Uncertainty quantification @ Michelin

1. 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

2. • 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

3. • Use of CAE-software for optimal aquaplaning
tyre
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4. • Result :
– Less prototypes needed
• Thus cheaper R&D costs
– Quicker result : shorter time-to-market
• Thus more competitive
– And safer tyre
OK/
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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%

5. Objective=
aquaplaning Scale of
Aquaplaning
degree
0 107
• 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 ?

6. • Results will probably vary
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Objective=
aquaplanning Scale of
Aquaplaning
degree
0 107 8
result
• Probably Gaussian distribution

7. • 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 !

8. • State of the Art today :
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OBJECTIVE
aquaplanning
Result
OBJECTIVE
durability
Inputs PX CAE-Model X
CAE
simulat.
Output X
Aquaplanning
OBJECTIVE
environment
Inputs PX CAE-Model X
CAE
simulat.
Output X
Durability
Objectives
OBJECTIVE
cost
Inputs PX CAE-Model X
CAE
simulat.
Output X
NOESISoptimisationsoftware

9. • State of the Art today :
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OBJECTIVE
aquaplanning
Result
OBJECTIVE
durability
Inputs PX CAE-Model X
CAE
simulat.
Output X
Aquaplanning
OBJECTIVE
environment
Inputs PX CAE-Model X
CAE
simulat.
Output X
Durability
Objectives
OBJECTIVE
cost
Inputs PX CAE-Model X
CAE
simulat.
Output X
NOESISoptimisationsoftware
Adding 4° objective ?

10. • 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
Environm
ent
Durability
Aquaplanning
Environm
ent
Durability

11. 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

12. Added value for companies
• Savings on R&D time and costs
• Decrease time-to-market
• Decrease the company’s
product responsibility risk

13. • 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

14. • But also define better product marketing
strategies
AnnualCosts(EUR/item)
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

15. 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 !”