Advertisement

Advances in Manufacturing

, Volume 2, Issue 4, pp 303–317 | Cite as

RETRACTED ARTICLE: Effects of roller burnishing process parameters on surface roughness of A356/5%SiC composite using response surface methodology

  • Shashi Prakash DwivediEmail author
  • Satpal Sharma
  • Raghvendra Kumar Mishra
Article

Abstract

In this study, a simple roller burnishing tool was made to operate burnishing processes on A356/5%SiC metal matrix composite fabricated by electromagnetic stir casting under different parameters. The effects of burnishing speed, burnishing force and number of burnishing passes on the surface roughness and tribological properties were measured. Scanning electron microscopy (SEM) graphs of the machined surface with PCD (insert-10) tool and roller burnished surface with tungsten carbide (WC) roller were taken into consideration to observe the surface finish of metal matrix composites. The mechanical properties (tensile strength, hardness, ductility) of A356/5%SiC metal matrix composites were studied for both unburnished samples and burnished samples. The results revealed that the roller burnished samples of A356/5%SiC led to the improvement in tensile strength, hardness and ductility. In order to find out the effects of roller burnishing process parameters on the surface roughness of A356/5%SiC metal matrix composite, response surface methodology (RSM) (Box–Behnken design) was used and a prediction model was developed relevant to average surface roughness using experimental data. In the range of process parameters, the result shows that roller burnishing speed increases, and surface roughness decreases, but on the other hand roller burnishing force and number of passes increase, and surface roughness increases. Optimum values of burnishing speed (1.5 m/s), burnishing force (50 N) and number of passes (2) during roller burnishing of A356/5%SiC metal matrix composite to minimize the surface roughness (predicted 1.232 µm) have been found out. There was only 5.03% error in the experimental and modeled results of surface roughness.

Keywords

Burnishing speed Burnishing force Response surface methodology (RSM) Box–Behnken design Desirability function 

1 Introduction

Increasing need for new lightweight materials with good mechanical properties has led to the development of a new generation of composite materials over recent decades, even though these increased mechanical properties after the addition of reinforcement create major challenges for machining with good surface quality. Composite materials with good mechanical properties, such as good strength, toughness and greater hardness, cause serious tool wear when traditional machining is used [1]. Burnishing is a low-cost surface treatment process and can be applied to improve surface quality. During burnishing, the generated pressure exerted by the tool exceeds the yield point of part surface at the point of contact, and causes a small plastic deformation. This plastic deformation created by roll or ball burnishing is a displacement of the material that flows from the peaks into the valleys under pressure, and results in a mirror-like surface finish with a strain-hardened, wear, and corrosion-resistant surface [2]. Both ball burnishing and roller burnishing are cold-working processes that do not involve material removal, and can produce work hardening of the part surface. Roller burnishing is applied to cylindrical workpieces on both external and internal surfaces, and its tools are similar to roller bearings [3].

El-Axir [4] studied the influence of burnishing speed, force, feed, and number of passes on both surface microhardness and roughness. Mathematical models were presented for predicting the surface microhardness and roughness of St-37 caused by roller burnishing under lubricated conditions. Variance analysis was conducted to determine the prominent parameters and the adequacy of the models. From an initial roughness of about surface roughness 4.5 µm, the specimen finished to a roughness of 0.5 µm. It is shown that the spindle speed, burnishing force, burnishing feed and number of passes have the most significant effect on both surface microhardness and surface roughness. El-Khabeery and El-Axir [5] presented an investigation of the effects of roller-burnishing upon surface roughness, surface microhardness and residual stress of 6061-T6 Al alloy. Mathematical models correlating three process parameters including burnishing speed, burnishing depth of penetration and number of passes, were established. It is shown that low burnishing speeds and high depth of penetration produce much smoother surfaces, whereas a combination of high speed with high depth leads to rougher surfaces because of chatter. The optimum number of passes that produces a good surface finish is found to be 3 or 4. Luo et al. [6] conducted the experiments with a simply designed cylindrical surfaced polycrystalline diamond tool. It was found that smaller parameters did not mean lower surface roughness or waviness, and different optimum burnishing parameters could be got under different burnishing conditions. Luo et al. [7] examined the effects of the burnishing parameters on the burnishing force and the surface microhardness with theoretical analysis and concluded that the burnishing feed and depth were the most significant factors. Luo et al. [8] compared theoretical results with the experiments in which Al alloy LY12 was selected as material for making the specimens. A new cylindrical polycrystalline diamond tool was developed for the burnishing process, and it showed that the theoretical model was basically correct in describing the burnishing process. Yeldose and Ramamoorthy [9] presented an investigation for the comparison of the effects of the uncoated and TiN coating by reactive magnetron sputtering on EN31 rollers in burnishing with varying process parameters. It was observed that the burnishing speed, burnishing force and number of passes had almost equal effect on the performance of the roller in burnishing, particularly with reference to the surface finish of the components produced. El-Taweel and El-Axir [10] showed that the burnishing force with a contribution percent of 39.87% for surface roughness and 42.85% for surface micro-hardness had the dominant effect on both surface roughness and micro-hardness followed by burnishing feed, burnishing speed and then by number of passes. Klocke et al. [11] observed an additional influence on the surface roughness for high roller ball diameters. Franzen et al. [12] showd that the process parameters of the roller burnishing process had a strong influence on the surface topology of the friction elements and their tribological properties. Sagbas [13] developed a quadratic regression model to predict surface roughness using response surface methodology (RSM) with rotatable central composite design (CCD). In the development of predictive models, burnishing force, number of passes, feed rate and burnishing speed were considered as model variables. Korzynski et al. [14] examined the effects of burnishing parameters on surface roughness and obtained the relevant mathematical models, and multinominals of the second order that also allow for the interaction of input factors for burnished 42CrMo4 alloy steel shafts. From the analysis it was concluded that surface microhardness increased by up to 29%. Świrad [15] introduced the new diamond sinter with ceramic bonding phase in the form of Ti3SiC2 as the tool material for sliding burnishing to eliminate existing defect of the applied composites. Tadic et al. [16] achieved high surface quality with relatively small burnishing forces for Al alloy EN AW-6082 (AlMgSi1) T651. Balland et al. [17] investigated the mechanics of roller burnishing through finite element simulation and experiments. Balland et al. [18] proposed a finite element modeling of the ball burnishing process and analyzed the effect of the burnishing process on the material.

On the basis of literature review, it was found that no researcher had investigated the mechanical properties and surface roughness of A356/SiC composite (Al/SiC composite) after roller burnishing with tungsten carbide rollers. Hence, in view of the above facts, an investigation was carried out to find the effects of roller burnishing process parameters on the surface roughness of A356/5%SiC metal matrix composite. The roller burnished A356/SiC composite was characterized in terms of the SEM micrograph of surface, tensile strength, ductility, hardness. In order to properly design a burnishing process, roller burnishing process parameters were optimized with respect to surface roughness using a Box–Behnken design RSM.

2 Materials and methods

2.1 Matrix alloy

In this study A356 alloy was selected. It has very good mechanical strength, ductility, hardness, fatigue strength, pressure tightness, fluidity, and machinability [19]. The chemical composition and properties of A356 are shown in Tables 1 and 2.
Table 1

Chemical composition of A356 alloy [19]

Element

Composition /wt.%

Si

6.5–7.5

Fe

0.2

Cu

0.2

Mn

0.1

Mg

0.25–0.45

Zn

0.1

Ti

0.1

Al

Balance

Table 2

Properties of A356 alloy [19]

Properties

Values

Liquidus temperature /°C

615

Solidus temperature /C

555

Density /(g·cm-3)

2.685

2.2 Reinforcement material

Silicon carbide was used as the reinforcement phase. To select a suitable reinforcement material for Al, important facts such as density, wettability and thermal stability were considered. Silicon carbide is a widely used reinforcement material because of its good wettability with the Al matrix [20, 21]. The silicon carbide particle parameters and properties are shown in Tables 3 and 4.
Table 3

Silicon carbide (beta) particle parameters

Properties

Values

Purity /%

95

Average particle size /µm

25

Density /(g·cm−3)

3.21

Morphology

Spherical

Table 4

Properties of silicon carbide

Properties

Values

Melting point temperature /°C

2,200–2,700

Hardness (Vickers)

2,800–3,300

Density /(g·cm−3)

3.2

Crystal structure

Hexagonal

2.3 Roller burnishing tool

A burnishing tool with changeable adapter roller was designed and fabricated for the purpose of the experimental tests. Figure 1 shows a schematic representation with dimension of the roller burnishing tool in which a shank is rigidly clamped on the lathe machine. A helical compression spring is used to exert the burnishing force during roller burnishing operations. A roller adapter is used to contain burnishing tungsten carbide (WC) roller with different rolls. A dial gauge is fixed at the end of the shank and directly placed in contact with the spring guide [22]. Thus, when roller burnishing force is applied, the axial sliding motion of the spring guide rod is identified by the dial gauge.
Fig. 1

Detailed sectional view of roller burnishing tool assembly [22]

2.4 Fabrication of metal matrix composite

Figure 2 shows the schematic of electromagnetic stir casting set-up. A356 alloy was heated to above 650 °C in muffle furnace. The temperature was controlled by connecting the relay from the muffle furnace and thermocouple up to 700 °C. Liquid A356 Al alloy at a given temperature (700 °C) was poured into a graphite crucible which was packed very well with the help of glass wool. Silicon carbide particles with average size of 25 µm were preheated at 450 °C for 1 h prior to introduction into the matrix. The argon gas was used at the tip of melt A356 alloy during the mixing of SiC. Coolant was used to provide the proper cooling to the windings of motor and vacuum box was used to provide vacuum inside the box to prevent casting defects. The prepared samples of A356/5%SiC metal matrix composites are shown in Fig. 3.
Fig. 2

Experimental set-up of electromagnetic stir casting process [23]

Fig. 3

Fabricated A356/5%SiC metal matrix composites by electromagnetic stir casting method

2.5 Selection of roller burnishing process parameters and their levels

Before the roller burnishing process of A356/5%SiC metal matrix composites, the turning processes [24, 25] were carried out in dry cutting conditions using CNC lathe with PCD (insert-10) tool. During turning of A356/5%SiC metal matrix composite, in all seventeen runs, depth of cut (0.20 mm), speed (3.16 m/s) and feed rate (0.14 mm/rev) were taken as fixed values [19]. After the machining of all seventeen turning samples, a lathe machine was used for roller burnishing process, as shown in Fig. 4. The roller burnishing processes were performed by clamping it on the tool post of lathe. The lathe machine has variable spindle speeds with a maximum power of 20 kW.
Fig. 4

Roller burnishing process

A calibration process was managed using the actual burnishing operation setting to obtain a relationship between the burnishing force, burnishing speed, number of passes and the corresponding surface roughness of A356/5%SiC metal matrix composite. During the roller burnishing tool calibration process on the surface roughness of machined A356/5%SiC metal matrix composite, burnishing speed of 1.17 m/s, burnishing force of 100 N and number of passes of 3 were taken as fixed values. The experimental surface roughness values of five samples of machined A356/5%SiC composite corresponding to these parameters (burnishing speed of 1.17 m/s, burnishing force of 100 N and number of passes of 3) were found to be 1.15 µm, 1.18 µm, 1.22 µm, 1.20 µm, 1.18 µm, respectively. This shows that there is only 5.73% error in the experimental results. Hence, the developed set-up for the roller burnishing can be effectively used. Figure 5 shows the SEM micrographs of the surface layer of the A356/5%SiC metal matrix composites during tool calibration process with WC roller.
Fig. 5

SEM micrographs of the surfaces of A356/5%SiC metal matrix composites generated under conditions in roller burnishing with WC roller

There are various process parameters of roller burnishing affecting the surface roughness. On the basis of pilot run investigations, the following process parameters were selected for study. Their ranges are given in Table 5.
Table 5

Process parameters with their ranges

Input parameters

Ranges

Burnishing speed /(m·s−1)

0.83–1.5

Burnishing force /N

50–150

Number of passes

2–4

2.6 RSM

RSM covers statistical experimental design, regression modeling technique, and optimization method. It is useful for the prediction and optimization of process parameters on machining performances. Box–Behnken design is an RSM design. It is used to study the quadratic effect of factors after identifying the significant factors using screening factorial experiments. Box–Behnken design does not contain any point at the vertices of the experimental region. This could be advantageous when the points on the corners of the cube represent factor-level combinations that are prohibitively expensive or impossible to test because of physical process constraints [19,26]. Steps involved in Box–Behnken design are given in Fig. 6.
Fig. 6

Steps involved in Box–Behnken design

Objective of the present work is to concentrate on the second strategy: statistical modeling to develop an appropriate approximating model between the response y and independent variables, ξ1, ξ2, ··· , ξk.

In general, the relationship is
$$ y = f({\xi_{1}}, {\xi_{2}} , \cdots ,{\xi_{k}}) + \varepsilon. $$
(1)
If normal distribution is with mean 0 and variance σ2, then, it may be written as
$$ E\left( y \right) = \eta = E(f(\xi_{ 1} ,\xi_{ 2} , \cdots ,\xi_{k} )) + E(\varepsilon ) = f(\xi_{ 1} ,\xi_{ 2} , \cdots ,\xi_{k} ) , $$
(2)
where variables ξ1, ξ2, ··· , ξk are usually the natural variables.
In terms of the coded variables, the response function (Eq. (2)) will be written as
$$ \eta = f(X_{ 1} ,X_{ 2} , \cdots ,X_{k} ). $$
(3)
For the case of two independent variables, the first-order model in terms of the coded variables will be written as
$$ \eta = \beta_{0} + \beta_{ 1} X_{ 1} + \beta_{ 2} X_{ 2} . $$
(4)
The form of the first-order model in Eq. (4) is sometimes called main effects model, because it includes only the main effects of the two variables X1 and X2. If there is an interaction between these variables, it can be added to the model easily as follows
$$ \eta = \beta_{0} + \beta_{ 1} X_{ 1} + \beta_{ 2} X_{ 2} + \beta_{ 1 2} X_{ 1} X_{ 2}. $$
(5)

2.7 Planning of experiments

The arrangement and the results of the 17 experiments carried out in this work based on the Box-Behnken design are shown in Table 6. The design is prepared with the help of Design Expert Software, which is used to create experimental designs.
Table 6

Design matrix and experimental results

Standard order

Run

Burnishing speed /(m·s−1)

Burnishing force /N

Number of passes

Surface roughness /µm

10

1

1.17

150

2

0.500

2

2

1.50

50

3

0.798

3

3

0.83

150

3

2.700

15

4

1.17

100

3

1.150

8

5

1.50

100

4

1.300

1

6

0.83

50

3

1.700

6

7

1.50

100

2

0.100

4

8

1.50

150

3

1.800

7

9

0.83

100

4

2.200

9

10

1.17

50

2

0.200

16

11

1.17

100

3

1.200

17

12

1.17

100

3

1.290

14

13

1.17

100

3

1.270

5

14

0.83

100

2

1.000

13

15

1.17

100

3

1.250

11

16

1.17

50

4

1.100

12

17

1.17

150

4

2.200

3 Results and discussion

3.1 Microstructure of metal matrix composite

The microstructures of A356/5%SiC metal matrix composites are exposed in Fig. 7. The microstructures point out the indication of minimum porosity in the A356/5%SiC metal matrix composites. The distribution of SiC in a matrix alloy is reasonably homogeneous. Further the microphotographs of A356/5%SiC composite exhibit a good bond between the matrix alloy (A356) and the SiC particles (see Fig. 8). Three major causes determine the properties and performance of metal matrix composites of A356/SiC: (i) properties of the constituent materials, (ii) the size, shape, quantity, and distribution of the reinforcement (SiC), and (iii) the effectiveness of the bond between matrix (A356 alloy) and reinforcement (SiC) in transferring stress across the interface.
Fig. 7

Microstructures of A356/SiC metal matrix composites

Fig. 8

Optical micrograph of A356/5% SiC indicating good bond between the matrix and reinforcements

3.2 Surface layer of A356/5%SiC composites

Figure 9 shows the SEM micrographs [27] of the surface layer of the A356/5%SiC metal matrix composites generated under turning with PCD (insert-10) tool and roller burnishing with WC roller. Cracks and pits are observed on the machined surfaces of A56/5%SiC composites under turning with PCD (insert-10) tool. Comparing Figs. 9a, b, it is found that the amounts of cracks and pits are significantly reduced and a better surface integrity is obtained after the roller burnishing with constant burnishing speed (1.5 m/s), constant burnishing force (50 N) and constant number of passes (2). After the roller burnishing process, reduced amounts of plastic deformation results in smaller amounts of cracks and pits (see Fig. 9b). It shows that average surface roughness of A356/5%SiC metal matrix composite under turning with PCD (insert-10) tool is 3.732 µm. While average surface roughness of A356/5%SiC metal matrix composite under roller burnishing is 1.232 µm (predicted). Reduced surface roughness of A356/5%SiC metal matrix composite under roller burnishing is 66.98%.
Fig. 9

SEM micrographs of the surfaces of A356/5%SiC metal matrix composites generated under conditions in a turning with PCD (insert-10) tool b roller burnishing with WC roller

Plastic deformation mechanisms of the surfaces of A356/5%SiC metal matrix composites before and after roller burnishing are shown in Figs. 10a–d, respectively. It can be seen from Figs. 10c, d that grain numbers within the sampling area increase (grain size reduces) after roller burnishing. This is an indication of the occurrence of grain refinements. This shows that the large grains with small size at the surface of A356/5%SiC composite give better surface finish. Turning with PCD (insert-10) tool leads to the generation of dislocations (see Figs. 10a, b). After the roller burnishing, dislocations are reduced.
Fig. 10

Plastic deformation mechanism of the surfaces of A356/5%SiC metal matrix composites at higher magnification: a, b turning with PCD (insert-10) tool, c, d roller burnishing with WC roller

3.3 Mechanical properties

For tensile and hardness testing, five samples of A356/5%SiC metal matrix composites have been prepared, as shown in Table 7. In this study the experimental result shows that the tensile strength of the samples A356/5% SiC metal matrix composites under turning with PCD (insert-10) tool is lower than roller burnishing with WC roller. Average tensile strength of metal matrix composites under turning with PCD (insert-10) tool is 300.24 MPa. While average tensile strength of metal matrix composite under roller burnishing is 305.80 MPa. Improved tensile strength under roller burnishing is 1.81%. Ductility is a solid material’s ability to deform under tensile stress, and it is often characterized by being stretched into a wire. Improved ductility of A356/5%SiC metal matrix composite under roller burnishing is 14.49% (see Table 7). It can be seen from Table 7 that average hardness of A356/5%SiC metal matrix composite under turning with PCD (insert-10) tool is 83.38 BHN. While average hardness of A356/5%SiC metal matrix composite under roller burnishing is 88.83 BHN. Improved hardness of metal matrix composite under roller burnishing is 6.13%.
Table 7

Observations of tensile strength, ductility and hardness of composites

Sample No.

Turning with PCD (insert-10) tool

Roller burnishing with WC roller

Tensile strength /MPa

Percentage elongation (ductility) /%

Hardness /BHN

Tensile strength /MPa

Percentage elongation (ductility) /%

Hardness /BHN

1

292.60

4.50

75.60

299.45

5.50

82.00

2

298.50

5.20

82.35

303.56

6.80

85.66

3

304.45

6.65

86.66

307.55

7.25

92.33

4

301.22

6.40

84.80

306.45

7.11

89.66

5

304.45

6.45

87.50

312.00

7.50

94.50

Average values

300.24

5.84

83.38

305.80

6.83

88.83

3.4 Analysis of surface roughness of A356/5%SiC roller burnished samples

The aim of the present investigation is to analyze the effects of burnishing speed (m/s), burnishing force (N) and number of passes of roller burnishing with WC roller on surface roughness of A356/5%SiC metal matrix composite. The selected experimental design is Box–Behnken design and the design matrix is shown in Table 6. The analysis of response was done using Design Expert Software. Analysis of variance (ANOVA) for surface roughness is shown in Table 8. F value is defined as the ratio of mean square model to mean square error, and the probability of F value greater than calculated F value is expressed by p value due to noise. If p value is less than 0.05, significance of corresponding term is found. Significant p value (p < 0.05) means that the testing sample data are a normal subset of the population data. For lack of fit p value must be greater than 0.05. An insignificant lack of fit is desirable as it implies anything left out of model is not significant and the model developed fits. Based on ANOVA test, the full quadratic model was found to be relevant for surface roughness of A356/5%SiC metal matrix composite under roller burnishing with WC roller with regression p value less than 0.05 and lack of fit greater than 0.05. From Table 8, terms burnishing speed, burnishing force, number of passes, square terms of burnishing speed, burnishing force, number of passes and interaction terms between burnishing force and number of passes are significant model terms. The regression equation can be expressed in Eqs. (3) and (4) in terms of coded factors and actual factors, respectively.
Table 8

ANOVA for surface roughness

Source

Sum of square

DF

Mean square

F value

p value Prob. >F

 

Model

7.63

9

0.85

93.90

< 0.0001

Significant

A (Burnishing speed)

1.62

1

1.62

179.68

< 0.0001

 

B(Burnishing force)

1.45

1

1.45

160.28

< 0.0001

 

C(Number of passes)

3.13

1

3.13

346.23

< 0.0001

 

AB

1.000 x 10−6

1

1.000 x 10−6

1.108 x 10−4

0.9919

 

AC

0.000

1

0.000

0.000

1.0000

 

BC

0.16

1

0.16

17.73

0.0040

 

A 2

0.47

1

0.47

51.96

0.0002

 

B 2

0.14

1

0.14

15.75

0.0054

 

C 2

0.73

1

0.73

80.63

< 0.0001

 

Residual

0.063

7

9.026 x 10−3

   

Lack of fit

0.050

3

0.017

5.21

0.0724

Not significant

Pure error

0.013

4

3.220 x 10−3

   

Cor total

7.69

16

    

Std. dev.

0.095

 

R-square

 

0.9918

 

Mean

1.28

 

Adj-R squared

 

0.9812

 

C.V./%

7.42

 

Pred R-squared

 

0.8927

 

Press

0.82

 

Adeq precision

 

34.993

 
$$ {\text{Surface}}\,{\text{roughness}} = 2.37 - 0.14A - .022B + 2.72C + 5.00 \times 10^{ - 7} \,AB - 7.70 \times 10^{ - 17} AC + 4.00 \times 10^{ - 3} BC + 8.34 \times 10^{ - 4} A^{2} + 7.35 \times 10^{ - 5} \,B^{2} - 0.42C^{2} $$
(6)

The determination coefficient (R2) was used to check the goodness of fit of the model. The coefficient of determination value (0.9918) was calculated for response. This indicates that 99.18% of experimental data certify the rapport with the data predicted by the model. The R2 value is always between 0 and 1, and its value illustrates correctness of the model. Coefficient of determination value (0.9918) should be close to 1.0 for a good statistical model. The adjusted R2 value regenerates the phrases with the significant terms. Adj R2 (0.9812) is also high to proponent for a high significance of the model. The Pred R2 (0.8927) suggests that the model could explain 95% of the changeability in anticipating new observations. Low value of coefficient of variation (7.42) expresses that deviations between experimental values and predicted values are low. Signal to noise ratio measures by Adeq precision. Adeq precision greater than 4 is desirable. In this study, Adeq precision value is 34.993, which reveals adequate signal.

Figure 11 displays the interaction between the predicted values and experimental values for surface roughness of A356/5%SiC metal matrix composite under roller burnishing with carbide rollers [28]. The points should be randomly dispersed along the 45° line. Majority of points below or above the line show areas of over or under prediction. The normal probabilities of residuals are presented in Fig. 12. After developing the regression model of surface roughness, the model adequacy investigating was achieved in order to authenticate that the underlying assumption of regression investigation was not disrupt. Figure 12 represents the normal probability plot of the residual which generates no sign of the offense since each point in the plot pursues a straight line pattern. The normal probability plot is used to verify the normality assumption.
Fig. 11

Correlation between the predicted and actual values

Fig. 12

The normal probability of residuals

Figure 13 displays the studentized residuals versus predicted values to investigate for constant error. Residuals versus predicted values should be distributed at irregular intervals. In a linear regression investigation it is expected that the scattering of residuals is in the population (total number of testing data). Here is a plot of the residuals versus predicted.
Fig. 13

Residuals versus predicted

Figure 14 displays the correlation between the residuals and experimental runs. Residuals versus runs should be random scatter and no trends.
Fig. 14

Residuals versus run

3.5 Analysis of desirability

3D graphs between desirability, burnishing speed, burnishing force and number of passes are shown in Fig. 15. The basic idea of the desirability function approach is to transform a multiple response problem into a single response problem by means of mathematical transformations. Desirabilities range from 0 to 1 for given response. The program combines the individual desirabilities into a single number and then searches for the greatest overall desirability. Value 1 represents the ideal case. Value 0 indicates that one or more responses fall outside desirable limits. RSM (Box-Behnken design) and desirability function analysis have been demonstrated to be efficient to optimize burnishing process parameters (burnishing speed, burnishing force and number of passes) for surface roughness of A356/5%SiC under roller burnishing with WC roller. Single response optimization determines how input parameters affect desirability of individual response. The numerical optimization finds a point that maximizes the desirability function.
Fig. 15

3D relation between desirability, burnishing speed, burnishing force and number of passes with desirability one. a desirability = 1.00, number of passes = 2.05, b desirability = 1.00, burnishing force = 61.30 N, c desirability = 1.00, burnishing speed = 1.28 m/s

From the ramp function graph, it can be observed that when burnishing speed, burnishing force and number of passes are 1.28 m/s, 61.30 N and 2.06, respectively, then the optimum value of surface roughness is 0.086 µm. Ramp function graph for roller burnishing process parameters for minimum surface roughness is given in Fig. 16. It exposes that what will be the values of parameters to obtain minimum surface roughness (0.086 µm) for different roller burnishing process parameters with desirability 1.
Fig. 16

Ramp function graph for minimum surface roughness with desirability 1

3.6 Effect of rolling burnishing process parameters on surface roughness

Burnishing is a superficial plastic deformation process used as a surface smoothing and surface enhancement finishing treatment after some machining processes to generate a compact and wear-resistant surface for longer and efficient component life [29]. In this study, the surface roughness of A356/5%SiC metal matrix composite under roller burnishing with WC oller was established, in which roller burnishing speed, roller burnishing force and numbers of passes are taken into consideration. The mathematical models, in terms of roller burnishing process parameters, were developed for surface roughness prediction using RSM on the basis of experimental results. The significance of these parameters on surface roughness of A356/5%SiC had been established by ANOVA.

3.6.1 Effect of burnishing speed on surface roughness

The outcomes of the roller burnishing speed with respect to surface roughness are shown in Figs. 17 and 18, respectively. It can be noticed that surface roughness decreases with the increase in roller burnishing speed. There are variations in the surface roughness, when the roller burnishing speed varies. Higher roller burnishing speed (1.5 m/s) increases the surface temperature of workpiece. Metallic bond of metal matrix composite materials becomes soft due to increased surface temperature of workpiece, and resistance offered by metal matrix composite material against roller burnishing tool becomes low.

3.6.2 Effect of burnishing force on surface roughness

The effect of variation in burnishing force (from 50 N to 150 N) on the surface roughness of roller burnished A356/5%SiC metal matrix composite is evaluated, as shown in Figs. 17 and 19. The low surface roughness values of roller burnished A356/5%SiC composites are observed at lower burnishing force (50 N). It was observed that an opposite effect was seen while increasing the burnishing force as compared to burnishing speed. By increasing the burnishing force, the surface roughness is increased. The increase in burnishing force increases friction between roller burnishing and A356/5%SiC composite. Due to higher friction, higher surface roughness is observed.
Fig. 17

Interaction effect of surface roughness, burnishing speed and burnishing force a 3D interaction b the contour plot

3.6.3 Effect of number of passes on surface roughness

The surface roughness, roller burnishing speed, roller burnishing force and number of passes are plotted in Figs. 18 and 19 for variable roller burnishing process parameters. It is observed that surface roughness at lower number of passes (2) is lesser, whereas at higher number of passes (4) is higher. It means that, with an increase number of passes, the surface roughness increases. It can be described as the increase in the number of passes value from 2 to 4, friction between WC roller and silicon carbide particles (SiCp) of A356/5%SiC composite during roller burnishing increases. This increased friction between roller and composite material produces rough surface of carbide rollers, and increases the value of surface roughness of A356/5%SiC metal matrix composites.
Fig. 18

Interaction effect of surface roughness, burnishing speed and number of passes a 3D interaction b the contour plot

Fig. 19

Interaction effect of surface roughness, burnishing force and number of passes a 3D interaction b the contour plot

3.7 Confirmation experiment

By evaluating the surface roughness of A356/5%SiC metal matrix composites under roller burnishing with WC roller, the average feasible predicted surface roughness is found to be 1.232 µm, as exhibited in Table 9. Importance of process parameters can be ranked from their F values which are indicated in Table 8. From Table 8, it can be concluded that number of passes of WC roller is contributing more and it is followed by roller burnishing speed and roller burnishing force. The experimental surface roughness (average of three test samples) corresponding to these parameters (burnishing speed of 1.5 m/s, burnishing force of 50 N and number of passes of 2) is found to be 1.17 µm. This shows that there is approximately 5.032% error between the experimental and modeled results. Hence, the developed model can be effectively used in the process parameter range to predict the surface roughness.
Table 9

Confirmation result

Response

Surface roughness

Prediction /µm

1.232

SD

0.095

SE (n = 1)

0.104

95% PI low

0.9859

95%PI high

1.4781

4 Conclusions

The following conclusions can be drawn from above analysis:
  1. (i)

    SEM micrographs of the surfaces of A356/5%SiC metal matrix composites generated under conditions in roller burnishing with WC roller show much smooth surface as compared to surface generated under condition in turning with PCD (insert-10) tool. Average surface roughness of machined A356/5%SiC composites with PCD (insert-10) tool is observed 3.732 µm, while the average surface roughness of roller burnished samples with WC roller is observed 1.232 µm (predicted). Reduced surface roughness of A356/5%SiC metal matrix composite under roller burnishing is 66.98%. Average tensile strength of machined A356/5%SiC composite with PCD (insert-10) tool is 300.2 MPa. While after the roller burnishing with WC rollers, it is 305.80 MPa. Tensile strength has improved by 1.81%. The average value of percentage elongation (ductility) of machined A356/5%SiC composites with PCD (insert-10) tool is 5.84. On the other hand average percentage of elongation of composite under roller burnishing was found to be 6.83. Improved ductility of A356/5%SiC metal matrix composite under roller burnishing is found 14.49%. From the results, average hardness of machined A356/5%SiC composite with PCD (insert-10) tool is 83.38 BHN, after the roller burnishing with WC roller 6.13% hardness improves. Within the chosen roller burnishing process parameters range, higher roller burnishing speed (1.5 m/s), lower roller burnishing force (50 N), and lower number of passes (2) are preferred for good surface finish of A356/5%SiC metal matrix composite under roller burnishing with WC roller.

     
  2. (ii)

    Within the roller burnishing process parameters range, surface roughness of A356/5%SiC decreases. By increasing the roller burnishing speed while increasing the roller burnishing force and number of passes from minimum to maximum limits, the surface roughness of A356/5%SiC composite increases. Based on ANOVA, roller burnishing speed, roller burnishing force, and number of passes are found to be suitable for surface roughness with regression p-value less than 0.05 and lack of fit more than 0.05. Within the roller burnishing process parameters range, it is found that the parameters which affect the surface roughness in descending order are as follows: number of passes, roller burnishing speed and roller burnishing force. The minimum value of surface roughness with desirability 1 is obtained to be 0.086 µm at roller burnishing speed of 1.28 m/s, burnishing force of 61.30 N and number of passes of 2.06. An empirical relationship has been developed to predict the surface roughness incorporating roller burnishing process parameters at 95% confidence level. The predicted value for surface roughness is found 1.232 µm. There is only 5.032% error in the experimental and modeled results.

     

References

  1. 1.
    Yan BH, Wang CC, Chow HM et al (2000) Feasibility study of rotary electrical discharge machining with ball burnishing for Al2O3/6061Al composite. Int J Machine Tools Manuf 40(10):1403–1421CrossRefGoogle Scholar
  2. 2.
    Gharbi F, Sghaier S, Hamdi H et al (2012) Ductility improvement of Al 1050A rolled sheet by a newly designed ball burnishing tool device. Int J Adv Manuf Technol 60:87–99CrossRefGoogle Scholar
  3. 3.
    Lopez de Lacalle LN, Lamikiz A, Sanchez JA et al (2007) The effect of ball burnishing on heat-treated steel and Inconel 718 milled surfaces. Int J Adv Manuf Technol 32:958–968CrossRefGoogle Scholar
  4. 4.
    El-Axir MH (2000) An investigation into roller burnishing. Int J Machine Tools Manuf 40(11):1603–1617CrossRefGoogle Scholar
  5. 5.
    El-Khabeery MM, El-Axir MH (2001) Experimental techniques for studying the effects of milling roller-burnishing parameters on surface integrity. Int J Machine Tools Manuf 41(12):1705–1719CrossRefGoogle Scholar
  6. 6.
    Luo H, Liu J, Wang L, Zhong Q (2005) Investigation of the burnishing process with PCD tool on non-ferrous metals. Int J Adv Manuf Technol 25:454–459CrossRefGoogle Scholar
  7. 7.
    Luo H, Liu J, Wang L, Wang Q (2006) The effect of burnishing parameters on burnishing force and surface microhardness. Int J Adv Manuf Technol 28:707–713CrossRefGoogle Scholar
  8. 8.
    Luoa H, Liu J, Wang L, Zhong Q (2006) Study of the mechanism of the burnishing process with cylindrical polycrystalline diamond tools. J Mater Process Technol 180:9–16CrossRefGoogle Scholar
  9. 9.
    Yeldose BC, Ramamoorthy B (2008) An investigation into the high performance of TiN-coated rollers in burnishing process. J Mater Process Technol 207:350–355CrossRefGoogle Scholar
  10. 10.
    El-Taweel TA, El-Axir MH (2009) Analysis and optimization of the ball burnishing process through the Taguchi technique. Int J Adv Manuf Technol 41:301–310CrossRefGoogle Scholar
  11. 11.
    Klocke F, Backer V, Wegner H et al (2009) Influence of process and geometry parameters on the surface layer state after roller burnishing of IN718. Prod Eng Res Dev 3:391–399CrossRefGoogle Scholar
  12. 12.
    Franzen V, Trompeter M, Brosius A et al (2010) Finishing of thermally sprayed tool coatings for sheet metal forming operations by roller burnishing. Int J Mater Form 3(1):147–150CrossRefGoogle Scholar
  13. 13.
    Aysun S (2011) Analysis and optimization of surface roughness in the ball burnishing process using response surface methodology and desirabilty function. Adv Eng Softw 42:992–998CrossRefGoogle Scholar
  14. 14.
    Korzynski M, Lubas J, Swirad S et al (2011) Surface layer characteristics due to slide diamond burnishing with a cylindrical-ended tool. J Mater Process Technol 211:84–94CrossRefGoogle Scholar
  15. 15.
    Swirad S (2011) The surface texture analysis after sliding burnishing with cylindrical elements. Wear 271:576–581CrossRefGoogle Scholar
  16. 16.
    Tadic B, Todorovic PM, Luzanin O et al (2013) Using specially designed high-stiffness burnishing tool to achieve high-quality surface finish. Int J Adv Manuf Technol 67:601–611CrossRefGoogle Scholar
  17. 17.
    Balland P, Tabourot L, Degre F et al (2013) An investigation of the mechanics of roller burnishing through finite element simulation and experiments. Int J Machine Tools Manuf 65:29–36CrossRefGoogle Scholar
  18. 18.
    Balland P, Tabourot L, Degre F et al (2013) Mechanics of the burnishing process. Precis Eng 37:129–134CrossRefGoogle Scholar
  19. 19.
    Dwivedi SP, Kumar S, Kumar A (2012) Effect of turning parameters on surface roughness of A356/5% SiC composite produced by electromagnetic stir casting. J Mech Sci Technol 26(12):3973–3979CrossRefGoogle Scholar
  20. 20.
    Muralidharan R, Ramana GR (2013) Thermal plasma synthesis of SiC. Adv Manuf 1:50–61CrossRefGoogle Scholar
  21. 21.
    Rao TB, Gopala Krishna A (2013) Simultaneous optimization of multiple performance characteristics in WEDM for machining ZC63/SiCp MMC. Adv Manuf 1:265–275CrossRefGoogle Scholar
  22. 22.
    El-Tayeb NSM, Low KO, Brevern PV (2007) Influence of roller burnishing contact width and burnishing orientation on surface quality and tribological behaviour of Aluminium 6061. J Mater Process Technol 186:272–278CrossRefGoogle Scholar
  23. 23.
    Dwivedi SP, Sharma S, Mishra K (2014) Microstructure and mechanical behavior of A356/SiC/Fly-ash hybrid composites produced by electromagnetic stir casting. J Braz Soc Mech Sci Eng 1–11Google Scholar
  24. 24.
    Wang G, Rong YM (2013) Advances of physics-based precision modeling and simulation for manufacturing processes. Adv Manuf 1:75–81CrossRefGoogle Scholar
  25. 25.
    Chen SL, Cao WS, Zhang F et al (2013) Development of a computational tool for materials design. Adv Manuf 1:123–129CrossRefGoogle Scholar
  26. 26.
    Kosaraju S, Anne VG (2013) Optimal machining conditions for turning Ti–6Al–4V using response surface methodology. Adv Manuf 1:329–339CrossRefGoogle Scholar
  27. 27.
    Xu Y, Gao F, Zhang B et al (2013) Technology of self-repairing and reinforcement of metal worn surface. Adv Manuf 1:102–105CrossRefGoogle Scholar
  28. 28.
    Lu WC, Ji XB, Li MJ et al (2013) Using support vector machine for materials design. Adv Manuf 1:151–159CrossRefGoogle Scholar
  29. 29.
    Li FL, Xia W, Zhou ZY et al (2013) Analytical prediction and experimental verification of surface roughness during the burnishing process. Int J Machine Tools Manuf 62:67–75CrossRefGoogle Scholar

Copyright information

© Shanghai University and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Shashi Prakash Dwivedi
    • 1
    Email author
  • Satpal Sharma
    • 1
  • Raghvendra Kumar Mishra
    • 1
  1. 1.Gautam Buddha UniversityGautam Budh NagarIndia

Personalised recommendations