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1.
MESH MORPHING BASED SHAPE
OPTIMIZATION OF A CLUTCH LEVER
Abaqus Global User Conference, 2004
Presented by:
Ramesh Padmanaban, Ragavendran Vasudevan,
, g ,
Radha Krishnan
Detroit Engineered Products, Inc.
Yogesh Wadhera – New Venture Gear
Mike Sheh – Engineous Inc.
2.
Recent Trends in the CAE Industry
Significant improvements in computing hardware:
High speed processors
Networked/distributed computing etc.
Significant improvements in Solvers:
Reduction in analysis execution time
Parallel processing
Availabililty of process integration & automation tools:
Ability to automate a routine CAE p
y process
Creation of automated workflows
3.
Recent Trends in the CAE Industry
Availability of general purpose optimization programs:
Conduct i l
C d t single and multi-discipline optimization studies that
d lti di i li ti i ti t di th t
utilize the automated workflows
Create higher level ‘quick turn-around’ response surface
models
Availability of Morphing & Parametrization Tools:
Ability to remove a key bottle neck in the optimization
process, i.e, CAD dependency
Rapid FE & CFD model generation – in a fraction of time
taken by conventional methods
b on entional
4.
Role of Design Exploration & Optimization
With these trends, the CAE engineer is best
positioned for:
Conducting DOE studies
Conduct Optimization studies
Perform design exploration
Perform DFSS (Design for Six Sigma) studies
Clear move from ‘single point design study to design
single study’
exploration
Good positioning of the CAE engineers to truly lead
p g g y
the design process
5.
CAE TO LEAD THE DESIGN PROCESS
HARDWARE SOLVER SPEED
IMPROVEMENTS IMPROVEMENTS
CAE not just a
validation tool
Instead,
Instead CAE will PROCESS
lead the design AUTOMATION & MORPHING &
OPTIMIZATION PARAMETRIZATION
process
TOOLS TOOLS
OPTIMIZED
DESIGN
6.
Different Shape Optimization Approaches
Explicit definition of nodal movement as design
variables:
Rather cumbersome to set up
Limited to small shape changes
Shape optimization with CAD in the loop:
OPTIMIZER
New values of
design parameters
ANALYSIS SOLVER PARAMETRIC CAD
SYSTEM
NO Is it OPTIMUM ? Regenerated CAD
model (new design)
YES FE PRE-PROCESSOR
PRE PROCESSOR
(Auto-meshing)
STOP
FE model of the new
design
7.
Different Shape Optimization Approaches
Remeshing from CAD data not fully automatable
If FE model is an assembly comprising of different
components and different types of elements remeshing
elements,
almost impossible
May be limited to only shape parameters
Mesh Morphing based shape optimization process
OPTIMIZER
New values of d i
N l f design
parameters
MORPHER (FE/CFD
Parametrization tool)
Analysis ready FE model of
new design
ANALYSIS SOLVER
NO Is it OPTIMUM ?
YES
STOP
8.
Different Shape Optimization Approaches
In this process CAD model generation is completely
eliminated from the optimization process
Models generated by the Morpher are analysis ready
Process inherently robust
Large shape changes are possible
9.
Important stages in the Morpher based shape
optimization process
Parametrization of the FE (or CFD) model
Analysis Process Automation
Optimization
10.
Parametrization: High Level Description
Linear Static Analysis
Model
Non-linear
Static Model
MESHWORKS/MORPHER Parametrized
V3.0 Model Data
Noise & Base
Morphing and
Vibrations model
Vib ti d l
Parametrization Tool
Crash Model
CFD Model
11.
Types of Design Parameters
SHAPE FEATURE STRUCTURAL GENERAL
PARAMETERS PARAMETERS PARAMETERS PARAMETERS
Cross-section of A- Automated stiffener Thickness of Air bag firing time
pillar (bead) creation shell structures
(stiffness/frequency) Friction coefficient
Number of stiffener Cross-sectional
Vent Opening width of Column stroke
beads and their properties of
Passenger Air Bag spacing beam members Stiffness curve
(occupant safety)
Automated Etc. Etc.
Front hood angle punching of holes
(external and slots
aerodynamics)
Etc.
Rail width & height
(crashworthiness)
Tether length &
connection location
Etc.
12.
Parametrizing FE/CFD models
Parametrization of the FE/CFD Model
/
Use Morpher to parametrize existing FE/CFD models
Introduce shape, structural, feature and general parameters
FE/CFD models become INTELLIGENT PARAMETRIC FE/CFD
models
13.
Creating a Design
Design A Design B
Design Design Design
parameter A parameter B parameter C
Morph Set A Morph Set B Morph Set C
Control Zone
Deformation Zone
Fixed Zone
14.
Concept of Parametric FE model (contd.)
DP3
DP1 – Length design variable
L th d i i bl
DP2 – Height design variable
DP3 – Width design variable DP2
DP4 – Radius design variable
DP4
DP1
DP1 – 3.5
DP2 – 4.0
40 Design 1
g
DP3 – 1.2
DP4 – 2.0
Parametrized FE model
DP1 – 4.0
DP2 – 5.0
DP3 – 2.0 Design 2
DP4 – 2.5
15.
Optimization loop
Design 1
DP1
Design 2
DP2
Design 3
g
P met i ed FE model
Parametrized
DP3
DP4 Design n
OPTIMUM
Loop 1 Loop 2 Loop 3 Loop n
16.
Analysis Process Automation
Use an analysis process automation tool such as Isight to
automate typical analysis process steps executed by the CAE
engineer
These steps would typically include:
Submission f h
S b i i of the model for analysis to Abaqus
d lf l i Ab
From the results generated, extract the specific output
parameters such as maximum deflection, maximum stress,
contact pressure etc
etc.
Compute sensitivities of output parameters with respect to
design variables
Based on sensitivities, generate new values for design variables
sensitivities
Execute Morpher to generate analysis model of new design
Proceed with the next loop
Analysis t ti i
A l i process automation is essential for automated
ti l f t t d
optimization process
17.
Optimization Methodology
Optimization
p
FEA Run time
Resource & Project calendar time
Number of Design Parameters
Design Space – Linear & Non-linear
A t t d
Automated Model
Response Surface M d l
R S f
18.
Analysis Process Automation & Optimization set up
Meshworks/Morpher
Parametrization &
shape change
engine
g
Analysis ready FE/CFD
iSIGHT (Design model with new shape
parameter values
generation)
Abaqus analysis
q y
FE / CFD
Analysis
iSIGHT
NO output
Parsing
Conve
r- Results extraction
gence
YES
optimum
19.
Optimization Loop (iSIGHT)
Batch Morpher Shape change
Abaqus Translation
Remote execution of Abaqus
iSIGHT waiting for Abaqus results
Results extraction
Remote deletion of old files
20.
iSIGHT integration with Morpher – Design Parameter file
iSIGHT modifies the values of each design parameter in DP file. A sample
file is shown above. The logical way of automatically modifying the design
parameter is using an optimization algorithm.
21.
Results Extraction
iSIGHT extracts the results from the text output file ( from analysis ). A sample file is shown above
22.
Presentation of actual case study – optimization
of clutch lever
Objective function :
Minimize D fl ti i th l t h l
Mi i i Deflection in the clutch lever
Constraints :
Maximum stress < baseline maximum
M i b li i stress
t
Design Variables:
Shape of diffe ent feat es of the cl tch le e
different features clutch lever
Range of the design variables:
Range based on manufacturability, package space and
manufacturability
element quality constraints
23.
Lever - Loads and BC
Two points are
rigidly constrained
g y
except for the rotation
about y axis
z axis translation is constrained
2.86KN
2 86KN
X axis
5.65KN
Z axis
Material = Cast Iron
24.
Design Variables of the Clutch lever
Thickness of ribs
Width of web
Thickness of web
Height of ribs
27.
Results Review - Maximum Von Mises stress history plot
MPa
28.
Optimization Statistics
Multi-genetic global optimization scheme used
Optimum obtained after 80 loops
Total completion time for Abaqus analysis, results extraction &
morphing in each loop = 30 min.
HPJ6700 machine used for analysis
After obtaining optimum for maximum stiffness, a separate
optimization was carried out to minimize mass
Results are presented in the subsequent slides
29.
Results of Optimization
Stiffness was improved about 25%
Stress levels were maintained below the target of the material
yield strength
There was marginal increase in weight
30.
Results of Optimization for maximizing stiffness
Shape change from original to Optimal design
Baseline design
Optimized design
31.
Results of Optimization for minimizing mass
Shape change from original to Optimal design
DV2
Baseline
Optimum
DV5 & DV7
Baseline
Optimum
DV4
Baseline
Optimum
Optimized design
Baseline design
DV5 & DV7
DV9
Baseline
Baseline
Optimum
Optimum
33.
Conclusions
A robust mesh morphing based shape optimization process has been
demonstrated
It can be effectively used on components with complex geometries
Significant shape change has been effected in a robust manner using
this process
The process can be successfully implemented on large system level
models with multi-disciplinary constraints
The ti i d d l b
Th optimized model can be exported out in stereo-lithography (STL)
t d ti t lith h
file format, that can be imported into any CAD system, using which a
detailed geometry can be built
Using hi
U i this process, the CAE engineer can truly lead the design process
h i l l d h d i
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