SN CURVE APPROACH FOR

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SN CURVE APPROACH FOR
VALIDATION OF THE MASTER
SN CURVE APPROACH FOR
SHORT FIBER REINFORCED
COMPOSITES
Atul Jain, Yasmine Abdin, Stefan Straesser,
Wim Van Paepegem, Ignace Verpoest, Stepan Lomov,
[email protected]
COMPTEST 2015, Madrid
Contents
Motivation
Master SN-curve approach (MSNC)
Experimental validation
MSNC vs. prevalent approaches
Conclusions
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Short fiber composites
 Usually injection molded with glass fiber reinforced
thermoplastic
 Different orientations, length distribution at every point
Different static and fatigue
properties at every point!!
σ
S
ԑ
N
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Short fiber composite and industry
Industrial application of such composites is
increasing, esp. in automotive- fatigue is a major
issue for this application!!
Engine mounts
Spare wheel recess
Engine cover cam
Efficient fatigue simulation can help fully exploit
weight reduction potential
Goal is to reduce extra testing by proposing hybrid
multiscale fatigue methodologies
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Image sources: www.reinforcedplastics.com
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Multi-scale analysis of short fiber
composites
Micro-macro analysis: Every point in the Finite
Element model treated as an RVE
Each RVE is characterized by an orientation distribution
of inclusions and a length distribution of inclusion
Static and fatigue properties must be calculated
efficiently at every point
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Multi-scale analysis of short fiber
composites
Detail of orientation at every
point in the component
Manufacturing simulation
Mapping of meshing
Static properties: MoriTanaka formulation
FE solver
Fatigue properties: ????
Fatigue solver
First choice scheme for static properties is full
Mori-Tanaka formulation
No analytic counter part for fatigue properties
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Master SN-curve approach
More than one damage event takes place at stress to
failure
For a certain number of cycles, damage needs to be
“big enough” or “spread enough” to propagate and
cause failure
“How much damage is enough damage to cause cyclic
failure?”
Damage modeling and quantification of damage is
needed
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First cycle- micromechanics
Fiber matrix debonding, matrix non-linearity and
fiber breakage is modeled
Fiber matrix
debonding
Fiber breakage
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Master SN curve approach
 Each data point in SN-curve gives two values
o number of cycles to failure (abscissa)
o the stress to failure for the number of cycles (ordinate)
Value of damage parameter is calculated for the ordinate
 Same value of damage parameter is assumed for different
SN-curves having the same abscissa
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Algorithm
Read reference SN data – stress level
0and corresponding number of cycles
SN-ref
Calculate Initial Young’s modulus E0 – MT
formulation
σ
Calculate the modulus and damage
parameter at stress level, S1 read from SN
curve
Dfirstcycle = 1-E1/E0
For second RVE: loading increments is
modelled and the modulus and damage
parameter is calculated
S1
E0
N
ԑ
S2
E02
Stress at which damage parameter
Dfirstcycle is reached, S2 is the stress to
failure for initially chosen cycles
Dfirstcycle = 1- E1RVE / E0RVE
E12
ԑ
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Experiments
50% GF reinforced PBT is
injection molded into plates
Coupons are milled in 3
directions w.r.t. to the matrix
flow
Fatigue and static tests are
performed
Frequency = 10Hz
R-ratio = 0.1
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Results of experiments
Clear dependence of fatigue properties on orientations
is observed
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ANOVA analysis confirms with 90% confidence
that the loss of stiffness curves are independent
of the FOD
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MSNC Simulation
Good predictions for the SN curve are seen
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MSNC simulation
Good match is observed for the SN curves
independent of the input SN curve!!
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Collected data-1
Data set #1 De Monte et. al.
35% weight fraction GF reinforced
polyamide (PA)
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Collected data-2
Data set #2 Klimkeit et. al.
30% weight fraction GF reinforced PBT
Apart from the SN curve the FOD
is also provided in both the papers
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Results ~ De Monte et al.
Similar good results are obtained also for
the published data
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Results ~ Klimkeit et al.
For the three sets of data considered:
 The MSNC approach is seen to be independent of the input SN
curve
 Errors increase when lower number of cycles are taken
as input
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MSNC approach and FOD of reference
SN curve
Error of the proposed scheme is defined as:
Group
0-degree
No. of
samples
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Mean
±
deviation
-0.02 ± 0.054
Standard Mean of abs
(error)
0.043
45-degree 21
0.06 ± 0.061
0.069
90-degree 21
-0.03 ±0.056
0.049
The magnitude of error in the proposed scheme is
independent of the FOD of the reference SN curve
This is confirmed further by students T-test with a
confidence of 95%
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MSNC approach vs. UTS based scaling
 A prevalent practice is to scale the SN curves based on one
input SN curve and UTS of the reference and the target RVE
 This approach would require one SN curve as input and
knowledge of UTS at every point
MSNC approach is seen to be more
accurate than the UTS based scaling!
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MSNC approach vs. test based
interpolation
This approach requires many SN curves as input
MSNC approach is seen to be more accurate
than the test based interpolation!
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Scaling vs. test based interpolation
 1 SN-curve as input is
Test based interpolation
necessary

 No specific requirement on
orientation of coupon

 Possible to predict SN-curves
with different volume
fractions, length distribution

etc.
Volume-fraction
2, 3 or even more curves are
needed as input
Coupons need to be orientated in
specific directions- testing becomes
expensive!!
Orientation only parameter on which
interpolation could be done
Orientation
Length distribution
State of art methods allow only
scaling across “orientation axis”
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Conclusions
 A hybrid method of scaling which involves both experimental
and micromechanical analysis was presented
 The MSNC approach is independent of the FOD of the
reference SN curve
 Only 1 SN curve is needed for such an analysis
 This could lead to a breakthrough in industrial deployment
of composite materials as collection of fatigue data is a
bottleneck for simulation
 The presented methodology has been implemented in
industrial software LMS Virtual.Lab Durability part of Siemens
Industry Software with partnership with PART Engineering
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Future Work
 Process integration and component level simulation
is performed
 First results are promising!! Critical areas are
correctly identified
 Life time to failure is off by a factor of about 5-8
 Multi-axial fatigue and mean stress corrections are
studied
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Thank you!
The authors wish to thank the IWT Vlaanderen for funding
this research as a part of the project “Fatigue life prediction of
random fiber composites using hybrid multi-scale modeling
methods” - COMPFAT Baekeland mandate number 100689
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