adel mahmood hassan*, mohammed almomani, tarek qasim and ahmed …tqqasim/publ-pdf/statistical...

Int. J. Experimental Design and Process Optimisation, Vol. 3, No. 1, 2012 91

Copyright © 2012 Inderscience Enterprises Ltd.

Statistical analysis of some mechanical properties of friction stir welded aluminium matrix composite

Adel Mahmood Hassan*, Mohammed Almomani, Tarek Qasim and Ahmed Ghaithan Department of Industrial Engineering, Jordan University of Science and Technology, P.O. Box 3030, Irbid 22110, Jordan E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] *Corresponding author

Abstract: In this study, the effect of friction stir welding (FSW) parameters such as rotational speed, welding (transverse) speed, and the type of pin profile tool on some mechanical properties was statistically investigated. Plates of aluminium matrix composites fabricated by stir casting method were joined by friction stir welding process. The statistical analysis has shown that the most important factor affecting hardness and tensile strength is the welding (transverse) speed, while the rotational speed has a second ranking and pin profile tool geometry is the least. The rotational speed has no statistical significant influence on the wear rate. However, the nugget zone, which was welded by square pin profile tool, seemed to exhibit better mechanical properties compared to those obtained by other pin profile tools.

Keywords: metal matrix composites; friction stir welding; FSW; mechanical properties.

Reference to this paper should be made as follows: Hassan, A.M., Almomani, M., Qasim, T. and Ghaithan, A. (2012) ‘Statistical analysis of some mechanical properties of friction stir welded aluminium matrix composite’, Int. J. Experimental Design and Process Optimisation, Vol. 3, No. 1, pp.91–109.

Biographical notes: Adel Mahmood Hassan is currently working at Jordan University of Science and Technology, Jordan, as a Professor in Engineering Materials. He holds a BSc from the University of Baghdad, Iraq, an MSc from University of Salford, England and a Dr-Ing from Hannover University, Germany. His research interests are in the fields of burnishing, precipitation hardening, particulate metal matrix composites, wear of metals and friction stir welding.

Mohammed Almomani is an Assistant Professor at Industrial Engineering Department at Jordan University of Science and Technology. He earned his PhD in Materials Engineering from the University of Wisconsin-Milwaukee (UWM). He holds a BSc in Mechanical Engineering from Jordan University of Science and Technology, and an MSc in Industrial Engineering from University of Jordan. His research interests include nanomaterials, composite material, corrosion, electrochemistry, maintenance engineering, statistic and design of experiments.

92 A.M. Hassan et al.

Tarek Qasim is an Assistant Professor at Industrial Engineering Department at Jordan University of Science and Technology. He received the PhD from the University of Western Australia, MSc from Tuskegee University, and BSc from University of Mosul. All of the above degrees were in mechanical engineering. His research interests include: fracture and fatigue of engineering materials; advanced structural ceramics and composites; fracture mechanics modelling, and coupling finite element analysis (FEA) results with experimental observations.

Ahmed Ghaithan earned his BSc in Mechanical Engineering from the Hashemite University, and MSc in Industrial Engineering from Jordan University of Science and Technology. His research interests include composite material, friction stir welding, statistic and design of experiments.

1 Introduction

Friction stir welding (FSW) was invented at The Welding Institute (TWI) of UK in 1991 (Kallee, 2006) as a solid-state joining technique (Thomas and Dolby, 2003). The basic concept of FSW is remarkably simple. In the FSW process, parts to be joined must be clamped to backing plate in order to prevent them from moving during the welding process. A rotating tool is inserted into the butting edges of plates to be joined as shown in Figure 1 (Wang et al., 2009). The pin of the tool is moved along the joint direction with a constant welding (transverse) speed where the pin of the tool serves two primary functions: first to heat of workpiece and second to move the material in order to produce the joint. The moved material is softened, and moved around the periphery of the pin tool, and subsequently re-coalesced along the back of the pin to produce the joint.

Figure 1 Schematic representation of FSW process

Source: Wang et al. (2009)

The localised heating softens the material around the pin and combination of tool rotation and transformation leads to the movement of material from the front to the back of the pin. As a result of this process, the material along the joint undergoes intense plastic

Statistical analysis of some mechanical properties of friction stir 93

deformation at elevated temperature, resulting in the generation of fine and equiaxed recrystallised grains (Jata and Semiatin, 2000; Liu et al., 2006). The fine microstructure in friction stir welds produces good mechanical properties (Mishra and Ma, 2005). As a result, the friction stir weld joint consists of three distinct zones: the nugget zone (NZ), the thermo-mechanically affected zone (TMAZ), and the heat-affected zone (HAZ) as illustrated in Figure 2. FSW can be used for joining different metals, high-strength alloys, and composite materials that have limitations to be welded by conventional fusion welding (Storjohann et al., 2005).

Figure 2 Zones of FSW joint

Source: Mishra and Ma (2005)

At present, most studies of the FSW focus on the effect of welding parameters such as welding (transverse) speed and rotational speed on FSW joint properties (Jones et al., 2005; Chen and Kovacevic, 2004). Little work has been done on the statistical analysis of FSW parameters. Some work has been carried out on design of experiments in order to examine the optimal level of FSW parameters, and to develop empirical models for the prediction of some mechanical properties of friction stir welded joints. For instance, the design of experiments concept and response surface method have been used to develop an empirical model to predict the tensile strength of friction-stir welded AA6061 aluminium alloy joints and to optimise the FSW process parameters to obtain the maximum tensile strength (Elangovan et al., 2009). Empirical relationship was introduced to predict the FSW process parameters such as rotational speed and welding (transverse) speed. The responses that are predicted were yield strength, ductility, and hardness (Balasubramanian, 2008). A response surface methodology was developed to predict the tensile strength of friction-stir welded AA7039 aluminium alloy (Lakshminarayanan and Balasubramanian, 2009). In addition, artificial neural networks (ANNs) were used for calculation of the mechanical properties of welded aluminium plates using FSW method. It was found that the correlations between the measured and predicted values of tensile strength, hardness of weld material were better than those of percentage elongation and yield strength (Okuyucu et al., 2007).

The objectives of this paper are to obtain the optimal response values in order to determine the significant parameters in FSW of aluminium matrix composites (AMCs), to set the optimal level for each of these parameters, also, to predict which responses are affected. Analysis of variance (ANOVA), main effect plot, and interactions plot are used to establish the correlation between the factors and responses.


2 Experimentation

2.1 Experimental design

Design of experiments is a statistical method used to investigate any production process. An experiment can be defined as series of tests in which changes are made to the input variables of a process or system (Montgomery, 2005). ANOVA is a statistical method applied to the results of the experiments to determine the percentage contribution of each parameter against a stated level of confidence (Ross, 1988). The effects of the FSW parameters on the selected response were investigated through the main effects plots. The optimum level for each FSW parameter was established through ANOVA.

The purpose of the statistical ANOVA is to determine the most influential design parameter that significantly affects the mechanical properties for the friction stir welded joints. Also, ANOVA is used to investigate the relationship between the response and selected process parameters. The problem to be solved in this study was to examine the possible differences in the mechanical properties (tensile strength, hardness, and wear resistance) of friction stir welded joints, which result from different combinations of the selected variables. The dependent variables for the set of experiments were chosen to be the tensile strength, hardness, and wear resistance. One replicate was performed for the three process parameters, i.e., welding (transverse) speed, pin profile tool, and rotational speed in randomised order during conducting FSW. The Minitab software was used to study the statistical analysis for the obtained results. Welding parameters and their factor levels are shown in Table 1. Table 1 Welding parameters and their factor levels

Parameters Level 1 Level 2 Level 3 Level 4

Pin profile tool Square Hexagonal Octagonal ------ Welding speed (mm/min) 35 45 55 65 Rotational speed (rpm) 630 800 1,000 1,250

2.2 Multiple regression prediction models

Multiple regression is an approach that is used to model the relationship between a dependent variable (response) and two or more regressors or predicate variables. The most important application of the regression model is to be used as a predictive model for the value of the response (Y) at specific values of xj. Least square method is one of the most popular methods that are used to estimate the regression coefficients to determine the best fit to data. This method is based on minimising the sum of squares of the errors between the measurements and the model (i.e., the predicted values) (Montogomery, 2005).

Multiple quadratic regression with a general form given in equation (1) is used to model the relation between a selected response of the mechanical properties (tensile strength, hardness, and wear resistance) of friction stir welded joints for different combinations of the process variables (tool pin profile, rotational speed, and welding (transverse) speed). The Minitab software was used to develop the proposed regression model.


1 2 3 4 5

2 2 26 7 8 9

o p s s p s p s

s s p s s

Y T R W T R T W

R W T R W

β β β β β β

β β β β

= + + + + +

+ + + + (1)

where Y is the estimated response and Tp, Rs, Ws are the tool pin profile, rotational speed, and welding speed respectively. Bj, (j = 1,2,…,9) is the regression coefficients.

2.3 Experimental equipment and setup

2.3.1 Materials

Commercial pure aluminium alloyed with 4 wt.% Mg, reinforced with 1 wt.% SiC- 1 wt.% graphite were used in the stir casting process to produce 100 mm × 75 mm × 8 mm plates. The chemical composition of the AMC is shown in Table 2. Commercial pure magnesium blocks with a density of 1.72 g/cm3 was used as a wetting agent in order to enhance wettability between aluminium matrix and the reinforced particles. Silicon carbide powder having a diameter of 200 µm and a density of 3.21 g/cm3 was chosen as reinforcement particles because they have high wear resistance. Table 2 Chemical composition of the aluminium in weight percentage

Cu Mg Si Fe Mn Ni Zn Ti Cr S C Al

0.02 3.95 0.88 0.72 0.02 0.08 < 0.01 < 0.01 0.19 0.008 0.418 93.694

In addition, graphite particles having a density of 2.1 g/cm3 were used as second reinforcement particles to improve the machinability and wear resistance of the considered composites because the graphite acts as a lubricating agent (Suresha and Sridhara, 2010). The mechanical properties of the annealed composites are shown in Table 3. Table 3 Mechanical properties of AMCs used in this study

Tensile strength (MPa) Yield strength (MPa) Rockwell hardness (HRH)

130 72.8 88.3

2.3.2 Tools and plates processing

The commercial pure aluminium small ingot blocks were put inside graphite crucible. The metal was heated to a temperature of 900°C by an electrical furnace, until the aluminium melted. Both SiC and graphite particles were wrapped inside aluminium foils, preheated, then, added to the molten aluminium and stirred to get a good distribution of the particles inside the melt. Also, the commercial pure magnesium ingots were added to the melt. The melt was stirred continuously. Finally, the melt poured into a permanent stainless steel mold to produce the plates. The pouring temperature was kept between 600°C to 700°C. The mould was left to be cooled to room temperature (Hassan et al., 2009). All plates produced were annealed to 400°C for a period of two hours before FSW process.

Tools with hexagonal, square, and octagonal pin profiles were fabricated from steel have 0.4% of carbon by machining processes. The steel were oil hardened and tempered


to reach a hardness of 63 HRC. The schematic diagrams for the fabricated tools are shown in Figure 3.

Figure 3 Different profiles for the tools, which are used in FSW (all dimensions are in mm) (a) hexagonal pin profile tool (b) square pin profile tool (c) octagonal pin profile tool

(a)

(b)

(c)

2.3.3 Fixture and machine

A conventional milling machine was used to join the AMCs plates by FSW process. A special fixture was designed in order to make the conventional milling machine more suitable for the process, shown in Figure 4. The fixture was fastened firmly on the milling machine. Then the plates were clamped firmly to the fixture and butt welded by FSW process.


2.3.4 Mechanical tests

Rockwell hardness was used to measure the hardness of the weld NZ performed by a universal hardness testing machine. Tensile test specimens were prepared by CNC milling machine so that the welded joint was in the centre of the specimen as shown in Figure 5.

Figure 4 Schematic for fixture used in this study (see online version for colours)

Figure 5 Schematic representation of tensile test specimen (all dimensions are in mm) (see online version for colours)

(a)

The dry wear tests were carried out at a normal load of 50 N and rotational speed of 100 rpm using a pin-on-disk type test machine. The wear rate can be calculated using equation (2) (Zhang and Fuping, 2007):

/ ( )W M D S= × (2)

where W: wear rate expressed in (cm3/m), M: mass loss during wear in (g), S: sliding distance in (m) and, D: density of the respective composite in (g/cm3), which was 2.67 g/cm3. The density of the composite was determined using the rule of mixture formula, where it was determined by multiplying the weight fractions of each element by its density as shown in equation (3).


( ) ( )( ) ( )

94% 4%

1% 1%composite Al Mg

SiC Graphite

AlX MgX

SiCX GrX

ρ ρ ρ

ρ ρ

= +

+ + (3)

where ρ is the density of each considered material.

3 Data analysis

3.1 Statistical analysis of hardness

The ANOVA test results for the hardness of the FSW joints are shown in Table 4. This analysis is carried out at a confidence level of 95%. We started by assuming that the three factor interaction term is non-existent. That allows us to accumulate the sums of squares for this term and use them to estimate an error term. Since the P values are less than 0.05 for the three process parameters, then varying the pin profile tools, rotational and welding (transverse) speeds have a significant influence on the hardness of the FSW weld joints. In other words, using various pin profile tools, rotational and welding (transverse) speeds will result in different values of hardness. Table 4 ANOVA test results for the average hardness of NZ

Source DF Seq SS Adj SS Adj MS F P

Pin profile 2 2.9892 2.9892 1.4946 10.93 0.001 Rotational speed (rpm) 3 32.9863 32.9863 10.9954 80.42 0.000 Welding speed (mm/min) 3 51.1032 51.1032 17.0344 124.59 0.000 Pin profile * rotational speed (rpm) 6 2.1835 2.1835 0.3639 2.66 0.050 Pin profile * welding speed (mm/min) 6 1.7674 1.7674 0.2946 2.15 0.097 Rotational speed (rpm) * welding speed (mm/min)

9 1.8626 1.8626 0.2070 1.51 0.217

Error 18 2.4610 2.4610 0.1367 Total 47 95.3533

Notes: S = 0.369757; R-Sq = 97.42%; R-Sq(adj) = 93.26%

Figure 6 depicts the plots of the main effects for hardness. It shows that welding (transverse) speed has a higher significant effect on hardness than the other parameters and the effect of this factor is directly proportional to hardness responses. It can be stated that the increasing of welding (transverse) speed causes the hardness to increase significantly. The welding (transverse) speed of 45 mm/min shows a lower hardness values. The rotational speed of 630 rpm shows higher hardness. Pin profile tool also shows an effect on hardness.

ANOVA test results also suggest that the effect of pin profile or rotational speed on the hardness is not depending on the value of the welding speed, as concluded from the high values of P (0.097, 0.217). In other words, there is no statistical significant interaction between pin profile and welding speed or between rotational and welding speeds.


Figure 6 Main effects plots for hardness (HRH) (see online version for colours)

Figure 7 Interactions plot for hardness (HRH) (see online version for colours)


Even though, Figure 7 reveals patterns of line segments crossing one another or slight aberrations in the interaction plot paths, there is no real ‘disorder interaction’ between pin profile and welding speed or between rotational and welding speeds, and the profile plot paths happen to cross due to random variation.

From the analysis of Table 4 it is evident that capabilities of the model, R2 is higher than 0.90. Normal probability plot of residuals (Figure 8) shows no severe deviation from the normality. This result verifies the basic assumption used in our analysis (errors are normally distributed).

Figure 8 Normal probability plot of the residuals for average Rockwell hardness of NZ (see online version for colours)

3.2 Statistical analysis of tensile strength

The results of F-test for tensile strength at a confidence level of 95% are shown in Table 5. We started by assuming that the three factor interaction term is non-existent. The results make obvious that the three process parameters (pin profile tools, rotational and welding (transverse) speeds) have a significant influence on the tensile strength of the FSW weld joints as the P values are less than 0.05. Welding (transverse) speed has more effect on tensile strength responses variation compared to rotational speed and tool pin profile type as explained in the main effects plots shown in Figure 9. This figure shows that welding (transverse) speed has a higher significant effect on tensile strength than the other parameters.


Table 5 ANOVA test results for the tensile strength


Pin profile 2 5,857.3 5857.3 2,928.6 28.23 0.000 Rotational speed (rpm) 3 9,723.3 9,723.3 3,241.1 31.24 0.000 Welding speed (mm/min) 3 18,653.3 18,653.3 6,217.8 59.93 0.000 Pin profile * rotational speed (rpm) 6 368.0 368.0 61.3 0.59 0.733 Pin profile * welding speed (mm/min) 6 1,707.5 1,707.5 284.6 2.74 0.045 Rotational speed (rpm) * welding speed (mm/min)

9 516.5 516.5 57.4 0.55 0.817

Error 18 1,867.4 1,867.4 103.7 Total 47 38,693.2


Figure 9 Main affects plot for tensile strength (see online version for colours)

The interaction plot, Figure 10, together with high P values (0.817, 0.733) suggests no statistical interaction between the rotational speed and the tool pin profile or between the rotational speed and the welding speed. In other words, the effects of the rotational speed on the tensile strength do not depend on the type of the used pin profile or the level of the welding speed.


Figure 10 Interactions plot for tensile strength (see online version for colours)

From the analysis of Table 5 it is evident that capabilities of the model, R2 factors, are satisfactory. Figure 11 shows the normal probability plot for the tensile strength. This figure supports the normality assumption, where the residuals are distributed along the straight line.

Figure 11 Normal probability plot of the residuals for tensile strength response (see online version for colours)


3.3 Statistical analysis of wear rate

The ANOVA test results for the wear rate of the FSW joints are shown in Table 6. This analysis is carried out at a confidence level of 95%. We started by assuming that the three factor interaction term is non-existent. It was found that both tool pin profile and welding speed have statistically significant influence on the wear rate of the FSW joints, meaning using various pin profile tools, and various welding (transverse) speed will result in changes in the wear rate that is statically important. The P value for the rotational speed (0.411) is greater than 0.05 indicating that changes of the wear rate produced from varying rotational speed are not statically significant. Table 6 ANOVA test results for the wear


Pin profile 2 0.282245 0.282245 0.141122 49.36 0.000 Rotational speed (rpm) 3 0.008674 0.008674 0.002891 1.01 0.411 Welding speed (mm/min) 3 0.129896 0.129896 0.043299 15.14 0.000 Pin profile * rotational speed (rpm) 6 0.08707 0.08707 0.001451 0.51 0.795 Pin profile * welding speed (mm/min) 6 0.028951 0.028951 0.004825 1.69 0.181 Rotational speed (rpm) * welding speed (mm/min)

9 0.020363 0.020363 0.002263 0.79 0.628

Error 18 0.051462 0.051462 0.002859 Total 47 0.530297


Figure 12 Main effects plot for wear rate (see online version for colours)


Figure 13 Interactions plot for wear rate (see online version for colours)

Figure 14 Normal probability plot of the residuals for wear rate response (see online version for colours)


Figure 12 shows the plots of the main effects for wear rate. It shows that the tool pin profile has a higher significant effect on wear rate than the other parameters. The changes of the wear rate resulting from varying rotational speed were small as shown in proximity horizontal profile path.

ANOVA test results also suggest that there is no ‘disorder interaction’ between the processing parameters, as concluded from the high values of P. The slight aberrations and the profile path crossing, shown in Figure 13, have resulted from random variations.

The model coefficient of determination R2 is satisfactory ( > 70%). Normal probability plot of residuals (Figure 14) shows no severe deviation from the normality. This result verifies the basic assumption used in our analysis (errors are normally distributed).

3.4 Multiple regression model

Minitab software was used to formulate the regression equations (4–6) that predict the average response of hardness, tensile strength, and wear of the FSW joints as a function of FSW processing parameters (tool pin profiles, rotational speed, and welding (transverse) speed). The levels of the FSW process parameters used in the regression model and their corresponding codes are shown in Table 7.

2

Avg Rockwell hardnessof NZ (HRH) 96.9 0.288 0.372 1.25

0.184 0.174 0.345p s s

p s p s s

T R W

T R T W W

= + − −

− + + (4)

3 6

2 2

Average wear rate (cm /m) 10 1.13 0.353 0.00975 0.205

0.0193 0.0952 0.0439p s s

p s p s

T R W

T W T W

−× = − − +

+ + − (5)

2 2 2

UTS (MPa) 119 79.1 4.2 25.2 1.88 0.69

0.52 20.6 0.69 7.69p s s p s p s

s s p s s

T R W T R T W

R W T R W

= + − − − +

− − − + (6)

Table 7 FSW process parameters and their levels used in regression analysis

Level Code

Hexagonal 1 Square 2

Pin profile tool

Octagonal 3 630 1 800 2

1,000 3

Rotational speed (rpm)

1,250 4 35 1 45 2 55 3

Welding (transverse) speed (mm/min)

65 4


This analysis is conducted at a significance level of 5%, that is, confidence level of 95%. The computed adjusted R2 values (Table 8) are satisfactory ( > 70%) indicating that the proposed models are capable of explaining most of the variability in the considered mechanical properties, i.e., the second order regression model for the hardness can explain the variation of the hardness in terms of FSW processing parameters to the extent of 86%. Table 8 Adjusted R2 values for the hardness, wear rate (cm3/m), and tensile strength (MPa)

Hardness Wear rate (cm3/m) × 10-6 Tensile strength (MPa)

Adjusted multiple regression coefficient R2

0.86 0.79 0.70

The adequacy of the proposed regression models is examined by residual analysis. Figure 15(a) to 5(c) show the residual plots for the average Rockwell hardness, tensile strength, and wear rate, respectively. The normal probability plot and the frequency histogram of the residuals in each indicate that errors are normally distributed. In addition, the plots of the residual versus fitted value or observation order do not indicate any serious model inadequacies. This result verifies the assumption that errors are uncorrelated random variables.

Figure 15 Residuals plot for (a) average Rockwell hardness, (b) tensile strength, and (c) wear rate (see online version for colours)

(a)


Figure 15 Residuals plot for (a) average Rockwell hardness, (b) tensile strength, and (c) wear rate (continued) (see online version for colours)

(b)

(c)


4 Conclusions

In this research, a statistical investigation was performed to consider hardness, tensile strength, and wear resistance in FSW of AMC. The statistical plan was general full-factorial design method. The relative effect of each factor and combination of factors on responses was obtained by ANOVA. The following conclusions may be withdrawn from the present work:

1 Hardness, tensile strength, and wear resistance of the welded zone are increased with increasing the welding (transverse) speed. They are also increased with decreasing the rotational speed.

2 According to the ANOVA results, the most important factor on the hardness and tensile strength of the composite is the welding (transverse) speed, while the rotational speed is second ranking factor and pin profile tool geometry is the least. While the change of the wear rate for various rotational speeds are not statistically significant.

3 Although, the hardness, tensile strength, and wear resistance are improved by the considered different types of pin profile tools. The joints welded by the square pin profile tool have better mechanical properties followed by hexagonal pin profile tool and octagonal pin profile tool respectively.

4 Quadratic multiple regression model is quite satisfactory to obtain predictions for the hardness, tensile strength, and wear resistance within the ranges of parameters that have been investigated during the experiments.

Acknowledgements

The authors are grateful to the Scientific Research Deanship/Jordan University of Science and Technology for its support of this research (Grant No. 2010/195). Thanks also extended to all members of the engineering workshop department and laboratories at Jordan University of Science and Technology for their help and assistance.

References Balasubramanian, V. (2008) ‘Relationship between base metal properties and friction stir welding

process parameters’, Material Science and Engineering: A, Vol. 480, Nos. 1–2, pp.397–403. Chen, C.M. and Kovacevic, R. (2004) ‘Joining of aluminum to steel with a combination of solid

state welding and fusion welding’, International Journal of Machine Tools and Manufacture, Vol. 44, No. 11, pp.1205–1214.

Elangovan, K., Balasubramanian, V. and Babu, S. (2009) ‘Predicting tensile strength of friction stir welded 6061 aluminium alloy joints by mathematical model’, Materials and Design, Vol. 30, No. 1, pp.188–193.

Hassan, A.M., Hayajneh, M., Alrashdan, A. and Mayyas, A.T. (2009) ‘Prediction of density, porosity and hardness in aluminum-copper based composite materials using artificial neural network’, Journal of Materials Processing Technology, Vol. 209, No. 2, pp.894–899.

Jata, K.V. and Semiatin, S.L. (2000) ‘Continuous dynamic recrystallization during friction stir welding of high strength aluminum alloys’, Scripta Materialia, Vol. 43, No. 8, pp.743–749.


Jones, M.J., Heurtier, P., Desrayaud, C., Montheillet, F., Allehaux, D. and Driver, J.H. (2005) ‘Correlation between microstructure and microhardness in a friction stir welded 2024 aluminium alloy’, Scripta. Materialia, Vol. 52, No. 8, pp.693–697.

Kallee, S.W. (2006) Friction Stir Welding at TWI, The Welding Institute (TWI), Cambridge, England.

Lakshminarayanan, A.K. and Balasubramanian, V. (2009) ‘Comparison of RSM with ANN in predicting tensile strength of friction stir welded AA7039 aluminium alloy joints’, Transactions of Nonferrous Metals Society of China, Vol. 19, No. 1, pp.9–18.

Liu, H.J., Chen, Y.C. and Feng, J.C. (2006) ‘Effect of heat treatment on tensile properties of friction stir welded joints of 2219-T6 aluminium alloy’, Materials Science and Technology, Vol. 22, No. 2, pp.237–241.

Mishra, R.S. and Ma, Z.Y. (2005) ‘Friction stir welding and processing’, Materials Science and Engineering R, Vol. 50, Nos. 1–2, pp.1–78.

Montgomery, D.C. (2005) Design and Analysis of Experiments, 6th ed., John Wiley & Sons, USA. Okuyucu, H., Kurt, A. and Arcaklioglu, E. (2007) ‘Artificial neural network application to the

friction stir welding of aluminum plates’, Materials and Design, Vol. 28, No. 1, pp.78–84. Ross, P.J. (1988) Taguchi Technique for Quality Engineering, McGraw-Hill, New York, USA. Storjohann, D., Barabash, O.M., Babu, S.S., David, S.A., Sklad, P.S. and Bloom, E.E. (2005)

‘Fusion and friction stir welding of aluminum-metal-matrix composites’, Metallurgical Materials Transactios A, Vol. A36, No. 11, pp.3237–3247.

Suresha, S. and Sridhara, B.K. (2010) ‘Effect of addition of graphite particulates on the wear behaviour in aluminium-silicon carbide-graphite composites’, Materials and Design, Vol. 31, No. 4, pp.1804–1812.

Thomas, W.M. and Dolby, R.E. (2003) ‘Friction stir welding developments’, Proceedings of the Sixth International Trends in Welding Research, Materials Park, ASM International, USA, pp.203–211.

Wang, W., Qing-yu, S., Peng, L., Hong-ke, L. and Li, T. (2009) ‘A novel way to produce bulk SiCp reinforced aluminum metal matrix composites by friction stir processing’, Journal of Materials Processing Technology, Vol. 209, No. 4, pp.2099–2103.

Zhang, S. and Fuping, W. (2007) ‘Comparison of friction and wear performances of brake material dry sliding against two aluminum matrix composites reinforced with different SiC particles’, Journal of Materials Processing Technology, Vol. 182, Nos. 1–3, pp.122–127.

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