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Erosion Analysis of Fiber Reinforced Epoxy Composites

Parvesh Antil1, Sarbjit Singh2, Sundeep Kumar1, Alakesh Manna2, Catalin Iulian Pruncu3,4 1College of Agricultural Engineering & Technology, CCS HAU, Hisar

email: parveshantil.pec@gmail.com, sundeepantil@yahoo.com 2Punjab Engineering College, Chandigarh, India, email: sarb1234.iitroorkee@gmail.com,

kgpmanna@rediffmail.com 3Mechanical Engineering, School of Engineering, University of Birmingham, B15 2TT, UK,

4Mechanical Engineering, Imperial College London, Exhibition Rd., SW7 2AZ

London, UK, email: c.pruncu@imperial.ac.uk

*Corresponding Author- Catalin Iulian Pruncu, Email: c.pruncu@imperial.ac.uk

Abstract Lightweight and electrically nonconductive fiber reinforced hybrid epoxy

composites have gained attentiveness within specific applications such as marine, automotive

and aerospace. However, the components made from these materials can be subjected to

significant erosion when they are used in shipping industries and water sports equipment. The

present paper proposes to address this challenge by analysing the erosion resistance of glass

fiber and silicon carbide (SiC) reinforced epoxy composites. Taguchi’s methodology was

adopted for the experimentation using the L9 orthogonal array. Impingement angle, erodent

type, and workpiece reinforcement were used as input process parameters, whereas erosion

loss from the composite was perceived as a response parameter. Regression coefficients and

equations for the erosion loss were derived from the regression analysis. The genetic

algorithm (GA) was proposed to obtain authentication against Taguchi’s methodology. The

surface analysis of eroded composites and erodent particles was later evaluated by a Scanning

Electron Microscope (SEM). The comparative outcomes achieved from GA and Taguchi’s

methodology indicates that the angle of impingement and reinforcement size are the primary

aspects that affect the erosion resistance of the composite surface.

Keywords: Epoxy Composites; Erosion Resistance; Genetic Algorithm; Glass Fibers; Taguchi’s

Methodology; SiC Particles;

1. Introduction

Fiber reinforced epoxy composites (FRECs) are being broadly used in marine, automotive

and aerospace industries due to their exceptional properties (i.e. higher strain rate to failure

ratio, nonconductive nature corroborated with toughness features) [1-3]. These properties

make the composites very attractive to replace old components structures with the novel

concept within automobile and aviation industries when used to produce cams, gears and

seals which are made up of these FRECs [4]. There the challenges arise when these

components (specifically in aircraft) are subjected to the continuous slurry and chemical

attack, which can reduce the strength of components made of composites. Such as, the

surface damage occurred in these parts due to the constant attack of the small and dispersed

solid particle in the air, and water flow has emerged as a severe problem [5]. The surgace

degradation in the form of erosion of the component surface results in inefficient repairs and

safekeeping of travellers [1]. The surface erosion from components made of FRECs may

appear as matrix elimination followed by fiber breakage and detachment of reinforcement [7-

9]. This erosion behavior is generally influenced by the parameters like angle of slurry

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attack, the nature of abrasive present in medium and impact velocity [10]. In the recent past,

various research studies were engrossed on the erosion analysis of fiber reinforced

polymer/epoxy composites. Reija et al. [11] tested glass fiber reinforced vinyl ester matrix

composite for the erosion behavior under aqueous and acidic environment and found that

composites surface degraded at a faster rate in an aqueous environment using quartz

abrasives as compared with an acidic environment. Bagci et al. [12] analysed the erosion

phenomenon of glass fiber reinforced polymer composite filled with boric acid and concluded

that additional filler in the form of boric acid had reduced the erosion resistance of the

composites. Tiwari et al. [13] investigated the effect of impingement angle and fiber

orientation on erosion behavior of carbon fiber and glass fibre-epoxy composites. Yang et al.

[14] investigated the influence of erosion damage on the fatigue and strength of the E glass-

epoxy composites. Apart from it, various numerical and optimization techniques were

proposed to address the challenge of erosion behavior of composites materials. Ant colony

optimization [15], adaptive neuro-fuzzy system [16], Finite Element Method [17], genetic

algorithm [18] etc. have been used by the researchers to optimize the results for various

processes to improve the erosion resistance. Gupta et al. [19] used artificial neural networks

to analyze the erosion behavior of plasma sprayed coating of glass microspheres. Thakre et

al. [20] used response surface methodology for the prediction of erosion pattern in

polyetherimide composites. Bagci et al. [21] used Taguchi’s method to optimize the process

parameters during the erosion of glass fiber reinforced epoxy composites. Mahapatra et al.

[22] used a genetic algorithm to analyze the erosion behavior of glass fiber reinforced

polyester composites. The literature findings reveal that the reinforcement, impingement

angle and nature of erodent plays a crucial role in influencing erosion of epoxy composites.

Despite, the research mentioned above that focus on understanding the composite behavior

when submitted to erosion activity, no study was devoted to actually evaluate the erosion

behaviour under natural abrasives like natural sand and saline water. Therefore, we present in

this study the effect of sand particles on the erosion behaviour of fiber reinforced epoxy

composites. Corroborating the experimental simulation and optimization algorithms were

possible to detect the optimum processes parameter, which allows improving the resistance of

FRECs to erosion mechanism.

2. EXPERIMENTAL DESIGN AND PLANNING

In the present research, the three main process parameters viz. impingement angle,

reinforcement and erodent were considered, as shown in Table 1. The experimentation was

planned as per Taguchi’s methodology [23] based L9 orthogonal array. The effect of process

parameters on erosion behavior of FRECs was simulated initially using analysis of variance

(ANOVA) feature on Minitab 18 software. The fraction involvement of each process

parameter on erosion behavior of FRECs was obtained by ANOVA. The regression

coefficient was obtained after modelling the process using regression analysis. The genetic

algorithm in MATLAB 2017 software was further used for confirmation of results proving a

confidence interval of 95%.

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Table 1 Process Parameters and Levels

Process Parameters Designation Level 1 Level 2 Level3

Impingement Angle

(˚)

I 30 60 90

Reinforcement

(mesh)

R 400 320 220

Erodent E Beach

Sand

Desert

Sand

River Sand

2.1 Material and Experimental Setup

The materials used for the erosion analysis are fiber reinforced epoxy composites (FRECs),

which contain two types of glass fibers and SiC as reinforcement. The glass fibers were

unsystematically oriented E glass 450 GSM sliced thread mat fiber and S glass 400 GSM

interlaced rowing mat fiber. The silicon carbide particles (SiCp) were of three dissimilar

sizes, i.e. 220, 320, and 400 grit with a concentration of 10 % by weight for each type. The

glass fibers are the primary reinforcement in the composite whereas SiC particles are

secondary reinforcement. The blend of Araldite epoxy resin and hardener in the proportion of

10:8 was used as a matrix. The SiC particles were mixed in the matrix (blend of epoxy and

hardener) with respect of weight percentage of matrix whereas the layers of glass fibers were

cut from the fiber rolls in a size of 180 × 240 mm. Three layers of E glass fibers having

weight 10.50 grams /layer and two layers of S glass rowing fibers having 14 grams/layer

were employed with 70 grams of matrix on each layer. The blended matrix cures at

temperatures from 68ºF (20ºC) to 356ºF (180ºC) without any discharge of impulsive

elements. The composites were fabricated by using hand layup method [1]. The erosion

analysis was performed under the erosion test setup, which was designed as per ASTM G76

standard using an air nozzle of 3.5 mm diameter. The schematic diagram of the developed

setup is shown in Figure 1. During experimentation, the workpieces were kept at impact

angles (β) varied from 30˚ to 90˚. The size of workpieces, i.e. 10x10x5 mm, was reserved

unceasing for whole investigation. The scanning electron microscope (SEM) images of

natural erodent (i.e. river sand, beach sand and desert sand) used for experimentation is

shown in Figure 2. The SEM micrographs show that river sand (Figure 2 c) particles possess

much sharp and conical edge as compared with the desert and beach sand (Figure 2 (a) and

(b)).

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Figure. 1 Schematic Diagram of Erosion Test Setup [26]

The high-pressure abrasive slurry composed of natural sand particles streams out over flow

regulator and hits the upper layer of workpiece through nozzle tip for 240 seconds. This

phenomenon was repeated for every test. The surface degradation was calculated in terms of

material loss from the composite surface per unit weight of imposed slurry. For every test

piece, three experiments were conducted with same dimensions to gather the mediocre

erosion rate. The variance in weight of test pieces before and after investigation was labeled

as weight loss. The test specimen with greater weight loss will have the least resistance to

erosion. The different test settings employed for erosion test are presented in Table 2.

Table 2 Test Conditions for Erosion Testing

Test factors SiC/Glass fibers + Epoxy

Erodent Beach Sand; Desert sand;

River Sand

Nature Angular

Impingement angle 30˚; 60˚; 90˚

Standoff distance 10 mm

Nozzle diameter 3.5 mm

Temperature Room temperature

Silicon carbide (SiC) 10 wt%

SiC Particle Size

(mesh)

400, 320, 220

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a) b)

c)

Figure 2. Micrographs acquired by SEM investigation for different type of sands used for

experimentation a) beach sand, b) desert sand and c) river sand

3. RESULT AND DISCUSSIONS

3.1 Taguchi’s Methodology

The results obtained in terms of erosion loss during experimentation were tabulated in Table

3, and the analysis of variance (ANOVA) for the erosion loss were shown in Table 4. The p-

value for impingement angle and reinforcement falls under 0.05, which specifies that these

process parameters are significant parameters in affecting erosion resistance of the

composites. The contribution of these two process parameters is 63.03% and 28.18%,

respectively, as relevance for affecting the erosion behavior. The p-value of erodent

parameter is 0.087, which is higher than 0.05. This implies that this parameter has not much

significant effect on erosion behavior, but it has a 10.33% contribution, which cannot be

neglected. The R square value for the erosion analysis comes out to be 99.01%. Figure 3

shows plots for raw data and S/N ratio (smaller the better) plot for Erosion analysis. As per

raw data, the erosion loss increases within the increase on the angle of impingement from 30˚

to 60˚, but with an increase in the angle of impingement from 60 to 90˚, the erosion resistance

property of composite improves. The erosion resistance improves because, escalation in

impact angle diminishes cutting wear as, by rise in vertical component, the material surface

converts in furrier and results in reduced erosion proportion. The plotted results revealed that

the composite surface eroded quickly when slurry strikes the workpiece at 60˚ and exposes

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the fibers. The exposed fibers result in higher cutting wear over fiber surface. The observed

trend of highest erosion at 60˚ is similar to the trend reported for erosion behavior in semi-

ductile materials [24]. The observed trends for reinforcement show that increase in mesh size,

i.e. decrease in micron size of the reinforced SiC particle improves the resistance against

erosion. It is because erosion resistance of the composite is dependent on the interfacial

interaction between matrix and reinforcement. Stronger interfacial interaction between the

matrix and reinforcement leads to better stress transfer, but this interfacial interaction

decreases with decrease in mesh size of particles [25-26]. The obtained results for the erodent

parameter demonstrates that the erosion resistance property of the polymer matrix composites

depends upon the surface characteristics of the erodent particles. Finer is the erodent surface;

better will be the erosion resistance property. The desert and beach sand particles have a

smooth and nearly rounded shape and offers less abrading action over the composite surface,

whereas river sand particles possess a conical and sharp edge.

Table 3 Experimental Design (L9) Orthogonal Array

Exp. No. Impingement angle (˚) Reinforcement

(mesh)

Erodent Erosion loss

(mg)

S/n ratio

1 30 SiC 400 Beach Sand 2.9 -9.2480

2 30 SiC 320 Desert Sand 3.3 -10.3703

3 30 SiC 220 River Sand 3.6 -11.1261

4 60 SiC 400 Desert Sand 3.6 -11.1261

5 60 SiC 320 River Sand 4.4 -12.8691

6 60 SiC 220 Beach Sand 4.1 -12.2557

7 90 SiC 400 River Sand 3.7 -11.3640

8 90 SiC 320 Beach Sand 3.8 -11.5957

9 90 SiC 220 Desert Sand 4.0 -12.0412

Table 4 Analysis of Variance for Erosion Loss

Source DF SS MS F Value P Value P (%)

Impingement Angle 2 0.98889 0.47444 61.00 0.016 63.03

Reinforcement 2 0.44222 0.22111 28.43 0.034 28.18

Erodent 2 0.16222 0.08111 10.43 0.087 10.33

Error 2 0.01556 0.00778 0.99

Total 8 1.56889 R2 = 99.01%; R2 (Adj.) = 96.03%

DF = Degree of Freedom; SS= Sum of Squares; MS= Mean Squares; P(%) = % Contribution

a) b)

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Figure 3. a) Raw data for erosion loss and b) S/N ratio for erosion loss

Figure 4 (a-c) shows SEM micrographs images of erosion caused by river sand on composite

surface reinforced with 400, 320 and 220 mesh SiC, respectively. The SEM image (Figure 4

(a)) shows that the abrasive slurry has uniformly eroded the surface. The strong interfacial

bonding between matrix and fibers has improved the erosion resistance strength of the

composite. The surface analysis in Figure 4 (b) reveals that the composite underwent several

stages of erosion and material removal from a composite surface. From the SEM

observations, it is clear that crucial reason for material removed from the surface of the

composite is micro cutting and ploughing. The SEM observations for Figure 4 (c) indicates

that continuous erodent slurry impact causes damage to the fiber matrix interface. The image

specifies that composite undergoes plastic deformation and results in the development of

deep crater due to constant erodent impact.

Figure 4. SEM micrographs of erosion in composite reinforced with (a) 400 mesh SiC (b) 320 mesh

SiC (c) 220 Mesh SiC

3.2 Regression Analysis

The regression analysis is generally used by the researchers in two forms, i.e. linear

regression and nonlinear regression to analyze the relationship between process parameters

and response parameter. Precisely, it is used when the output or response parameter is

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affected by more than one process parameters [27]. For the present research paper, the linear

regression for erosion is shown in equation (1).

Erosion = = 0.8667 + 2.017 × I + 0.9333 × R - 0.4833 × E - 0.4833 × I×I - 0.2000 × R×R +

0.1500 × E×E + 0.06667 × I×R + 0.0333 × I×E ….. (1)

4. GENETIC ALGORITHM

Genetic algorithm (GA) is an approach used to discover an ideal solution of an objective

function which was initially implemented by Holland [28] in the early seventies. This

approach was based on the programming and assessed the solution to any problem by the

natural evolutionary process. This approach was modified by De Jong [29] by converting it

from a program based to function based. GA is a mathematical model which is used to

provide a typical result when the output is influenced by several process parameters. GA

manages several entities in every iteration for any problem. The offsprings are created by

merging of genetics from those entities which are failed to imitate. It helps in providing better

solutions to persist. The obtained solution is equated with fitness function, and iteration helps

in providing a better solution. These algorithms are used to get prominent solutions to

optimize the results by depending on bio-inspired operators such as mutation, crossover, and

selection [30]. The upper bound and lower bounds for the process parameters are defined

before starting the optimization process. The developed regression equation is then used to

find out the best parameter to improve erosion resistance. Within bound conditions, the

optimum solution for the output quality characteristics (OQC) was calculated. During the

optimization process, the mutation was observed as constraint dependent, cross over function

was scattered and cross over ratio was 0.8 to find the most significant parameter to improve

erosion resistance. The obtained results from the genetic algorithm (Figure 5) shows that the

impingement angle, i.e. the angle at which the erodent strike the workpiece surface is the

most significant parameter. In actual terms, this parameters is somehow uncontrollable

because during structure used in aviation and shipping cannot avoid collision with abrasive

particle present in the air and liquid medium. In that case, the second most significant

parameter comes out to be reinforcement provided during the fabrication process. Finer is the

reinforcement lesser will be the inter-particle distance and higher will be fiber reinforcement

bond strength [3]. The improved bonding strength will strengthen the erosion resistance

property of the composite material. The obtained result from the genetic algorithm and

Taguchi’s methodology shown good agreement in predicting the most significant or

influential process parameter for improvement in resistance against erosion.

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Figure 5. Most influencing Process Parameter

5. CONCLUSIONS

In the present paper, experimental simulation and optimization algorithms were used to

perceive the erosion behavior of fiber reinforced epoxy composites (FRECs). The results

attained from this study permits to indicate the best process parameter, which can help to

improve the resistance to erosion of FRECs. The following main conclusions were drawn:

• The fiber reinforced epoxy composites (FRECs) are successfully fabricated and tested

accordingly to ASTM G76 standard, which permits to evaluate the erosion behavior.

• The experiments were designed as per Taguchi’s methodology, and the results were

analyzed qualitatively and then verified by genetic algorithms. The observed results

for the erosion behavior show that the reinforcement size is a dominant factor that

affects the erosion resistance of the composite surface.

• The p-value for impingement angle and reinforcement falls under 0.05, which

specifies that these process parameters are significant parameters for detecting the

erosion resistance of the composites. The contribution of these two process

parameters is found to be 63.03% and 28.18%, respectively, in affecting the erosion

behavior.

• The obtained result from the genetic algorithm and Taguchi’s methodology showed

good agreement in predicting the most important process parameter for improvement

in resistance against erosion.

• The SEM micrographs morphology showed the erosion pattern and behavior of

composite surface under constant slurry attack.

Acknowledgement The author(s) would like to thank Mechanical Engineering Department,

Punjab Engineering College, Chandigarh, India and Sophistical Analytical Instrumentation

Facility, Panjab University, Chandigarh, India for providing their valuable lab and technical

support in conducting the experiments for the research work.

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