a systematic approach for weight reduction and … · a systematic approach for weight reduction...

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

6

How to freeze data?


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.

7


• 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

8

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.

9


• 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

10

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


• 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


• 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

14

New vehicle



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

15







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

16







Baseline Mat. Update Mat. Baseline Mat. Update Mat.

17







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







-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.

19

FE Model







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

20

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







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

21

Safety attributes NVH attributes







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

22







Underbody Optimized GaugesUpperbody Optimized Gauges

23

To�achieve�9.56�Kg weight�reduction��w/o�using�light�alloys







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

24

34%

pw/ 3% UHSS usage







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

25

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







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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a systematic approach for weight reduction and … · a systematic approach for weight reduction...

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