application of a functional adaptive simulation … · 2016-02-20 · taguchi principles...

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Institut für Maschinenelemente, Universität Stuttgart

Technologie Transfer Initiative, Universität Stuttgart

Rob

ust

Se

nsitiv

e

Robust Design

in commercial vehicle braking systems

12. Weimar Optimization and Stochastic Days 2015

WOST 2015

Dipl.-Ing. Stefan Kemmler

M.Sc. Alexander Fuchs *

Dr.-Ing. Tobias Leopold *

Prof. Dr.-Ing. Bernd Bertsche

* Knorr-Bremse Group

Systeme für Nutzfahrzeuge GmbH

Institute of Machine Components

Reliability Engineering

Technologie Transfer Initiative

APPLICATION OF A FUNCTIONAL ADAPTIVE

SIMULATION MODEL FOR THE ROBUST

PRODUCT-OPTIMIZATION

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Outline of the presentation

Introduction to the optimization strategies RDO and Taguchi

Application example: adjusting unit

Functional adaptive simulation strategy SIM-SMAR2T

RDO and Taguchi in optiSLang

Summary and conclusion

2Agenda

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INTRODUCTION TO THE

OPTIMIZATION STRATEGIES RDO

AND TAGUCHI

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Taguchi principles

4Introduction to the optimization strategies RDO and Taguchi

Source: Fowlkes, 1995

Step 1:

Reduce variability with the aim

of S/N Ratio.

Step 2:

Go back to your target.

S/N Ratio (i.e.):

Nominal-the-best (type II)

𝜂 = −10 𝑙𝑜𝑔 (𝜎2)

Target

Probability

density

f

Target

Probability

density

f

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Robust Design Optimization (RDO)

Robust optimization

Using different optimization methods

Identification of global and local models of robustness

Usage of expected value as robust value

Target:

Parameter-Optimization

based on merged mean value

and corresponding variance.


parameter x2

initial design

global optimum

robust optimum

variance of the target

valid proof of

parameter space

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Comparison of both Robust Design methods


Robust Design Optimization (RDO)

Exclusive consideration of sensitive designs

Target and constraint are predefined

Simultaneous optimization of µ and σ

Optimum mostly not feasible regarding manufacturing

Taguchi method(TM)

Clear separation of the parameter through arrays

No direct specification of constraints

Separate optimization of µ and σ

Parameters dimension based on manufacturing aspects

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APPLICATION EXAMPLE:

ADJUSTING UNIT

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Adjusting Unit

Between the brake pad and the brake disc a defined clearance is

necessary

Protection against dragging of the brake

Too large clearance reduces braking efficiency

8Adjusting Unit

adjusting

unit

lever

bridge

brake

caliper

brake

carrier

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Adjusting Unit

Purely mechanical effect relationships

Main function is to compensate the wear of the pads and the disc

Clearance is obtained by geometric elements in the adjusting unit

Function includes five modes of operation:

9Adjusting Unit

shift fork

freewheel

overload clutch

return spring

output (spindle)

Overcome constructive clearance

AdjustmentOverload –decoupling

Retracting movement

Service function

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FUNCTIONAL ADAPTIVE

SIMULATION STRATEGY SIM-

SMAR2T

10

FE model

Temperature

Fitting Curve

META model (MOP)

Response Surface

SIM-SMART (BASIS model)(1D model with GUI)

Varia

ble

A

Varia

ble

B

Varia

ble

C

Varia

ble

D

ANSYS

Wear

STEP 2:

META model (MOP) modeling with optiSLang

Target: continuous correlation

Variable A

Variable D

FIT

Parameter-

indentification of the

1D model

STEP 1:

SUB model X modeling

META model simulation

Target: Behaviour of input- and output-parameters

META model (MOP)

Response Surface

Variable B

Variable C

Coefficien

t of frictio

n

Dia

mete

r

Wear

Tem

pera

ture

SUB X

optiSLang

STEP 3:

META model (MOP) integration

Interaction between the models

Parameter variation / tolerance analyse

Direct parametersVariable / parameter in

1D model

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Parameter Design – simulation strategy

Define the expedient simulation strategy!

11Functional adaptive simulation strategy SIM-SMAR2T

Source: Kemmler, 2014

Overlo

ad (

no

rm.)

[N

m]

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0

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34

56

7

515

2535

45

FE-

Model

SUB-

MODEL 2

SUB-

MODEL 1

SUB-

MODEL 3

- Forwards - - Adjust - - Overload -

SUB-

MODEL 4

- Backwards -

ϑ

µ

M

ϑ

µ

M

Φ

ϑ

µ

M

Φ

META-

ModelΦ

SIM-

SMAR2T

M M M M

+ + = ..Φ Φ Φ Φ

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Simulation strategy - overview


FE model

Temperature

Fitting Curve

META model (MOP)

Response Surface


Varia

ble

A

Varia

ble

B

Varia

ble

C

Varia

ble

D

ANSYS

Wear

STEP 2:



Variable A

Variable D

FIT

Parameter-


1D model

STEP 1:




META model (MOP)

Response Surface

Variable B

Variable C

Coefficien

t of frictio

n

Dia

mete

r

Wear

Tem

pera

ture

SUB X

optiSLang

STEP 3:





1D model

Source: Kemmler, 2014

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FE model

Temperature

Fitting Curve

META model (MOP)

Response Surface


Varia

ble

A

Varia

ble

B

Varia

ble

C

Varia

ble

D

ANSYS

Wear

STEP 2:



Variable A

Variable D

FIT

Parameter-


1D model

STEP 1:




META model (MOP)

Response Surface

Variable B

Variable C

Coefficien

t of frictio

n

Dia

mete

r

Wear

Tem

pera

ture

SUB X

optiSLang

STEP 3:





1D model

Simulation strategy – STEP 1

13Functional adaptive simulation strategy SIM-SMAR2T – STEP 1

FE model

Temperature

Fitting Curve

ANSYS

Wear

Variable A

Variable D

FIT

Parameter-


1D model

STEP 1:




Variable B

Variable C

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FE model

Temperature

Fitting Curve

META model (MOP)

Response Surface


Varia

ble

A

Varia

ble

B

Varia

ble

C

Varia

ble

D

ANSYS

Wear

STEP 2:



Variable A

Variable D

FIT

Parameter-


1D model

STEP 1:




META model (MOP)

Response Surface

Variable B

Variable C

Coefficien

t of frictio

n

Dia

mete

r

Wear

Tem

pera

ture

SUB X

optiSLang

STEP 3:





1D model



STEP 2:

META-Model (MOP) Modeling with optiSLang

Target: Continuous Correlation

META-Model (MOP)

Response Surface

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FE model

Temperature

Fitting Curve

META model (MOP)

Response Surface


Varia

ble

A

Varia

ble

B

Varia

ble

C

Varia

ble

D

ANSYS

Wear

STEP 2:



Variable A

Variable D

FIT

Parameter-


1D model

STEP 1:




META model (MOP)

Response Surface

Variable B

Variable C

Coefficien

t of frictio

n

Dia

mete

r

Wear

Tem

pera

ture

SUB X

optiSLang

STEP 3:





1D model



META model (MOP)

Response Surface


Varia

ble

A

Varia

ble

B

Varia

ble

C

Varia

ble

D

Coefficien

t of frictio

n

Dia

mete

r

Wear

Tem

pera

ture

SUB X

STEP 3:




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Transfer in optiSLang


Step 1:

Store all system

parameters to sensitivity

Step 2:

Transfer the

respective para-

meters to the

respective MOP

Step 3:

Send MOP results to automatized

calculation script

Step 4:

Send results to

sensitivity module

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Results of SIM-SMAR2T

Significant factors are level systems and

plays of the adjusting unit.


ℎ𝑁 = 𝑓𝑁(ℎ𝑖𝑑𝑙𝑒 − 𝑠𝑐𝑐)

Components

K ball

CW cone washer

LB bearing bushing

LS bearing washer

PS adjusting washer

Abbreviation

AS output clearance

D_I Inner diameter

W angle

EZ short lever arm (X)

KS constructive clearance

ZFS flank clearance

y_EZ short lever arm (Y)

z_H distance lever-brake

Inp

ut P

ara

me

ter

E-Modulus_LS_LB (<1 %)

E-Modulus_CW (<1 %)

E-Modulus_K (<1 %)

D_I_PS (<1 %)

D_I_LB (<1 %)

Z_H (1 %)

W_ZFS (9 %)

y_EZ (12 %)

W_KS (36 %)

W_AS (41 %)

Coefficient of Importance (CoI) [%]

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RDO AND TAGUCHI IN OPTISLANG

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Taguchi in optiSLang – Design of experiments

Taguchi - Workflow in optiSLang

19RDO and Taguchi in optiSLang

Outer array: L12(211)

Inner array: L81(332)

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Taguchi and SIM-SMAR2T in optiSLang


design system parameters

1

2

…

n

P1 P2 P3 … Pn

P1 P2 P3 … Pn

P1 P2 P3 … Pn

P1 P2 P3 … Pn

alpha

α1

α2

…

αn

Overlo

ad (

no

rm.)

[N

m]

1

0,8

0,6

0,4

0,2

0

12

34

56

7

515

2535

45

Angle [º]To

rqu

e [N

m]

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Approach for RDO in optiSLang

ARSM-results:


Reason: noise

Solution:

Meta-Meta-Model

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30,129,829,5

18,16

18,08

18,00

1,00,80,5 1,00,80,6

766044

18,16

18,08

18,00

0,960,800,64 29,629,4529,3

2,01,851,7

18,16

18,08

18,00

1,41,10,8 1,21,00,8

D_H_V Z_I

Me

an

of

SN

ra

tio

s

R_H_V Z R_H_R_I

W_H_A S_H D_SpH_V Z_A

R_SpH_V Z R_SpH_R_A S_SpH

Main Effects Plot for SN ratios

Signal-to-noise: Nominal is best (-10*Log10(s**2))

30,129,829,5

1,00

0,75

0,5

1,10,80,5 1,00,80,6

766044

1,00

0,75

0,5

0,950,800,65 29,629,4529,3

2,01,851,7

1,00

0,75

0,5

1,41,10,8 1,21,000,8

D_H_VZ_I

Me

an

of

Me

an

s

R_H_VZ R_H_R_I

W_H_A S_H D_SpH_VZ_A

R_SpH_VZ R_SpH_R_A S_SpH

Main Effects Plot for Means

Results of both tools – S/N-Ratio

Taguchi-Method Robust Design Optimization


30,129,829,5

18,16

18,08

18,00

1,00,80,5 1,00,80,6

766044

18,16

18,08

18,00

0,960,800,64 29,629,4529,3

2,01,851,7

18,16

18,08

18,00

1,41,10,8 1,21,00,8

D_H_VZ_I

Mean

of

SN

rati

os

R_H_VZ R_H_R_I

W_H_A S_H D_SpH_VZ_A

R_SpH_VZ R_SpH_R_A S_SpH

Main Effects Plot for SN ratios

Signal-to-noise: Nominal is best (-10*Log10(s^2))

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Results of both tools – mean values


0

5

10

15

20

25

30

35

40R_H_VZ

D_SpH_VZ_A

D_H_VZ_I

R_SpH_VZ

R_SpH_R_AW_H_A

R_H_R_I

S_SpH

S_H

Difference [%]

Parameter UnitBest Design

RDO

Best Design

TM

Difference

[%]

W_AS [°] 0,820 0,790 3,66

σ [°] 0,091 0,092 1,09

Design - Parameter Sensitivity

Results due to the target:Robustness-analysis with ALHS!

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24

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Functional adaptive simulation strategy SIM-SMAR2T

Results of SIM-SMAR2T

General investigation of the approaches RDO and Taguchi Method

Application of both methods using the output clearance of the

adjuster unit as an example

Evaluation of the results

Construction of an universal flow chart for the application related

optimization according to RDO and/or Taguchi Method

Conclusion: Both approaches lead to the same objective

25Summary and conclusion

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Flowchart (printed in paper):

Detailed and universal approach

Provides the developer a clear

structure and decision aid

Approach for the pre-process to

design the right meta-model in

respect to:

Coupled terms

Regression-method

Interactions

Handling with sensitivities

Realization of RDO or TM

26Summary and conclusion

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References

Fowlkes, 1995:

Fowlkes W.Y., Creveling C. M., “Engineering Methods for Robust

Product Design: Using Taguchi Methods in Technology and Product

Development”, Addison-Wesley, 1995

Kemmler, 2014:

Kemmler, S., et al., “Method for the development of a functional

adaptive simulation model for designing robust products”, 11th Weimar

Optimization and Stochastic Days, 2014

27

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THANK YOU FOR YOUR

ATTENTION.

Institute of Machine Components

University of Stuttgart

http://www.ima.uni-stuttgart.de

[email protected] STUTTGART

Knorr-Bremse

Systeme für Nutzfahrzeuge GmbH

http://www.knorr-bremse.com

[email protected]

http://www.ima.uni-stuttgart.de/

mailto:[email protected]

http://www.knorr-bremse.com/

mailto:[email protected]

application of a functional adaptive simulation … · 2016-02-20 · taguchi principles...

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