realistic and customer correlated test schedules...4 week accelerated rig test scenario test...

Unrestricted © Siemens 2020 Where today meets tomorrow.

Realistic and customer

correlated test schedules

Unrestricted © Siemens 2020

2020.03.30 Siemens Digital Industries Software

Need for durability engineering

How to capitalize on these implications ?



Agenda

Loads and damage

Load characterization

Customer correlation

Accelerated testing and analysis

Customer application case



Road A

How to understand fatigue content of loads ?

Comparison of two measurements

Road B

Time Time

Hard





Road A Road B

DamageDamage

Time Time

Easy ☺




Damage calculation

Damage

Fatigue life (= 1/Damage)

S-N curve for

CA loading

Loads

Failure

t, time

σ, F

Rainflow counting

→ Range, mean & cycles

Damage accumulation

𝑖=1

𝐼𝑛𝑖𝑁𝑖



1E+00 1E+01 1E+02 1E+03 1E+04 1E+05 1E+06 1E+07 1E+08

What is an S-N curve ?

Characteristics

Infinite life

High cycle

fatigue(Elastic behavior)

Low cycle

fatigue(Plastic behavior)

Number of stress cycles to failure (N)

Str

ess a

mp

litu

de

(S

)

N1

S1

Sut

Se

Sy

Finite life




Endo (1968). Rainflow counting for variable loads

Tatsuo Endo*1925 1989

Rainflow counting

= technique to decompose a spectrum of varying, complex stress

into a set of simple stress reversals

0+ +=



Rainflow counting

Time5

4

3

2

1

10

9

8

7

6

2. Look for closed loops

3. Accumulate nr. of loop cycles

in ‘From-To’ cells of table

= Rainflow-matrix

‘From’

load

level

‘To’ load level

6

7

8

9

10

1

2

3

4

5

54321 109876

8

7

8

4

3

4

2 2

5

Load history

1. Classification

into bins




Rainflow characteristics

Zero range

Zero mean

Compression

Tension

Standing

Hanging

90000

60000

40000

30000

20000

10000

0

t

t0

- mean

t0

+ mean

t

Larger amplitudes(more damage)

Larger amplitudes(more damage)

To

Fro

m

# Cycles



Counting methods in fatigue analysis

Benefits

• Representations → easy to understand

• Ability for fatigue related data manipulation (editing, extrapolation, superposition)

• Easy way to compare data

• Simple counting algorithms

• Substantial reduction of data amount

Same counting result = Same fatigue potential

Counting

method




Palmgren (1924) – Miner (1945). Damage accumulation rule

Assume that, during the service life, we have

• 500 cycles of load type 1

(defined by mid-value and magnitude)

• 1000 cycles of load type 2

• and 10000 cycles of load type 3

the Palmgren-Miner rule states that failure occurs

when

with:

• ni = the number of actual applied load cycles

of type i

• Ni = the pertinent fatigue life for that specific

applied load cycle i

𝑖=1

𝐼𝑛𝑖𝑁𝑖

= 1

= 0.5 = 0.01 = 0




Rainflow → Range-pair → Damage → Cumulative damage

Range-pair (cycle count)

4 0.9 1000.0300.0100.030.010.03.01.0Cycle Count

184.1

11599.0

1000.0

2000.0

3000.0

4000.0

5000.0

6000.0

7000.0

8000.0

9000.0

10000.0

11000.0

[N]

File Channel Number of bins Lower Limit Upper Limit Unit Hysteresis Filter

D:\Program Files\LMS\TecWare 3.7SL1+\demo\general\nature.erfm FORCE_vert 64 -936.7174 10846.3457 [N] 1

Range Pair

Ra

ng

e

# Cycles

Cumulative damage

6 0 10010 20 30 40 50 60 70 80 90Cumulative Damage [100%=6.2541E-3]

184.1

11599.0

1000.0

2000.0

3000.0

4000.0

5000.0

6000.0

7000.0

8000.0

9000.0

10000.0

11000.0

[N]



Range Pair

Ra

ng

e

Pseudo-damage

Damage

accumulation

(Miner’s rule)

Range-pair (damage)

5 0.00000 0.006250.00050 0.00100 0.00150 0.00200 0.00250 0.00300 0.00350 0.00400 0.00450 0.00500 0.00550Damage

184.1

11599.0

1000.0

2000.0

3000.0

4000.0

5000.0

6000.0

7000.0

8000.0

9000.0

10000.0

11000.0

[N]



Range Pair

Ra

ng

e

Pseudo-damage

Take S-N curve

into account for

pseudo-damage

distribution

Axes rotating:

X-axis = Mean

Y-axis = Range

Folding:

No distinction

between

standing

/hanging cycles

Rainflow (range/mean)

3Mean

Ra

ng

e

Rainflow

1To

Fro

m

Rainflow (symmetric)

2To

Fro

m

Only ranges and

# cycles are kept.

Mean value is

ignored.





Road A Road B

DamageDamage




Scatter in loadings

Scenario: 6 measurements

One lap on same road

Same weather conditions

Six different drivers

Range-pair histograms (cycle count)

Six different load histories

Six different range-pair histograms

Six different damage values

Six different fatigue lifes

Damage histograms (cumulative damage)



Agenda

Loads and damage







How to design realistic test schedule ?

Target setting, handling multiple events

Extr

apola

tion

Edit

Desig

n

1%

Worst customer case

Test track

Special test

Load cases Load histories Rainflow matrices Rainflow manipulation




Target setting, handling multiple events

65%

25%

10%

TARGET

1%

Worst customer case

Test track

Special test

Load cases

Supe

rpositio

n




Target setting – Extrapolation for longer duration

Single lap

(measured)

Simply multiplied x6

Six laps

(measured)

Extrapolated

xMultiplying

not sufficient!

Six laps ?

Similar to real

measurement !




Target setting – Extrapolation for longer duration

Original matrix

Extrapolated matrix

Parameterization

• Extrapolation factor (“number of laps”)

• Smoothing factor (“reliability of data”)

-4-2

02

4-4

-2

0

2

4

00.5

11.5

2re

l. H

äufig

keite

n

Compensate for scattering on:

Type of drivers, load data, component dimensions, material properties, ...




From target to test procedure

Track mixture

Target

Special test III

Special test II

Special test I

Test track D

Test track C

Test track B

Test track A

S cj · track j

Equal

damage !

Test schedule

3x 7x

6x 4x



Target Result


From target to test procedure using CombiTrack

Target

Result

Target

Nr. of track

repetitions

Target

damageDamage

per channel



Agenda

Loads and damage








Statistical approach Fleet measurement required

Siemens CuCo: Traditional RLDA + FleetTraditional customer correlation

Extensive

measurement

setup

(50+ channels)

Very limited

sample size

(1 car, few weeks)

Limited

customer

relevance !

Lean CuCo

measurement

setup (8 channels)

Extended

sample size

(Fleet: 5+ cars,

2-6 months)

High

customer

relevance !




Combined CuCo approach, ‘Traditional RLDA & fleet’

• Full RLDA (50-100 ch.)

on public roads

• One vehicle

• Limited time/mileage

Traditional RLDA

to capture

detailed load information

• Observation of

customer behavior

• Many customers/vehicles

• Extended accumulated

observation time/mileage

Fleet measurements

to capture

usage statistics

+

Detailed loading

per usage condition

Statistical frequency

of usage conditions

Usage space

Discretization (cells)

Pro

babili

ty d

ensity

Loading/Strength

Customer

usage

profile

Durability

test



Customer

usage

profile

Durability

test

Pro

babili

ty d

ensity

Loading/Strength


Result: “Fingerprint” of customer representative loading

“Fingerprint” of major vehicle loading components

Braking load (X)

1000

100

10

1

Acceleration load (X)Lateral load (y)

Vertical load (Z)

Low frequency

Vertical load (Z)

High frequency1% Customer EUR

Durability test

1% Customer new market



Agenda

Loads and damage







How can you accelerate a test ?

1 Increase

speed

Increase

amplitude2 Omit non-

damaging events3 Simplify

the test4

Basic principle = conservation of damage




Increase testing speed

1 Increase

speed

Increase



the test4



1st natural

frequency


Increase testing speed

Frequency content should stay reasonably below 1st natural frequency (resonances)

Important: Avoid too high compression !

27,4 s13,7 s




Increase amplitude

1 Increase

speed

Increase



the test4




Increase amplitude

Logarithmic nature of fatigue

Changing slightly the cyclic load applied

to an optimally shaped steel component,

has a big influence on the life time of this

component

Load

Number of

cycles to failure

log

log

Life = (load)-k

1

k

+15% ÷2 !

Important:

Do not increase amplitude too much !

Yield

strength

Load (%) Number of cycles (%)

100 100

115 50

87 200

Be careful not to generate an uncharacteristic

failure mode (plastic vs. elastic).




Omit non-damaging events

1 Increase

speed

Increase



the test4




Omit non-damaging events – Uni-axial

Acceleration of x14 !

100070

All cycles below 4000N only contribute

less than 0.5% of the total damage

If you remove these from the loading, you end

up with 70 cycles instead of 1000




Omit non-damaging events – Multi-axial – RP-filtering

Vertical

Lateral

Longitudinal

Longitudinal

Uni-axial rainflow channel-per-channel is not OK

→ Phase relation lost !

Vertical

Lateral

Different loads !




Omit non-damaging events – Multi-axial – RP-filtering

0 152 4 6 8 10 12 14Zeit [s] 1

-2000

3000

-1500

-1000-500

0500

1000

15002000

2500

FO

RC

E_

lon

g N

1

-3000

2000

-2500

-2000-1500

-1000-500

0

5001000

1500

FO

RC

E_

lat N

2

-2000

12000

-600

8002200

36005000

6400

78009200

10600

FO

RC

E_

ve

rt N

3

Attribute Name Unit

C:\Programme\LMS\TW 3.3 QC\demo\general\nature.dmd,FORCE_long NC:\Programme\LMS\TW 3.3 QC\demo\general\nature_edt.dmd,FORCE_long NC:\Programme\LMS\TW 3.3 QC\demo\general\nature.dmd,FORCE_lat NC:\Programme\LMS\TW 3.3 QC\demo\general\nature_edt.dmd,FORCE_lat NC:\Programme\LMS\TW 3.3 QC\demo\general\nature.dmd,FORCE_vert NC:\Programme\LMS\TW 3.3 QC\demo\general\nature_edt.dmd,FORCE_vert N

Small amplitudes/

little damage

Large amplitudes/

high damage

0.0 27.75.0 10.0 15.0 20.0 25.0Zeit [s] 1

-3510.1

3510.1

-3000.0

-2000.0

-1000.0

0.0

1000.0

2000.0

3000.0

FO

RC

E_lo

ng N

1

-2062.6

2062.6

-1500.0

-1000.0

-500.0

0.0

500.0

1000.0

1500.0

FO

RC

E_la

t N

2

-10509.7

10509.7

-7500.0

-5000.0

-2500.0

0.0

2500.0

5000.0

7500.0

FO

RC

E_vert

N3

Attribute Name Unit

C:\Programme\LMS\TW 3.3 QC\demo\general\nature.dmd,FORCE_long N

C:\Programme\LMS\TW 3.3 QC\demo\general\nature_edt_edt.dmd,FORCE_long NC:\Programme\LMS\TW 3.3 QC\demo\general\nature.dmd,FORCE_lat N

C:\Programme\LMS\TW 3.3 QC\demo\general\nature_edt_edt.dmd,FORCE_lat N

C:\Programme\LMS\TW 3.3 QC\demo\general\nature.dmd,FORCE_v ert NC:\Programme\LMS\TW 3.3 QC\demo\general\nature_edt_edt.dmd,FORCE_v ert N

Phase relation kept !




Simplify the test

1 Increase

speed

Increase



the test4




Simplify the test – Constant amplitude test

Testing with

maximum

amplitude

4x more

damaging !10025

Accelerate by 4x

Increase sine wave

frequency ?28s 7s




Simplify the test – Block cycle test

Mix of different ‘Constant-amplitude’ tests for more representative results

Original time series

Normal/incidental/accidental(automatic or user-defined)

Damage calculation(Standard S-N curve or user-defined)

Rainflow matrix

Block cycle test

Damage equivalent 3 36

12



Agenda

Loads and damage







Accelerated durability tests for PVG and test rig

• Meeting 1.2 million km durability requirement

• Real tests would take 3 years ...

Large-scale customer data collection

• 5000 km Turkish public road data

• Ford Lommel proving ground

Target setting & Test schedule definition

Resulting in

▪ 6-8 week test track schedule

▪ 4 week accelerated rig test scenario

Test acceleration of factor 100

Application case Ford Otosan

Driving 1.2 million kilometers in 8 weeks

Siemens engineers performed dedicated data collection, applied extensive load data processing

techniques and developed a 6- to 8-week test track sequence and 4-week accelerated rig test

scenario that matched the fatigue damage generated by 1.2 million km of road driving.



Application case Ford Otosan

Project steps

Validation

• PVG test

• 4-poster test of the cabin

• 4-poster virtual test

of the cabin

Test schedule

definition

• Data consolidation

• Data analysis

• Target for 1 Mio km

Goal

• 10.000 km PVG

Durability test

Data collection

• 4 months on Turkish roads

• 1 week proving ground

Preparation

Loads definition

• Target vehicle

• Current usage (full & empty)

Route selection

• 16 routes with 140 sections

in total

• = +/- 5.000 km

Instrumentation

• +/- 60 channels (acc., strain, displ.)



Simcenter durability solutions throughout the development process



Thank you!

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