Misalignment topology optimization with manufacturing constraints

2020 ◽  
Vol 61 (6) ◽  
pp. 2467-2480
Author(s):  
Simon Bauduin ◽  
Pablo Alarcon ◽  
Eduardo Fernandez ◽  
Pierre Duysinx
2020 ◽  
pp. 1-53
Author(s):  
Gilho Yoon ◽  
Seon Il Ha

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.


Author(s):  
Michael Greminger

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.


Author(s):  
Vlad Florea ◽  
Vishrut Shah ◽  
Stephen Roper ◽  
Garrett Vierhout ◽  
Il Yong Kim

Over the past decade there has been an increasing demand for light-weight components for the automotive and aerospace industries. This has led to significant advancement in Topology Optimization methods, especially in developing new algorithms which can consider multi-material design. While Multi-Material Topology Optimization (MMTO) can be used to determine the optimum material layout and choice for a given engineering design problem, it fails to consider practical manufacturing constraints. One such constraint is the practical joining of multi-component designs. In this paper, a new method will be proposed for simultaneously performing MMTO and Joint Topology Optimization (JTO). This algorithm will use a serial approach to loop through the MMTO and JTO phases to obtain a truly optimum design which considers both aspects. A case study is performed on an automotive ladder frame chassis component as a proof of concept for the proposed approach. Two loops of the proposed process resulted in a reduction of components and in the number of joints used between them. This translates into a tangible improvement in the manufacturability of the MMTO design. Ultimately, being able to consider additional manufacturing constraints in the Topology Optimization process can greatly benefit research and development efforts. A better design is reached with fewer iterations, thus driving down engineering costs. Topology Optimization can help in determining a cost effective and efficient design which address existing structural design challenges.


Author(s):  
James K. Guest ◽  
Mu Zhu

Projection-based algorithms are arising as a powerful tool for continuum topology optimization. They use independent design variables that are projected onto element space to create structure topology. The projection functions are designed so that geometric properties, such as the minimum length scale of features, are naturally achieved. They therefore offer an efficient means for imposing geometry-related design specifications and/or manufacturing constraints. This paper presents recent advances in projection-based algorithms, including topology optimization under manufacturing constraints related to milling and casting processes. The new advancements leverage the logic of recently proposed algorithms for Heaviside projection, including eliminating continuation methods on projection parameters and potential for using multiple design variables to achieve active projection of each phase used in design. The primary advantages of such an approach are that manufacturing restrictions are achieved naturally, without need for additional constraints, and that sensitivity calculations are efficient and straightforward. The primary drawback of the approach is that the so-called neighborhood maps require storage for efficient processing when using unstructured meshing.


2020 ◽  
Author(s):  
Vishrut Shah ◽  
Kiarash Kashanian ◽  
Manish Pamwar ◽  
Balbir Sangha ◽  
Il Yong Kim

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