ME 8883/CSE 8803: Materials Informatics

8th December 2014

Developing Structure-Property Linkage for Glass

Fibre Reinforced Polymer Composites

Presented by

Geet Lahoti, 2nd Year ISyE PhD Student

Alicia White, 2nd Year MSE PhD Student

Guided by

Prof. Surya Kalidindi

Dr. Tony Fast

Background

• Combine properties of the two materials

• Composites are used in many industries

Figure 1: Components of a BMW sedan fabricated with

lignocellulosic fiber reinforced polymer (FRP)

composites [1] Figure 2: FRP materials in passenger

aircraft [2]Figure 3: Indian glass fibre composites

market (2006) [3]

12/8/2014 2

Background

• Forming processes create varied

and complex microstructures

• Microstructure varies even within a

simple part such as this plate

• Understanding the complexity of

these microstructures is an open

field which can give insight to the

properties of these materials

Figure 4: Variety in microstructure across an injected part

12/8/2014 3

Motivation

• The structure and organization of the

reinforcement greatly affect the final

properties of the part

• Conventional approaches to property

determination do not take into account

the microstructure of the reinforcement

• Voigt model [4]

• Ruess Model [5]

• Those that do are based on a assumed

configurations of the fibres, not the

actual microstructure [6]Figure 5: Complex microstructure of FRPC.

12/8/2014 4

Project Outline

• Objective: Develop Structure-Property Linkage for GFRPs

Manufacture

GFRP Samples

Is the no.

of

samples

enough?

Segmentation

Spatial

Correlation

Dimensionality

Reduction

Microstructure

Simulation

Perform

Micro-computed

tomography

(micro-CT)

Physical Property

from Finite

Element Analysis

Physical Property

from

Experimental

Testing

Relationship

Modelling

Yes

No

12/8/2014 5

Project Execution

Step 1: Samples and Micro-CT Data

• Fibre: Glass

• Polymer: Polypropylene

• Processing: hot melt impregnation and extrusion/compression molding

• Micro-CT Images: DICOM Format

• No. of Samples: 2

• Dimensions of each sample: 1300 X 1300 X 900 voxels

• Dimensions under consideration: 300 X 300 X 300 voxels

12/8/2014 6

Project Execution

Step 2: Segmentation

• Need to separate the fiber from the matrix to get an

accurate representation of microstructure

• Apply peak fitting algorithm to histogram of pixel values

• Segmentation based on Gaussian Likelihood Maximization

• Gaussian Function

f(x) = 1

𝑎𝑒

−(𝑥−𝑏)2

2𝑐2

where, a=height, b=center, c=width

• Multi Otsu’s Method

Original Microstructure

Segmented Microstructure

12/8/2014 7

Project Execution

Step 3: Microstructure Simulation

Fibers Elongated along Y Axis Fibers Elongated along Diagonal Fibers Elongated along X Axis

12/8/2014 8

Dimensions: 21 X 21 X 21

Project Execution

Step 4: Physical Property Simulation• Finite Element was performed under uniaxial strain conditions

• Property under consideration is going to be C11.

• Stress and strain were calculated and used to find the components of the

stiffness tensor corresponding to Ɛ1

StrainStress

12/8/2014 9

Project Execution

Step 5: 2-Point Statistics• 2-Point Statistics: Probability density associated with finding local states h and

h’ at the tail and head, respectively, of a prescribed vector r randomly placed

into the microstructure[7]

12/8/2014 10

Project Execution

Step 6: Dimensionality Reduction• 2-Point Statistics: Extremely large set

• Low dimensional representation

• Principal Components Analysis [7]

• Linear transformation of high-dimensional data to a new orthogonal frame

12/8/2014 11

Project Execution

Step 6: Dimensionality Reduction• Please add description about 2-Point Stats

PCs for Sample 1: 0.0104 0.0000 0.0000 -0.0003 -0.0001

PCs for Sample 2: 0.0104 0.0000 0.0001 0.0002 0.0002

12/8/2014 12

Project Execution

Step 7: Structure-Property Linkage• Regression

Property = 𝟑𝟑. 𝟔 +0.88 PC1 + 15.76 PC2 + 3.83 PC3 - 5.83 PC4 + 17.22 PC5

Property Predicted by model

for sample 1 : 33.6129

and

for sample 2: 33.6098

Rsquare: 0.9638

CV Mean Absolute Error: 0.14237

Property Predicted by FEM simulations

for sample 1: 4.99

and

for sample 2: 6.48

12/8/2014 13

Conclusion & Future Work

• Investigated digital representations of sample microstructures

• Developed a S-P linkage based on simulated dataset

• Obtain more real samples

• Validating linkages with the segmented real microstructures

• Carry out physical experimental testing of samples

• Simulate a rich set of microstructures

• Other Studies using the same protocol: Consider other composites like

Carbon Fibre Reinforced Polymers

12/8/2014 14

References1. http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1517-70762010000200006

2. http://www.reinforcedplastics.com/view/4437/india-on-the-up/

3. http://essaywritingserviceuk.co.uk/advice-and-guidance/free-essays/the-potential-of-frp-

materials-in-a-passenger-aircraft-structure/

4. W. Voigt, ”On the relation between the elasticity constants of isotropic bodies," Ann

Phys Chem 274 (1889): 573-587.

5. A. Reuss and Z. Angrew, ”A calculation of bulk modulus of polycrystaliine materials."

ZAMM- Journal of Apllied Mathmatics and Mechanics, Vol. 9, No. 1, 1929, pp.49-58

6. http://onlinelibrary.wiley.com/doi/10.1002/pc.20002/pdf

7. Surya R. Kalidindi, “Data Science and Cyberinfrastructure: Critical Enablers for

Accelerated Development of Hierarchical Materials

12/8/2014 15

Thank You!

Questions?

12/8/2014 16