Sag Analysis of an Automotive Roller Blind System in Terms of the Variation in the Production...
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Transcript of Sag Analysis of an Automotive Roller Blind System in Terms of the Variation in the Production...
Sag Analysis of an Automotive Roller
Blind System in Terms of the
Variation in the Production Process
5th European HyperWorks Technology Conference (EHTC) November 7 – 9, 2011
Bonn, Germany
D. Bernhardt, R. Peter
Inteva Products
Roof Systems Germany GmbH, Dietzenbach
European HyperWorks Technology Conference , November 7 – 9, 2011
Agenda
Introduction
Determination of Input Parameters
Determination of Transfer Functions
Determination of Input Parameter Variations
Monte-Carlo Analysis Results
Summary & Outlook
European HyperWorks Technology Conference , November 7 – 9, 2011
Inteva Products
42 Locations – 18 countries – 4 continents
2.2 Billion USD Annual Revenue
2008 Inteva was born from Delphi Closures & Interiors
2011 Inteva Acquires ArvinMeritor’s Body Systems Business Unit
Closures: Motors & Electronics:
Interior Systems: Roof Systems:
European HyperWorks Technology Conference , November 7 – 9, 2011
Roller Blind System
Main customer interface requirements
Proper optical impression No significant sag of the fabric
Reliable operation to control light exposure No strength, noise or operational issues mainly driven by lateral and transversal forces in the fabric
Due to the variations in the manufacturing process a predictive model for the sag and the forces in the roller blind system is developed to assess and optimize scrap rates in terms of
Material property limits
Process specifications
European HyperWorks Technology Conference , November 7 – 9, 2011
Main Steps for Statistical Prediction by Monte-
Carlo Approach
Determination of the
transfer functions
Key
characteristics
Random data
(Populations)
Determination of
main input factors Monte-Carlo
Analysis Determination of the
variation
of the input factors
OUTPUT INPUT ANALYSIS
European HyperWorks Technology Conference , November 7 – 9, 2011
Roller Blind System Design
Frame
Fabric
Cross Bow
Steel tape
Rollo Guide Rail
European HyperWorks Technology Conference , November 7 – 9, 2011
Determination of Main Input Factors
Quantitative determination of the effect of main components
Sequential disassembly of components and evaluation of differences, averaged for several samples
Following components are analyzed
Fabric
Cross Bow
Steel Tape
Force Measurement
-30
-20
-10
0
10
20
30
0 100 200 300 400 500 600 700 800 900 1000
Displacement [mm]
Fo
rce [
N]
Bow open
Bow close
Fabric close
Steel tape close
Fabric open
Steel tape open
Linear (Fabric open)
Linear (Steel tape open)
Linear (Fabric close)
Linear (Steel tape close)
extended
retracted
European HyperWorks Technology Conference , November 7 – 9, 2011
Parameter Diagram
Fabric
stiffness
Bearing
distanc
e
Normal
force in
fabric
Friction
coefficient
Fabric
operational
force
Rollo
operational
force
Sag
Steel
tape
force
Cross
bow
force
Pre-
tension
distanc
e
Production
process
Sun roof
assembly
process
Fabric
Weight
European HyperWorks Technology Conference , November 7 – 9, 2011
FEA Model for Sag Analysis
Fabric
Orthotropic material law
EZ, EX, EY
Half model application of symmetry conditions
and pre-tension distance in the fabric
Frame with mounting
conditions
Cross bow
Gap definitions to fabric
Steel tape merged to fabric,
coupled to frame
Analysis with ANSYS with large deformations
Application of
gravity load (1g)
European HyperWorks Technology Conference , November 7 – 9, 2011
Relationship Between Sag, Normal Force and Pre-
tension Distance
2
23
0*24
*
*
*
H
gs
AE
Hss
H
gsf
8
**
2
Sag
Clamping
displacement
[mm]
Froce
[N inY]
Relation
Force [%]
Sag
[mm in Z]
Relation Sag
[%]
FEA 46,58 100% 2,18 100%
Analytical 48,26 104% 2,279 105%1
Evaluation and Correlation of numerical & analytical results
Mag
Equations based on rope theory: FEA
European HyperWorks Technology Conference , November 7 – 9, 2011
Correlation Testing for Sag/ Normal Force
A fairly good correlation between analytical solution and measurement can be
achieved
0
2
4
6
8
10
12
14
0 2 4 6 8 10 12 14
Measured sag [mm]
An
aly
tic
al s
ag
[m
m]
European HyperWorks Technology Conference , November 7 – 9, 2011
Relationship Operational / Normal Force
Steel weights
Force measurement device
Rollo guide rails
Steel cable connected to steel tape
0
5
10
15
20
25
0 5 10 15 20 25
Normal Force
Fri
cti
on
Fo
rce
Test_1
Test_2
Linear
Determination of friction coefficient
Test is performed for several weights (normal forces)
European HyperWorks Technology Conference , November 7 – 9, 2011
Variation of Fabric Stiffness
0,0
50,0
100,0
150,0
200,0
250,0
0 0,2 0,4 0,6 0,8 1
Stress [MPa]
Secan
t E
-Mo
du
l [M
Pa]
1A 1B
1C 1D
1E 1F
2A 2B
2C 2D
2E 2F
3A 3B
3C 3D
3E 3F
4A 4B
4C 4D
4E 4F
5A 5B
5C 5D
5E 5F
50% 15,9%
0,135% 84,1%
99,87% Spec averg
Spec Min
Stress Strain data converted to secant E-Modulus
Consideration of non-linear behavior by defining E-Modulus depending on the stress
Variation can be captured by log normal distribution
European HyperWorks Technology Conference , November 7 – 9, 2011
Guide Rail
Guide Rail
Fro
nt P
art
Rear P
art
Panel 2
Rollo guide rail distance for
steel tape bearing Load case front part
1 mm
Load case rear part
Load case panel 2
1 mm
1 mm
Variation of Bearing Distance
The frame is over constrained
For the statistical analysis a sensitivity needs to be
calculated done by FEA models and unit load cases
Resulting tolerance can be calculated by including the
sensitivity in the RSS approach
-0,2
0
0,2
0,4
0,6
0,8
1
1,2
0 200 400 600 800 1000 1200 1400
X-Coordiante [mm]
Y-D
isp
l. a
t b
eari
ng
[m
m]
Front Part_1 mm wider
Rear Part 1 mm wider
Panel_2 1 mm wider
European HyperWorks Technology Conference , November 7 – 9, 2011
Monte-Carlo Analysis Results
Scrap
Monte-Carlo Analysis in Excel (5000 samples)
Input of random data (populations, Minitab) derived from test results
Implemented analytical relations
Iteration of normal force
Calculation of sag
Prediction of out of spec parts
Scrap
Sag Operational Force
European HyperWorks Technology Conference , November 7 – 9, 2011
Summary & Outlook
The response of a fairly complex system can be calculated by relatively
simple analytical equations and approaches. This allows to cover the
variation in the analysis w/o exorbitantly increase the calculation effort
Key for this approach is to reduce the complexity of the analysis
approaches, but still capture the main effects correctly. Perquisite for this
is the availability of suitable test methods and data
Outlook
Further refinement of input statistics
Including additional input parameters to better represent noise effects
European HyperWorks Technology Conference , November 7 – 9, 2011