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Graduate Thesis
Post-processing of 2D topology optimized designs and extraction of smooth and
crisp geometry
May 28, 2021 · 23 mins read
Overview
This thesis deals with the post-processing of the results obtained from a topology optimization
algorithms which outputs the result as a 2D image. A suitable methodology is discussed where this
output 2d geometry is processed and converted into an CAD geometry all while minimizing deviation in
geometry, compliance and volume fraction while validating the results. All the programming is done in
MATLAB and it uses an image based post-processing approach. The proposed workow is tested on
several numerical examples to assess the performance, limitations and numerical instabilities.
Direct links to the primary referred paper, thesis and code repository.
Contents
Background
Introduction
Problem Denition
Methodology
Topology Optimization
Geometry Smoothing
Geometry Extraction
Results
Material Properties
MBB Beam
T Beam
Additional Details
Result Metrics
Limitations
Outlook
![](https://csdnimg.cn/release/download_crawler_static/89141830/bg2.jpg)
Background
Introduction
Topology Optimization (TO) is a mathematical methodology that optimizes a material domain within a
prescribed design space, subject to predened requirements and boundary conditions such that the
resulting domain meets a prescribed set of goals. TO can help greatly reduce the lead time of the
component all while developing a component suited for a particular objective and also help in reducing
the amount of material used in the manufacture of the component.
Problem Denition
To begin with, a simple TO code will be much easier to manipulate compared to huge commercial
softwares. One such code is the popular 88-line MATLAB code which is an ideal starting point as it
supports multiple load-cases, is mesh independent and is most importantly a fast solver. This code is
also short and is validated by the numerous publications which support it as one of the best codes to
understand how TO works. However, this code has its limitations; it can only solve 2D optimization
problems and uses only bilinear quad-elements in its FE formulation. The optimized result from the
code is in the form of a black-and-white image which needs to be manipulated to make it more suitable
to our purposes.
This thesis deals with the formulation of a methodology or a workow to post-process the resulting
geometry from the 88-line code to make it ready for production in as little time as possible. TO
geometries are usually subjected to post-processing as there might be the presence of rough surfaces,
jagged edges or intermediate densities. If these rough edges end up in the nal geometry they can
cause stress concentrations or even decrease the fatigue life of the structure. Hence, a faster and an
efcient way to post-process results is needed to make way in a much smoother and rapid
product/prototype development.
Methodology
The outcome of the literature study denitively showed that the most prominent way of resolving
these problems consists of three phases which are combined under a singular approach called
'Structural Design Optimization' The three phases are listed and explained in detail below.
Topology Optimization
Geometry Smoothing
Geometry Extraction
The workow formulated is adapted from "Structural Design Optimization" concept. A owchart
depicting the generalized workow can be seen in the gure below.
![](https://csdnimg.cn/release/download_crawler_static/89141830/bg3.jpg)
The entire workow can be divided into three major phases excluding TO. The process begins with the
input denitions being used for TO whose results along with the same inputs are shared with the next
phase i.e. geometry smoothing. The smoothed geometry is then validated and nally geometry is
extracted.
Topology Optimization
The TO problem in this thesis is a compliance minimization problem under a volume constraint. The
generalized TO problem to solve the compliance minimization problem can be seen in the equation
below.
where
C
is compliance,
V(
x
)
is current volume fraction, V0 is maximum volume fraction and
V
is the
target volume fraction.
The volume fraction
V(
x
)
is calculated by taking the arithmetic mean of the elemental densities, see the
equation below.
The 88-line code TO solver is setup to solve the above compliance minimization problem. This solver
code is modied with the following few additions.
Compliance Computation
Compute & visualize von Mises Stresses
Structural Design Optimization.
(
P
)
⎧
⎪
⎨
⎪
⎩
min
x
C
(
x
) =
u
T
Ku
subject to
{
V
(
x
)
V
0
=
¯
V
0 ≤
x
e
≤ 1,
e
= 1, 2, . . . ,
n
e
,
V
(
x
) =
∑
n
e
e
=1
¯
x
e
n
e
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