufacturing constraints for a limited set of manufacturing methods. These approaches have the drawback that they are often based on heuristics to obtain the manufacturability constraint and thus cannot be applied generally to multiple manufacturing methods. In order to create a general approach to imposing manufacturing constraints on topology optimization, generative adversarial networks (GANs) are used. GANs have the capability to produce samples from a distribution defined by training data. In this work, the GAN is trained by generating synthetic 3D voxel training data that represent the distribution of designs that can be created by a particular manufacturing method. Once trained, the GAN forms a mapping from a latent vector space to the space of manufacturable designs. The topology optimization is then performed on the latent vector space ensuring that the design obtained is manufacturable. The effectiveness of this approach is demonstrated by training a GAN on designs intended to be manufacturable on a 3-axis computer numerically controlled (CNC) milling machine.

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