SlideShare a Scribd company logo
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
FrauenhoferMpCCSI
soware
!"#$%&'( ) '
*$+ , -. '/01'
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/01'
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9 $%#$%': '
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&456 . /7'
123'
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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
0 107
• This CAE software results for the OBJECTIVE =
aquaplaning in a most optimal solution
FrauenhoferMpCCSI
soware
!"#$%&'( ) '
*$+ , -. '/01'
&234 . 5, '
/01'
&6+ $7. %8'
9 $%#$%': '
!"#$%&'( ) '
* +, -* . /0'123'
&456 . /7'
123'
&89 $:. %-'
; $%#$%'<'
8
result
• But:...uncertainties road conditions, tyre
production, rain conditions
• Reliability result ?
• Results will probably vary
FrauenhoferMpCCSI
soware
!"#$%&'( ) '
*$+ , -. '/01'
&234 . 5, '
/01'
&6+ $7. %8'
9 $%#$%': '
!"#$%&'( ) '
* +, -* . /0'123'
&456 . /7'
123'
&89 $:. %-'
; $%#$%'<'
Objective=
aquaplanning Scale of
Aquaplaning
degree
0 107 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 :
FrauenhoferMpCCSI
soware
!"#$%&'( ) '
*$+ , -. '/01'
&234 . 5, '
/01'
&6+ $7. %8'
9 $%#$%': '
!"#$%&'( ) '
* +, -* . /0'123'
&456 . /7'
123'
&89 $:. %-'
; $%#$%'<'
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
• State of the Art today :
FrauenhoferMpCCSI
soware
!"#$%&'( ) '
*$+ , -. '/01'
&234 . 5, '
/01'
&6+ $7. %8'
9 $%#$%': '
!"#$%&'( ) '
* +, -* . /0'123'
&456 . /7'
123'
&89 $:. %-'
; $%#$%'<'
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 ?
• 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
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
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
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 !”

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The michelin case

  • 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 FrauenhoferMpCCSI soware !"#$%&'( ) ' *$+ , -. '/01' &234 . 5, ' /01' &6+ $7. %8' 9 $%#$%': ' !"#$%&'( ) ' * +, -* . /0'123' &456 . /7' 123' &89 $:. %-' ; $%#$%'<'
  • 4. • 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%
  • 5. Objective= aquaplaning Scale of Aquaplaning degree 0 107 • This CAE software results for the OBJECTIVE = aquaplaning in a most optimal solution FrauenhoferMpCCSI soware !"#$%&'( ) ' *$+ , -. '/01' &234 . 5, ' /01' &6+ $7. %8' 9 $%#$%': ' !"#$%&'( ) ' * +, -* . /0'123' &456 . /7' 123' &89 $:. %-' ; $%#$%'<' 8 result • But:...uncertainties road conditions, tyre production, rain conditions • Reliability result ?
  • 6. • Results will probably vary FrauenhoferMpCCSI soware !"#$%&'( ) ' *$+ , -. '/01' &234 . 5, ' /01' &6+ $7. %8' 9 $%#$%': ' !"#$%&'( ) ' * +, -* . /0'123' &456 . /7' 123' &89 $:. %-' ; $%#$%'<' 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 : FrauenhoferMpCCSI soware !"#$%&'( ) ' *$+ , -. '/01' &234 . 5, ' /01' &6+ $7. %8' 9 $%#$%': ' !"#$%&'( ) ' * +, -* . /0'123' &456 . /7' 123' &89 $:. %-' ; $%#$%'<' 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 : FrauenhoferMpCCSI soware !"#$%&'( ) ' *$+ , -. '/01' &234 . 5, ' /01' &6+ $7. %8' 9 $%#$%': ' !"#$%&'( ) ' * +, -* . /0'123' &456 . /7' 123' &89 $:. %-' ; $%#$%'<' 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 !”