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Successive robust design optimization of an electronic connector
Dirk Roos
dynardo – dynamic software and engineering GmbH
Ralf Hoffmann & Thomas LieblTyco Electronics AMP GmbH
2 Weimarer Optimierungs- und Stochastiktage 5.0, 20./21. November 2008
Design for Six Sigma
• Six Sigma is a concept to optimize the manufacturing processes such that they automatically produce parts conforming with six sigma quality
• Design for Six Sigma is a concept to optimize the design such that the parts conform with six sigma quality, i.e. quality and reliability are explicit optimization goals
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Robust Design Optimization
Objective function
and additional stochastic constraints
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Limit state function
Material limit
Successive Robust Design Optimization
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reads and writes parametric data to and from all ASCII input of
any external solverreads binary parametric data from
ABAQUS odb formatreads and writes parametric data to
EXCEL, CATIA and ANSYS Workbench
ANSYS Workbench reads and writes parametric data to and from many CAD software in order to explore a wide range of responses based on a limited number of actual solutions:
Autodesk Inventor,CATIA SolidWorks, Solid Edge, Mechanical Desktop, Unigraphics and Pro/ENGINEER
Process integration
6 Weimarer Optimierungs- und Stochastiktage 5.0, 20./21. November 2008
ANSYS WorkbenchStructural Mechanics - Fluid Dynamics - Heat Transfer - Electromagnetics
An adaptable multi-physics design and analysis system that integrates and coordinates different simulation tasks
CAD / PDMCAD / PDM
Sensitivity Robustness Optimization Robust DesignReliability
Process integration
7 Weimarer Optimierungs- und Stochastiktage 5.0, 20./21. November 2008
Input datas from CeTol into Design Explorer / OptiSlang
3D Tolerance Simulation (CeTol)
ANSYS - WorkbenchCAD-Model (UG;ProE) CAD-Parameter
Statistical function ofer geometric – parameter
Kinematic Model
With rigid body
CAD – Plug In
Geometry-Tolerance
Result: statistical function of functional parameter
Analysis and comparison with manufacturing
Mathematical model to perform forecast
of Robustness & Reliability
Workflow Optimization, Robustness & Reliability Analysis Tyco Electronics
Parameter exchange
Process datas
8 Weimarer Optimierungs- und Stochastiktage 5.0, 20./21. November 2008
Connector Problem description
Basic Design Idea:2 rows with double contacts
(2x 10 Contact points)
Contact reliablity increased due to parallel contact points
Problem description:Contact of each spring and
all other springs influenced to each other…
Contact force influenced by Body deformation…
Status quo:Optimization and Robustness Analysis by Design Explorer
Question:optimized Design to meet
contact force > 1N
Reliability of optimized Design
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ProE CAD model (with 36 design CAD parameters)
Connector Problem description
ANSYS Workbench model (with 10 contact force response parameters)
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Design Explorer onlyreduced model possibleStudy single spring DX / optiSLangStudy reduced model DX
Study full model optiSLang
ProE CAD model (with n=36 design CAD parameters)
ANSYS Workbench model (with 10 contact force response parameters)
Connector Problem description
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Model single springAnsys Workbench Simulation Model single spring with Input Geometry Parameter and Result Force Reaction @ Tab (front; rear)
most important aim: set up workflow CAD-ANSYS- optiSLang
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Model single springComparison Results Design Explorer <-> optiSLang Response Surface Design Optimization
DOE-Design sample type md_y1 d_y2 F1 RSO F1 diff F2 RSO F2 diff F1/F1 targ F2/F2 targ
CCD CCD Auto Defined 0.271 0.241 3.00 2.93 102.3% 2.98 3.490 85.4% 97.8% 116.3%
CCD G-optimized 0.279 0.253 3.09 2.79 110.6% 3.080 2.790 110.4% 93.0% 93.0%
Opt Space Filling Auto Defined 0.281 0.256 3.34 2.87 116.4% 3.05 3.030 100.7% 95.7% 101.0%
Opt Space Filling full quadratic 0.279 0.253 3.09 2.79 110.8% 2.99 3.198 93.5% 92.8% 106.6%
ARSM OptiSlang CCD Aadaptive 0.278 0.254 3.00 3.00 100.0% 3 3.000 100.0% 100.0% 100.0%
Goal Driven Opimization
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Design Explorer Analysis of Connector with reduced parameter model
Input Parameter: (outside and inner springs linked together)
Response Parameter:
Design Explorer Analysis
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Design Explorer Analysis of Connector with reduced parameter model Optimization reduced parameter model
Target: F > 3N (1N+3s) ( y3=0.01 )
Design Explorer Analysis
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Design Explorer Analysis of Connector with reduced parameter model
Robust Design Analysis; Reliability Estimation…
Design Explorer Analysis
mean s UGW OGWF1ov 5.08 0.71 0.82 9.34F2ov 1.58 0.47 -1.23 4.40F3ov 0.58 0.39 -1.75 2.91F4ov 1.92 0.51 -1.16 5.01F5ov 5.87 0.72 1.53 10.20F1oh 1.34 0.68 -2.75 5.44F2oh 2.08 0.62 -1.65 5.81F3oh 2.35 0.59 -1.21 5.92F4oh 1.98 0.65 -1.91 5.87F5oh 1.00 0.65 -2.91 4.90
DOE Central Composite Design50.00%55.00%60.00%65.00%70.00%75.00%80.00%85.00%90.00%95.00%
100.00%
all >=4 >=3 >=2 >=1
Probability Function front contact
50.00%55.00%60.00%65.00%70.00%75.00%80.00%85.00%90.00%95.00%
100.00%
all >=4 >=3 >=2 >=1
Probability Function rear contact
ReliabilityTarget failed
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ProE CAD model (with n=36 design CAD parameters)
ANSYS Workbench model (with 10 contact force response parameters)
Iterative RDO with optiSLang
ANSYS Workbench finite element model with mean number of nodes of 35.660
Mean calculation time 1 hour @ 2 Xeon 2.66 GHz CPU
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Step 1 - Robustness analysis
n=31 random CAD parameters
Global variance-based robustness analysis
Advanced latin hypercube sampling with
N=90 parallel finite element calculations
Calculation time 20 hours with
Distributed calculation of ANSYS Workbench on 8 Xeon 2.66 GHz CPUs
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Step 1 - Robustness analysis
First global variance- based robustness evaluation
Identification of n=15 most important design parameters
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Step 1 - Robustness analysis
Performance critical contact force F3o_v
With failure probability of 89 %!
Large Number of numerical outliers
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Step 2 - Optimization• n=15 most important CAD
design parameters• Deterministic optimization• Minimal distance function
approach defines optimal weighted objectives with objective term definition & scaling & weights
• Target contact forces are result from six sigma analysis based on the histograms
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Step 2 - Optimization
Adaptive response surface method with D-optimal linear DOE
N=126 parallel finite element calculations
Calculation time 25 hours with
Distributed calculation of ANSYS Workbench on 8 Xeon 2.66 GHz CPUs
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Step 2 - Optimization
Stagnation of the objective improvement after the 5th adaption
Performance critical contact force F3o_v
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Step 3 – feasible design searchIntroducing of
constraints to obtain a feasible start design
Using an Evolutionary Algorithm
N=391 parallel finite element calculations
Calculation time 80 hours
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Step 3 – feasible design search
Increasing the performance critical contact force F3o_v
1.6 N -> 2.3 N
Decreasing of the non-critical contact force F2o_h3.1 N -> 1.0 N
25 Weimarer Optimierungs- und Stochastiktage 5.0, 20./21. November 2008
Step 4 – Design improvement
Target: Increasing of the non-critical contact force F2o_h
• Adaptive response surface method with D-optimal linear DOE
• Start design is based on best design resulting the EA optimization
• Start design range only 20 % of the total design space
• N=172 parallel finite element calculations
• Calculation time 35 hours
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Step 4 – Design improvement
• Increasing the non-critical contact force F2o_h
• 3.1 N -> 1.0 N -> 1.6 N• All mean contact forces are
larger than the limit state of 1 N !
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Step 5 - Robustness analysis
n=36 random CAD parameters
Global variance-based robustness analysis of the optimized design
Advanced latin hypercube sampling with
N=50 parallel finite element calculations
Calculation time 10 hours
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Step 5 - Robustness analysis
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Step 5 - Robustness analysis
Performance critical contact force F3o_v
With failure probability 9 %!
Contact force F2o_h with failure probability 1 %!
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Results Robustness Analysis
Failure probabilities of the other contact forces lesser than 1%
Design without numerical outliers
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Step 6 - Reliability analysis
Identification of n=12 most important random parameters using coefficients of importance
Defining the limit state condition for violation the minimal number of 10 contact forces
More than 50% of the contact forces are lesser than 1N
Using APDL command
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Step 6 - Reliability analysisReliability analysis
using ARSM with N=137 D-optimal design of experiment
Adaptive sampling on the MLS surrogate model without samples in the unsafe domain
Probability of failure is near zero
Optimized design is an Six Sigma Design
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Results
Variance and probability-based robust design optimization with n=36 random CAD parametersIncreasing the performance critical contact force F3o_v according failure probability 89% -> 9%Failure probabilities of the other contact forces lesser than 1%System failure probability (more than 50% of the contact forces are lesser than 1) is near zero! (Six Sigma Design)Optimized design without numerical outliers N=950 parallel finite element calculationsTotal calculation time 1 week
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Identify product design parameters that are critical to achieve a performance characteristicQuantify the effect of variations on product behavior and performanceAdjust the design parameter to hit the target performance
Reduces product costReduces product costUnderstanding potential sources of variationsMinimize the effect of variations (noise) Qualify possible steps to desensitize the design to these variations
More robust and affordable designsMore robust and affordable designsCost-effective quality inspection
No inspection for parameters that are not critical to performancNo inspection for parameters that are not critical to performancee
Benefits significance of robust design optimization
![Robust discrete optimization and network flowsdbertsim/papers/Robust Optimization/Robust Discrete optimization and...approach, Averbakh [2] showed that polynomial solvability is preserved](https://img.dokumen.tips/doc/110x75/5e8bccbc0dd72141917dfdea/robust-discrete-optimization-and-network-i-dbertsimpapersrobust-optimizationrobust.jpg)
