Michelin case

• Until 2011: development of new tyre

through testing of different prototypes

• For each test: a prototype has to be

produced, tested and analyzed

• This was very time consuming and costly

• And tends to alchemy ! The most experienced guy!

OK/ntO

k

Not OK

OK

# of prototype cycling time = 20 cycles

Tested towards following objectives:

• Durability

• Aquaplaning

• Environmental friendliness

• Costs

• From 2011: development of new tyre

through use of CAE-software

for design of optimal aquaplaning tyre

SlideSlide 1616BELGISCHE STARTCONFERENTIE KP7 30-01-2007

History of TROPHY projectHistory of TROPHY project

Complexity of hydroplaning

• Use of CAE-software for optimal aquaplaning tyre

FrauenhoferM

pCCSI

soware

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• Result :

– Less prototypes needed

• Thus cheaper R&D costs

– Quicker result : shorter time-to-market

• Thus more competitive

– And safer tyre

OK/ntO

k

Not OK

OK

# of prototype cycling time = 15 cycles

Still Tested towards following objectives:

• Aquaplaning • Durability • Environmental friendliness • Costs

Use of CAE-software

Prototype cycling time reduced with 25%

Objective=

aquaplaning Scale of Aquaplaning degree

0 10 7

• This CAE software results for the OBJECTIVE = aquaplaning in a most optimal solution

FrauenhoferM

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8

result

• But:...uncertainties road conditions, tyre production, rain conditions

• Reliability result ?

• Results will probably vary

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Objective=

aquaplanning Scale of Aquaplaning degree

0 10 7 8

result

• Probably Gaussian distribution

• Michelin’s ultimate target is to be able to simulate / design, through CAE-software the most optimal tyre

• which satisfies to the following multiple

objectives

– Least sensitive for aquaplaning

– the most durable

– the most environmental friendly

– And cheapest tyre !

• State of the Art today :

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BJEC

TIV

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aq

ua

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Result

OB

JEC

TIV

E

du

rab

ility

InputsPX CAE-ModelXCAE

simulat.OutputX

Aquaplanning

OB

JEC

TIV

E

en

viro

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InputsPX CAE-ModelXCAE

simulat.OutputX

Du

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Objectives

OB

JEC

TIV

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InputsPX CAE-ModelXCAE

simulat.OutputX

NO

ESIS

op

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ftw

are

• State of the Art today :

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BJEC

TIV

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aq

ua

pla

nn

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Result

OB

JEC

TIV

E

du

rab

ility

InputsPX CAE-ModelXCAE

simulat.OutputX

Aquaplanning

OB

JEC

TIV

E

en

viro

nm

en

t

InputsPX CAE-ModelXCAE

simulat.OutputX

Du

rab

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Objectives

OB

JEC

TIV

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InputsPX CAE-ModelXCAE

simulat.OutputX

NO

ESIS

op

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ftw

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Adding 4° objective ?

• For 1 objective +/- reliable

– We can determine a relative accurate optimum

– Because uncertainties are causing little impact

• For multiple objectives not reliable

– Uncertainties accumulated above uncertainties make global optima unreliabel

Aquaplanning

Envi

ronm

ent

Du

rab

ility

Aquaplanning

Envi

ronm

ent

Du

rab

ility

The euforia dream

• To design/ simulate

based on multiple

CAE-software tools,

the perfect tyre

• To find, for a series of multiple objectives, a robust global optima

Added value for companies

• Savings on R&D time and costs

• Decrease time-to-market

• Decrease the company’s

product responsibility risk

• Result in better, more efficient, more performant products and processes

• Change company know-how from “alchemy” to “science”

• And thus boost the company’s competitiveness

• But also define better product marketing strategies

An

nu

al C

ost

s (E

UR

/ite

m)

Effectiveness

Pareto Front Most optimal robust designs

Initial product

Sales price Initial product

C0

E0

Graph A: Positioning initial product vs Pareto Front

New design A

New design B

Sales price positioning B

Sales price Initial product Sales price positiong A

They consider this issue as a major shortcomming of Computer Based Engineering

• Sic Roll Royce

“Optimization under uncertainties is the

following big scientific challenge for Computed Aided Engineering !”