a systematic approach for weight reduction and … · a systematic approach for weight reduction...
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A Systematic Approach for Weight Reduction and Improvement of Material Usage
Lei Shi, Ph.D., ASME/SAE MemberTechnical Director R&D of Great Wall Motor CompanyTechnical Director, R&D of Great Wall Motor Company
April 27, 2016 Birmingham, UK
What are we?
Overview‐We are China’s largest SUV and pickup manufacturer, with over 30 holding subsidiaries, more than 60,000 employees, four vehicle production bases and a production capacity of 800,000 units.‐We own Haval and Great Wall brands, covering products range of SUV, passenger car and pickup. ‐ Our assets had amounted to 91.273 billion RBM by Dec. 10, 2015.‐We persist in focused development, uphold the brand concept of "focus, dedication and specialization“
66.25
60
70
Our products: SUV, Pickup and Sedan
Unit: 10, 000
2015 SUV Top-ten Sales Ranking in China
L Top one: HAVAL
40.22 35.60
29.56 26.18 25.58 25.31 24.50 24.06 21.71 20
30
40
50
‐ First produced economic type of SUV in domestic.Hover H6 got “China annual SUV prize” in 2012
HAV
A
0
10
哈弗 本田 长安 现代 福特 大众 江淮 别克 日产 丰田
‐ Hover H6 got China annual SUV prize in 2012, which sales so popular and even hard to meetthe purchase requirement.
HAVAL H8 HAVAL H6 HAVAL Coupe-c HAVAL Coupe HAVAL H9 HAVAL H6 Update HAVAL H2
2
What are we?
Pickup
‐ Domestic sales for GW pickup gains No.1 in 14 continually years‐ Together with Ford, Toyota pickup named as “World Three Pickup”‐ Trusted by 700,000 vehicle owner, and sales for more than 100 countries and areas
Sedan
C50 C30 C2OR C50 Update
‐ First made the saloon car in 2007, got No.2 in terms of sales quantity in 2011‐ First passed EWVTA as Chinese self‐owned saloon brand manufacture
3
What are we?
EuropeNorth America
JapanChinaGWM New R&D Center
(Baoding, China)
GWM European R&D Center GWM Japan R&D Center GWM North America R&D Center (On‐going)
4
Outline
• Background/Objective
• MDO-based Platform Development
• Application on Vehicle Body Design
• Summary• Summary
5
Background / Objective
• Goal of R&D activities:
Over-Over-
Style Freeze •T/Section•Ext/Int
Data Freeze•M/Section•CAE &
Over-designOver-
design
•Ext/Int•S.E.•Feasibility Report
CAE & Physical Test•S.E..•Neighbor Parts Status ThroughStatus Through
CAECAEverification
Optimum-design
Optimum-design
DataFreeze
ReduceStyle Change &y g
Data Change Under-designUnder-design
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How to freeze data?
Background / Objective
Design ChangeDesign ChangeCC ffff
• Goal of R&D activities:
CutCut--offoff
Reach�Attributes�Target
within�given�cost�and�weight
Weight Cost Attributes+ =• Objective:
- To expedite the entire PD process by offering faster turnaround time a comprehensive MDO-To expedite the entire PD process by offering faster turnaround time, a comprehensive MDObased platform for vehicle attribute integration is developed. - Three key issues are addressed: parameter model synchronization, metamodel predictive capabilities, and pre/post processing.- Cost/Process/Weight/Attributes Model are developed and used to improve the ratio of materialCost/Process/Weight/Attributes Model are developed and used to improve the ratio of material usage while meeting cost, weight and attributes target.
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Background / Objective
• What is MDO:
- Multidisciplinary Design Optimization (MDO) is a methodology for improving design of engineering systems, e.g., automobile, aircraft, or spacecraft, in which everything influences everything else.
- By Dr. J. Sobieski from NASA Langleye y g y
NVH: Torsion, Bending, Dynamic stiffness
Perf
orm
ance
Conventional Trails
偏置碰:64kph
Offset impact
Baseline
Design
Multidisciplinar
SuboptimalDesign
64kph
正碰:35mph
Frontal
y Optimal Design
Discipline A
Discipline B Optimum
Madymo: FC1‐USNCAPFC2‐EuroNCAP
Design Domain: Discipline ADesign Domain: Discipline BFeasible Design Domain
Discipline A Optimum
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Design Variables
MDO-based Platform Development
• The platform should support:
- Easily access to remote analysis tools and bring together multiple analysis tools into an integrated system analysis while hiding the details of data management;
- High performance computing for a large amount of CAE runs for large-scale design problem;
Advanced metamodel techniques for better prediction of high non linear responses for- Advanced metamodel techniques for better prediction of high non-linear responses for vehicle design;
- Parameter model synchronization techniques for seamlessly integrating all theParameter model synchronization techniques for seamlessly integrating all the attributes together to achieve “One model driven design” concept;
- Cost and process mathematical model development for vehicle attributes integration.
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MDO-based Platform Development
• Framework of MDO-based Platform:
Effective approximated model computation intensive for high fidelity models
Key techniques enable the innovative design to be a reality in automotive industry
Effective approximated model: computation intensive for high fidelity modelsy
Low-fidelity surrogate model
Tests / High-fidelity CAE model
Prediction meanˆ ( )ey x d
22 2
12
2 2
( ( , ))ˆ( )(2 )ln ln exp( )2(2 ) det
nm
i ii
n
y A xprobQ
aa | A,I
K
Design space identification: Large number of design variables and constraints
x / 2ˆ ˆ( ) diag MSE ( )e ez y x d y x d
×100% PI (prediction interval)
( )y x d
Design space identification: Large number of design variables and constraints
y
z Original design spaceReduced design space
Unfeasible designsFeasible designs
Parent node
Parent node Reduced design space 2
Rule 1Rule 2
Efficient RBDO technique: Variabilities of design variables, data uncertainty
x
y
Reduced design space1 Unfeasible space
ff q g y
Manfacturing Loading
Simulation of Discipline ‘1’
Variation of Product
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Material OthersSimulation of Discipline ‘n’
MDO-based Platform DevelopmentGenerally,
• Metamodel-based design optimization:
- One of major deficiencies of using metamodel in design optimization is the poor accuracy due to the lack of data uncertainty.
- A metamodel technique based on model bias correction method*, which has been well proven in precious published work, is used:
metamodel experimental error of the
)()()( xxyxy me
metamodel experimental error of the physical observation
physical observations or the high-fidelity finite element model
bias function or model discrepancy function
Generally, is assumed as the Gaussian Process model, since it has a good way of quantifying the prediction uncertainty
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* L. Shi, R.J. Yang et al.,An adaptive response surface method using Bayesian metric and model bias correction function, ASME Journal of Mechanical Design, 2014, 136(3):1-8
MDO-based Platform Development
• Cost/Process mathematical model development:
- cost/process model tool is developed using VB scripts and implemented in Excel etc.
Cost Model =VÉáà { Material, Geometry, Structure, Connection, Process parameter etc. }
Weight Model =jx|z{à{Material, Geometry, Structure, Connection, Process parameter etc.}
Cost Model VÉáà { Material, Geometry, Structure, Connection, Process parameter etc. }
Process Model =cÜÉvxáá { Material, Geometry, Structure, Connection, Process parameter etc. }
Attribute Model=TààÜ|uâàx{ Material, Geometry, Structure, Connection, etc. }
12
MDO-based Platform Development
• Cost/Process mathematical model development:
- cost/process model tool is developed using Data Mining tech and implemented in Excel etc.
13
Application on Vehicle Body Design
• Strategy for body design:
Fi d th li ht t & ffi i tPredecessor
Find the lightest & efficient structure that is able to meet design targets under a given set of conditions & loadsRight
i
architecture
set of conditions & loads
Righttopology
size
Rightdesign
Rightmaterial
designdetails
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New vehicle
Application on Vehicle Body Design
• Strategy for body design:
i li l ibib ibib i li lMaterial Material AnalysisAnalysis
Attribute Attribute IntegrationIntegration
Attribute Attribute ValidationValidation
Material Usage Material Usage ConfirmationConfirmation Cost AnalysisCost Analysis
1%
29%
25%
13%
Weight
AlLSSHSSAHSS
NVH: Torsion, Bending, Dynamic stiffness
32%
25% PHS
偏置碰:64kph
正碰:35mph
Madymo: FC1‐USNCAPFC2‐EuroNCAP
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Application on Vehicle Body Design
• Strategy for body design:
i li l ibib ibib i li lMaterial Material AnalysisAnalysis
Attribute Attribute IntegrationIntegration
Attribute Attribute ValidationValidation
Material Usage Material Usage ConfirmationConfirmation Cost AnalysisCost Analysis
M t i lMaterial usage of baseline BIW Material Weight (Kg)
Material usage of baseline BIW
28 t pes of sheet metal and16 t pes are ithin 10 Kg 28 types of sheet metal, and16 types are within 10 Kg 72 grades of sheet metal, and 60% are within 2 Kg
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Application on Vehicle Body Design
• Strategy for body design:
i li l ibib ibib i li lMaterial Material AnalysisAnalysis
Attribute Attribute IntegrationIntegration
Attribute Attribute ValidationValidation
Material Usage Material Usage ConfirmationConfirmation Cost AnalysisCost Analysis
Baseline Mat. Update Mat. Baseline Mat. Update Mat.
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Application on Vehicle Body Design
• Strategy for body design:
i li l ibib ibib i li lMaterial Material AnalysisAnalysis
Attribute Attribute IntegrationIntegration
Attribute Attribute ValidationValidation
Material Usage Material Usage ConfirmationConfirmation Cost AnalysisCost Analysis
Investigate Potential Process and Materials for Body Design
d C T
Define Engineering Problem
under Cost Target
Build up Initial Simulation Model to Perform CAE Runs
Conduct Design Sensitivity Analysis to Identify the Critical Design Variables
Build up Parameter Model to Setup Morph Parameter
BIW Trimmed-body Safety
MDO性能集成
Define DOE Submit DOE Jobs
Extract CAE Results
Construct Metamodel
Trimmed-body�VTF/NTF/IPI
Trimmed-body�Acoustic Mode
BIW�NM
Bending Stiff
Full�Frontal
40% OffsetPerform Optimization
Achieve Targets EndYes
Finite Element Validation
Acoustic�Mode
Trimmed-body�Bending�Mode
Bending�Stiff
Torsion�Stiff
40%�Offset
Side�Impact
18
g?
Trade-off Study
NoTrimmed-body�Torsion�Mode
BIW�IPIRoof�Crush
Application on Vehicle Body Design
• Strategy for body design:
i li l ibib ibib i li lMaterial Material AnalysisAnalysis
Attribute Attribute IntegrationIntegration
Attribute Attribute ValidationValidation
Material Usage Material Usage ConfirmationConfirmation Cost AnalysisCost Analysis
-The optimization aims to minimize the weight while maintaining thexWeightMin )( -The optimization aims to minimize the weight while maintaining the target attributes- A non-dominated sorting genetic algorithm (NSGA-II) is employed to solve the equation.etsTSafetyxAttributesSafety
etsTNVHxAttributesNVHtoSubject
xWeightMinRx
arg )( arg )(
:
)( 138
AttributesSafety NVH
Full frontal 40% offset Side impact Roof crush Body-in-white Trimmed b d
Summary of vehicle attributes analysisboundUpperxbound Lower
Full frontal 40% offset Side impact Roof crush Body in white bodyElements
Nodes25122412059953
25763602131036
25122582057233
13346421266096
1006834991074
15469321539606
Run-time 16h (12CPUs) 38h (12CPUs) 27h (12CPUs) 12h (12CPUs) 0.5h (4CPUs) 4h (4CPUs)CAE Solver Ls-dyna Ls-dyna Ls-dyna Ls-dyna MSC.Nastran MSC.Nastran
Response Acc, intrusion, etc. Intrusion Velocity,
intrusion, etc. Force load, etc. Normal mode, stiffness, etc.
NTF, VTF, IPI, etc.
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FE Model
Application on Vehicle Body Design
• Strategy for body design:
i li l ibib ibib i li lMaterial Material AnalysisAnalysis
Attribute Attribute IntegrationIntegration
Attribute Attribute ValidationValidation
Material Usage Material Usage ConfirmationConfirmation Cost AnalysisCost Analysis
Two metrics:The Lightweight Index (LI) is used as the indicator of weight reduction, which is defined as:
2d/kN
kgAK
mL BIW
where is weight of body-in-white, is global static torsion stiffness and A is the projected area of wheel base multiplying by track.
2deg/ mmkNmmAKT
BIWmTK
The Attributes Available Space (AAS) is first proposed in this work to evaluate overall full vehicle performance, and defined as:
M tTAtt ib t1
Lightweight index
where is the number of responses of one discipline (Safety, or NVH), is the target
M
i i
ii
etTetTAttributes
MAAS
1 argarg1
M ietsT arg
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requirement for each attribute. If the attributes are required to meet the inequation “ ”, then AAS is negative. The AAS is lower, and the performance is more conservative.
ii etsTAttributes arg
Application on Vehicle Body Design
• Strategy for body design:
i li l ibib ibib i li lMaterial Material AnalysisAnalysis
Attribute Attribute IntegrationIntegration
Attribute Attribute ValidationValidation
Material Usage Material Usage ConfirmationConfirmation Cost AnalysisCost Analysis
Summary of vehicle attributes and weight reduction
Items Initial MDO DifferenceLightweight index 3.96 3.72 ---AAS for safety -22% -25% 3%AAS for NVH -18% -20% 2%
Summary of vehicle attributes and weight reduction
AAS for NVH 18% 20% 2%Mass (Kg) 379.3 369.7 -9.56
New DesignPredecessor New DesignPredecessor0
18%
0g g
+2%+3%
-22%-25%
-18%-20%
AAS
AAS
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Safety attributes NVH attributes
Application on Vehicle Body Design
• Strategy for body design:
i li l ibib ibib i li lMaterial Material AnalysisAnalysis
Attribute Attribute IntegrationIntegration
Attribute Attribute ValidationValidation
Material Usage Material Usage ConfirmationConfirmation Cost AnalysisCost Analysis
Torsion Rigidity: +16%Bending Rigidity: 6%
New Design
To optimize roof
Tors
ion
Predecessor
Optimize A-pillar cross sec to
To improve the joint stru. to get high rigidity connection
To optimize roof cross-member with better and larger sectionBending
cross sec. to achieve better stiff.
Optimize A-pillar inner
To optimize the welded line of D-pillar inner plate to improve torsion rigidity
p pjoint. to achieve better stiff. & reduce weight.
To improve the joint stiffness
To optimize the cross-sec to improve torsion rigidity
To improve the joint stru. to get high rigidity connection
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Application on Vehicle Body Design
• Strategy for body design:
i li l ibib ibib i li lMaterial Material AnalysisAnalysis
Attribute Attribute IntegrationIntegration
Attribute Attribute ValidationValidation
Material Usage Material Usage ConfirmationConfirmation Cost AnalysisCost Analysis
Underbody Optimized GaugesUpperbody Optimized Gauges
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To�achieve�9.56�Kg weight�reduction��w/o�using�light�alloys
Application on Vehicle Body Design
• Strategy for body design:
i li l ibib ibib i li lMaterial Material AnalysisAnalysis
Attribute Attribute IntegrationIntegration
Attribute Attribute ValidationValidation
Material Usage Material Usage ConfirmationConfirmation Cost AnalysisCost Analysis
29%0%11%
29%20%
Low Strength Steels
40%
High Strength Steels Advanced High Strength Steels Ultra High Strength Steels Press Hardened Steels
11%
• Optimization for material utilization starts during the concept and design phase
29%23%
3%11%g p g p• CAE is used to evaluate the vehicle attributes, e.g., safety & durability etc.• AHSS is used from 20% to 23%• B-pillar inner & cross-member are enhanced
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34%
pw/ 3% UHSS usage
Application on Vehicle Body Design
• Strategy for body design:
i li l ibib ibib i li lMaterial Material AnalysisAnalysis
Attribute Attribute IntegrationIntegration
Attribute Attribute ValidationValidation
Material Usage Material Usage ConfirmationConfirmation Cost AnalysisCost Analysis
Mi d ti l iOriginal Material Usage Proposed MaterialMixed nesting analysis oposed ate a
UsageMaster component
Slavecomponent
Master component
Slavecomponent
Master component Slave component Mat Thickness(mm)
Mat Thickness(mm)
Mat Thickness(mm)
Innrein panel of Rewheel cover Mounting nut plate of Rde B250P1 1 5 B250P1 1 5 B250P1 1 5Innrein. panel of Rewheel cover height sensor B250P1 1.5 B250P1 1.5 B250P1 1.5
Left ACC fixed Brcket Rein. panel of left egin. susp SAPH440 2.5 SAPH440 2.5 B250P1 2.0
Rein. Panel of lf/rt susp mout Fixd bracket of lf/rt braking tubing SAPH440 3.0 SPHC 2.5 B250P1 2.0
Inner panel of C-pillarFixed braket of lower C-pillar DC04 0.8 DC01 1.2 DC51D+Z 1.0Fixed bracket of lower tubing DC01 1.2C t d l f Ot h l DC01 1 2Cncted panel of Otr wheel cover DC01 1.2
Connected panel of Eng.Cab.Bracket of water box DC51D+Z 1.2 DC51D+Z 1.2 DC51D+Z
1.2Bracket of safety box DC51D+Z 1.2PEPS bracket DC51D+Z 1.2
Conn. Rear Cross RfMemr Fixed panel of IP tube DC03 1.2 DC51D+Z 1.5 DC51D+Z 1.5Rear outer panel Outer water tank strt DC53D+Z 0.7 DC53D+Z 0.7 DC53D+Z 0.7
Outer water tank strt1 DC53D+Z 0 7 0 7
Side outer panel
Outer water tank strt1DC56D+Z 0.7
DC53D+Z 0.7DC56D+Z
0.7Supt.Outer plate DC01 0.8 0.6Fixed brkt of liftgate Ct DC01 0.8 0.6Sup. Brkt of Rr lighting Sys DC53D+Z 0.7 0.7Connection panel of Re Cov DC51D+Z 0.8 0.7Brkt of Lf/Rr Side Sensor DC01 1.2 0.7R i P l f R D l ki B210P1 1 2 0 7
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Rein. Panel of Rr Dr locking B210P1 1.2 0.7Outer panel of Lf/Rr Wheel Cover
IP panel fixed Brkt HC220YD+Z 1.4 HC220YD+Z 1.4 HC220YD+Z 1.4IP panel fixed Brkt-II HC220YD+Z 1.4
Application on Vehicle Body Design
• Strategy for body design:
i li l ibib ibib i li lMaterial Material AnalysisAnalysis
Attribute Attribute IntegrationIntegration
Attribute Attribute ValidationValidation
Material Usage Material Usage ConfirmationConfirmation Cost AnalysisCost Analysis
Stamping costStamping cost
Material cost Production cost Tooling cost Others
Blank
Scrap
Material lo
Energy
Equipme
Labor
Plant
Maintenan
Quality con
Mold
Jig
Check fixt
Managem
Packagedelivery
Othersoss
y ent
nce
ntrol
ture
ment
e &
y s
Achieve 153RMB Cost Saving.Achieve 153RMB Cost Saving.
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Summary
A high-fidelity MDO-based platform is successfully implemented by using advanced optimization methodology, parameter model synchronization and pre/post processing etc., and provide several major elements:
- Facilitate collaboration of discipline experts/engineers that in turn facilitate steering of the design and enhance efficiency of product development.design and enhance efficiency of product development.- Offer well-founded decision making or trade-off tool for conflicting design target during product development process.- Provide the CAE or design engineer the design direction or space to validate the multiple attribute requirements when the design targets are not met.attribute requirements when the design targets are not met.- Achieve a superior design meeting design target faster through the modification and enhancement of design model from the engineering judgment and expert inputs.
A li ti i d t i l l f i ht d ti i ll d t t d th h th A realistic industrial example for weight reduction is well demonstrated through the proposed platform and achieved a 9.56 Kg weight saving and over 20 Kg raw material mass saving to achieve over 150 RMB cost saving.
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