the michelin case
TRANSCRIPT
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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
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• 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
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• 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%
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Objective=
aquaplaning Scale of Aquaplaning degree
0 10 7
• This CAE software results for the OBJECTIVE = aquaplaning in a most optimal solution
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8
result
• But:...uncertainties road conditions, tyre production, rain conditions
• Reliability result ?
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• Results will probably vary
FrauenhoferM
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Objective=
aquaplanning Scale of Aquaplaning degree
0 10 7 8
result
• Probably Gaussian distribution
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• 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 !
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• State of the Art today :
FrauenhoferM
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BJEC
TIV
E
aq
ua
pla
nn
ing
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
ility
Objectives
OB
JEC
TIV
E
co
st
InputsPX CAE-ModelXCAE
simulat.OutputX
NO
ESIS
op
tim
isat
ion
so
ftw
are
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• State of the Art today :
FrauenhoferM
pCCSI
soware
!" #$%&'( ) '* $+ , - . '/ 01 '&234 . 5, '
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BJEC
TIV
E
aq
ua
pla
nn
ing
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
ility
Objectives
OB
JEC
TIV
E
co
st
InputsPX CAE-ModelXCAE
simulat.OutputX
NO
ESIS
op
tim
isat
ion
so
ftw
are
Adding 4° objective ?
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• 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
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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
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Added value for companies
• Savings on R&D time and costs
• Decrease time-to-market
• Decrease the company’s
product responsibility risk
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• 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
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• 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
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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 !”