Engineering, Environment

Abrasive wear behaviour of Al-Cu-Mg/palm kernel shell ash particulate composite

Gambo Anthony VICTOR

Department of Mechanical Engineering, Ahmadu Bello University, Zaria, Nigeria

E-mail: gambo.anthony@yahoo.com;

Corresponding author, phone: +2348038467245

Received: June 20, 2017 / Accepted: December 02, 2017/ Published: December 30, 2017

Abstract

This paper presents a systematic approach to develop a wear model of Al-Cu-Mg/Palm kernel shell ash particulate composites (PKSAp) produced by double stir-casting method. Four factors, five levels, central composite, rotatable design matrix was used to optimize the number of experiments. The factors considered were sliding velocity, sliding distance, normal load and mass fraction of PKSA reinforcement in the matrix. Response surface methodology (RSM) was employed to develop the mathematical model. The developed regression model was validated by statistical software MINITAB and statistical tool such as analysis of variance (ANOVA). It was found that the developed regression model could be effectively used to predict the wear rate at 95% confidence level. The regression model indicated that the wear rate of cast Al-Cu-Mg/PKSAp composite decreased with an increase in the mass fraction of PKSA and increased with an increase of the sliding velocity, sliding distance and normal load acting on the composite specimen.

Keywords

Al-Cu-Mg alloy; Analysis of variance; Wear rate; Response Surface Methodology; Palm kernel shell ash particles

Introduction

Industrial technology is growing at a very rapid rate and consequently there is an increasing demand and need for new materials [1]. To meet such demands, particulate reinforced composites constitute a large portion of these new advanced materials. The choice of the processing method depends on the property requirements, cost factor consideration and future applications prospects [2].

Incorporation of hard second phase particles in the alloy matrices to produce MMCs has also been reported to be more beneficial and economical [1, 3] due to its high specific strength and corrosion resistance properties. In the past, various studies have been carried out on metal matrix composites. SiC, TiC, TaC, WC, B4C [4] are the most commonly used particulates to reinforce in the metal or in the alloy matrix or in the matrices like aluminium or iron.

Recently, there has been an increasing interest in composites containing low density and low cost reinforcements [5, 6]. The availability of natural fibers has tempted researchers to try locally available fibers and to what extent they satisfy the required specifications as good reinforcement for tribological applications. Natural fibers such as banana, cotton, coir, sisal and jute have attracted the attention of scientists and technologists for application in consumer goods, low cost housing and other civil structures. It has been found that these natural fiber composites possess better electrical resistance, good thermal and acoustic insulating properties and higher resistance to fracture. Natural fibers have many advantages compared to synthetic fibers, for example low weight, low density; low cost, acceptable specific properties and they are recyclable and biodegradable. They are also renewable and have relatively high strength and stiffness and cause no skin irritations [7]. However, there are also some disadvantages, for example moisture uptake, quality variations and low thermal stability. Many investigations have been made on the potential of the natural fibers as reinforcements for composites and in several cases the result have shown that the natural fiber composites own good stiffness, but the composites do not reach the same level of strength as the glass fiber composite [8].

Among various discontinuous dispersions used palm kernel shell ash (PKSA) has been found to be one of the most inexpensive and low density reinforcement available in large quantities as solid waste from coconut processing industries [9]. Hence, composites with palm kernel shell ash as reinforcement are likely to overcome the cost barrier for wide spread applications in automotive and small engine applications. It is therefore expected that the incorporation of palm kernel shell ash particles (PKSAp) in aluminium alloy will promote yet another use of this low-cost waste by-product and, at the same time, have the potential for conserving energy-intensive aluminium and thereby, reducing the cost of aluminium products [10].

There have been few dry sliding wear behaviour studies based on various reinforcements like SiC, Al₂O₃, fly ash and Zircon [11]. The principle tribological parameters that control (load, sliding velocity, sliding distance, counterpart material, weight % of reinforcement, shape, and size) specific wear rate and coefficient of friction were analysed.  From the literature, it is understood that the relationship between the parameters in dry sliding wear is complex and independent, selection of the optimal parameter of combination is important to reduce specific wear rate and coefficient of friction. Design of experiment, Genetic algorithm and response surface method is widely used to optimize the dry sliding parameters. There has been experimental investigation using Taguchi and ANOVA to identify the significant factors while testing with Al 2219 SiC and Al 2219 SiC graphite material shows that the sliding distance, sliding velocity and load are having significant effect [11]. From these discussions it is clear that though lot of work has been done on MMCs, as per the information of the author, no work has been done on the use of Response Surface Methodology (RSM) technique to predict the tribological performance of Al-Cu-Mg/ PKSAp composite. Therefore, this work aims at adopting RSM technique to obtain an empirical model of wear loss (response) as a function of amount of reinforcement, applied load, sliding velocity and sliding distance (input factors).  

Materials and methods

Preparation of palm kernel shell ash

Palm kernel shell was crushed and grounded to form palm kernel shell powder; the powder was packed in a graphite crucible and fired in electric resistance furnace to a temperature of 1300⁰C for 1 hour to form palm kernel shell ash (PKSA).

Particle size analysis of the palm kernel shell ash particles was carried out in accordance with BS1377:1990 [4]. About 150g of the ash particles were placed into a set of sieves arranged in descending order of fineness and shaken for 15 minutes which is the recommended time to achieve complete classification, the particles that were retained in the BS 75µm was used in this study.

Chemical composition of the palm kernel shell ash particles is presented in Table 1.

Table 1. Chemical composition of palm kernel shell ash

Element	Al2O3	SiO2	CaO	Fe2O3	MgO	K2O	Na2O	LOI
% by wt	13.17	65.56	6.56	4.79	1.24	5.56	1.20	0.90

Fabrication of composites

A high purity aluminium electrical wire obtained from Northern Cable Company (NOCACO) Kaduna, Nigeria, was used as the matrix. Synthesis of the metal matrix composite was by double stir-casting method at the foundry shop of the National Metallurgical Development Centre (NMDC), Jos, Nigeria. The specimens were produced by keeping the percentage of copper and magnesium constant and different volume fraction of PKSAp 5, 10, 15 and 20% with particles size of 75µm were added in the mix. All the melting was carried out in a clay-graphite crucible in a resistance furnace. Al-4%Cu-0.8%Mg alloy was preheated at 450⁰C for 2 hours before melting, and before mixing the palm kernel shell ash particles, it was preheated at 1000⁰C for 1 hour to make their surfaces oxidized[12]. The furnace temperature was first raised above the liquids temperature of aluminium (720⁰C) to melt the alloy completely and then cooled down just below the liquids to keep the slurry in a semisolid state. At this stage, the preheated palm kernel shell ash particles was added and mixed manually. Manual mixing was used because it was very difficult to mix using automatic device when the alloy was in a semi-solid state.  

After sufficient manual mixing, the composite slurry was re-heated to a fully liquid state and then automatic mechanical mixing was carried out for about 20 minutes at an average stirring rate of 150 rpm. In the final mixing processes, the furnace temperature was controlled between 730 and 740⁰C and 0.01%NaCl-KCl was added as a covering flux. The pouring temperature was controlled to be about 720⁰C [13].                 

Specimen of 6mm × 6mm ×50mm were cut from the cast composite, the end of the specimens were polished with abrasive paper of grades 600 followed by grade 1000. Dry sliding tests were carried out as per ASTM G99-95 test standards on pin-on-disc equipment [14], the disc of which is of EN31 steel with surface roughness, Ra 0.1µm. The sample pins were cleaned with acetone and weighed before and after testing using an electronic balance to an accuracy of 0.0001g to determine the amount of wear. The sliding end of the pin and disc surfaces were cleaned with acetone before testing. The specific wear rate (Ws) was calculated using the following Eq. (1) [11]:

Ws =                                                                                                        (1)

Where: Ws is the specific wear rate,  is the mass loss,  is the density,  is the normal load, and  is the sliding distance. After the test, the sliding surface of selected test samples was observed by scanning electron microscopy (SEM) as shown in figure 1.

Figure 1. Pin on disc equipment [11]

Identifying the important process parameter

Based on preliminary trials, the independent process parameters affecting the tribological behaviour of a composite were identified as: sliding velocity A(m/s), sliding distance B(m), applied load C(N),  and  fraction of reinforcement D (wt%).

Finding the limits of control variable

Many trial experiments were conducted on the aluminium matrix composite (AMC) specimens to find out the feasible limits of the process in such a way that the lower limit of each parameter was fixed to yield a noticeable wear. The upper limit was selected when wear was not severe. In order to have an easy interpretation of results and to understand the effect of each parameter on the response, the lower and upper levels of the parameters are coded as -2 and +2 respectively. The coded values for any intermediate range were calculated using the following relationship, Eq. (2):

 = 2[2X – ( + )]/ (-)                                                 (2)

Where: X_i is the required coded value of a variable, X is any value of the variable from X_max to X_min; X_max is the upper limit of the variable; X_min is the lower limit of the variable.

Table 2 below shows the factors and their level employed in the experiments. Wear in terms of specific wear rate is the measured response used to evaluate the tribological behaviour.

Table 2. Factors and their levels in central composite design experimental plan

Factors	Level
	-2	-1	0	1	2
Sliding velocity A (m/s)	0.4	0.8	1.2	1.6	2.0
Sliding distance  B(m)	400	800	1200	1600	2000
Applied load C (N)	10	20	30	40	50
Reinforcements D (wt %)	0	5	10	15	20

Design of experiment (DOE)

In the present investigation, experiments were designed on the basis of the Design of Experiments (DOE) technique proposed by Box and Hunter [16, 17]. A 2^k factorial, where k is the number of variables, with central composite second-order rotatable design was used to improve the reliability of results and to reduce the size of experimentation without loss of accuracy.

The four-factor, five level central composite rotatable design requires 31 experiments with 16 factorial points. The next 8 experimental runs comprised a combination of each process variable at either their lowest (-2) or highest (+2) level with the other three variables kept at the intermediate levels (0) constituting the stars point and seven centre points for replication to estimate the experimental error. The experiment has been carried out according to the run order in the experiment design matrix. At the end of each run, settings for all four parameters were changed and reset for the next run. This was essential to introduce variability caused by errors in experimental settings [17].

Development of wear model

The wear loss (W) of the Al-Cu-Mg/PKSAp composite is a function of sliding velocity, sliding distance, normal load and mass fraction of PKSAp reinforcement in the aluminium alloy matrix. It can be expressed as, Eq. (3):

W = f (A, B, C, D)                                                                                           (3)

Where: W – Response; A - Sliding velocity; B - Sliding distance; C - Normal load; D - Fraction of CSAp reinforcement.

For the four-factors, the selected polynomial (regression) could be expressed as Eq. (4):

W = b₀ + b₁A + b₂B + b₃C + b₄D + b₁₁A² + b₂₂B² + b₃₃C² + b₄₄D² + b₁₂AB + b₁₃AC + b₁₄AD + b₂₃BC + b₂₄BD + b₃₄CD                                                                             (4)

Where: b₀ is the free term of the regression equation, the coefficients b₁, b₂, and b₃ are linear terms, the coefficients b₁₁, b₂₂, and b₃₃, are quadratic terms, and the coefficients, b₁₂, b₁₃, and b₂₃, are interaction terms. The values of the coefficients were calculated with the help of MINITAB a statistical analysis software, which is widely used in many fields of engineering research. The values of the coefficients for the wear model is presented in Table 3:

Table 3. The value of the coefficients model

Coefficients	Value	Coefficients	Value
b0	126	b14	– 5.830
b1	– 2.303	b22	–2.111
b2	– 3.056	b23	– 0.167
b3	2.028	b24	– 4.292
b4	4.806	b33	– 4.861
b11	– 0.611	b34	– 0.417
b12	– 3.170	b44	– 2.264
b13	– 1.295

Results and discussion

Verification of the adequacy of the developed model

Analysis of Variance (ANOVA) and the F-ratio test was performed to check the adequacy of the model as well as the significance of the individual model coefficients. The ANOVA was carried out for a confidence limit of 95% or P-value of 0.05. This implies any factor with P-value equal to or less than 0.05 is significant. From the analysis of the results obtained in table 3, it is clear that sliding velocity (A), sliding distance (B), load (C) and fraction of PKSAp reinforcement (D).

Are significant along with the interactions BD and CD and the quadratic terms of A, B and D as the P value of these terms is less than 0.05. Other model terms can be said not to be significant. These insignificant model terms can be removed and may result in an improved model [11].                                                                   

Another criterion that is commonly used to illustrate the adequacy of a fitted regression model is the coefficient of determination (R2). For the models developed, the calculated R2 value and adjusted R2 value are 97.84% and 93.62% respectively.

Table 3. ANOVA for specific wear rate of Al-Cu-Mg/PKSAp

Source	SS	DF	MS	F-Value	P-Value
Constant	2.02436	14	0.96569	53.86	0.0021
A	0.003263	1	0.003263	17.82	0.0046
B	0.054209	1	0.054209	89.09	0.0075
C	0.009741	1	0.009741	7.37	0.0224
D	0.000448	1	0.000448	0.074	0.0016
AB	0.001627	1	0.001627	15.66	0.0576
AC	0.054825	1	0.054825	2.89	0.0682
AD	0.007683	1	0.007683	3.26	0.0976
BC	0.009569	1	0.009569	1.52	0.2179
BD	0.000321	1	0.000321	0.96	0.0013
CD	0.008346	1	0.008346	2.21	0.0363
A^2	0.002771	1	0.002771	2.73	0.0044
B^2	0.002681	1	0.002681	2.46	0.0268
C^2	0.001762	1	0.001762	1.08	0.8743
D^2	0.059178	1	0.059178	3.79	0.0466
Residual error	0.734415	6	0.037769	-	-
Lack of fit	0.008382	10	0.008382
F-ratio as per table (14, 6, 0.05) = 3.96

These values indicate that the regression model is quite adequate. The adequacy of the model was further confirmed by a scatter diagram. Figure 2 shows a typical scatter diagram for the model (of wear of Al-Cu-Mg/PKSAp). The observed values and predicted values of the responses are scattered close to the 45⁰C line, indicating an almost perfect fit of the developed model.

Figure 2. Normal probability plot of residuals

Conformity tests  

Five experimental data which were never used in the modelling process were used to test the performance of the model.

Table 4. Results of conformity test

Input variables				Specific Wear rate m3/N-m×10-13
A	B	C	D	Measured	Predicted	% Error
0.5	300	10	3	8.998	8.714	3.259
0.9	500	20	6	9.444	9.106	3.712
1.5	1000	35	9	5.717	6.071	-5.831
1.5	1400	35	12	7.320	6.801	7.631
3.0	1900	65	12	6.112	5.905	3.506

The difference in the experimental values of wear corresponding to a set of input parameters and the predicted values were taken as error of prediction and are calculated as per Eq. (5) reported as % error in Table 4 along with other results. It is observed from this table that the results are within acceptable range and the average deviation is 2.455%.

 %  =      (5) 

Effects of the different process parameter

The effects of the different process parameter on the wear behaviour of Al-Cu-Mg/PKSAp composite are predicted from the developed mathematical model by varying one parameter value from its maximum level to maximum level while keeping the other three parameters values at their centre levels. The experimental results are plotted and presented in figures (4-7) as a function of wear. The general trends between cause and effect are discussed below.

Effect of sliding velocity

The effect of sliding velocity on the specific wear rate of Al-Cu-Mg/PKSAp composite is shown in Figure 4a; it is obvious from the figures that wear rate increase with sliding velocity. Sliding velocity influences the frictional heat developed in the area of contact between the test pin and counter surface.

Figure 4a. Effect of sliding velocity on the wear rate

More frictional heat is developed in the contact area when sliding velocity is increased [19]. Thus micro thermal softening of matrix material may take place which lowers the bonding strength of PKSAp with aluminium matrix alloy [20]. 

Figure 4 (b) shows SEM micrographs of worn surface of the cast Al-Cu-Mg/20%CSAp composite at sliding velocity of 2m/s with normal load of 50 KN and sliding distance of 2000 m.

Figure 4b. SEM image of worn

The figure shows that the extended PKSAp can be easily pulled out from the matrix as a result of micro thermal softening of matrix and higher shearing force developed on the contact surface. Those pulled-out PKSAp particles may act as wear debris between test pin and counter face and form the third body abrasive wear mechanism [21].

Effect of sliding distance

Figure 5a shows the variation of wear rate with sliding distance of Al-Cu-Mg/PKSAp composite. It is evident from the figure that sliding distance increases linearly with the wear rate.

Figure 5a. Effect of sliding distance on the wear rate

When the sliding distance increases, frictional heat on the contact surface also increases. As the raised temperature in the contact surface decreases, the resistance offered by the matrix against the shear force, the rate of deformation as well as pull-out of PKSAp from the matrix are increased. This leads to subsurface cracks which nucleate at the interfaces between PKSAp and aluminium alloy matrix as depicted in Figure 5b (worn surface of Al-Cu-Mg/20%PKSAp composite tested at sliding distance of 2000 m).

Figure 5b. SEM image of worn surface

The worn surface of the composite reveals continuous deep grooves. The edges at the groves are plastically deformed, due to the generation of higher frictional heat [21].

Effect of normal load

Figure 6 a show the effect of applied load on the wear rate of Al-Cu-Mg/PKSAp composite.

Figure 6a. Effect of applied load on the wear rate

From Figure 6 a, it is observed that wear rate of the composite increases while increasing the applied load. This is because at higher load, frictional thrust increases, which results in increased deboning and fracture. A similar effect of normal load on wear rate has been observed by [22]. Figure 6b shows SEM micrograph of the worn surface of cast Al-Cu-Mg/ 20%PKSAp composite at normal load of 50 N with sliding distance of 2000 m and sliding velocity of 2 m/s.

Figure 6b. Worn surface tested at applied load of 50N

The worn surface shows asperities of broken soft aluminium matrix due to shear force on the worn surface. Due to the high applied pressure, cracks nucleate as a result of ploughing of hard asperities.

Effect of fraction of reinforcement

Figure 7a shows the wear rate of Al-Cu-Mg/PKSAp composite as a function of mass fraction of PKSA in the matrix. It is obvious from the figure that the wear rate decreases with increase in the mass fraction of PKSA by keeping other wear process parameters constant, this result is in agreement with similar works by [21].

Figure 7a. Effect of fraction of reinforcement on the wear rate

Figure 7b shows the uniform distribution of PKSAp in the Al-Cu-Mg matrix and Figure 7c shows the SEM image of worn surface of Al-Cu-Mg/20%PKSAp composite.

Figure 7b. SEM image of worn surface

Figure 7c. SEM image of worn surface

Conclusions

The relationships between process parameters for wear behaviour of Al-Cu-Mg/PKSAp have been established. Response surface methodology was adopted to develop the regression models, which were checked for their adequacy using ANOVA test, scatter diagrams are found to be satisfactory.

Confirmation experiments showed the developed models are reasonably accurate. The accuracy of the developed model can be improved by including more number of parameters and levels. Wear co-efficient tends to decrease with increasing particle volume content. It also indicates that coconut shell ash addition is beneficial in reducing wear of the Al-Cu-Mg/PKSAp composite.

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