Effect of Silicon Carbide Content on Tribological Properties of Aluminium Zinc Alloy Composite

Effect of Silicon Carbide Content on Tribological Properties of Aluminium Zinc Alloy Composite

S. B. Chikalthankar1, V. M. Nandedkar2 and D. P. Sonawane3

1&3 Department of Mechanical Engineering, Government college of Engineering, Aurangabad (M.S.), India.

2 Department of Production Engineering, SGGSI&T, Nanded (M.S.), India.

Abstract- Aluminium Zinc alloys are occupying attention of both researches and industries as a promising material for tribological applications. These are light weight having good malleability, formability, high corrosion resistance and high electrical and thermal conductivity. The present work is an attempt to s t u d y d r y sliding wear of AZ alloy and wear parameters. The pin-on-disc test machine is used to study effect of wear parameters like applied load, rpm, sliding distance and percentage of Silicon carbide on the dry sliding wear of the alloy have been investigated. Techniques of Taguchi were used to conduct the experiment for acquiring the data in controlled way. An orthogonal array and the analysis of variance were employed to investigate the influence of process parameters on the wear of alloy. The objective of experimentation is to establish a correlation between dry sliding wear of alloy and wear parameters. These correlations were obtained by regressions and confirmation tests. These tests were conducted to verify the experimental results from the mentioned correlations.

Key words-AZ alloy; wear; orthogonal array; Analysis of variance; Taguchi method

1. INTRODUCTION

   Aluminium and Zinc alloys have huge industrial applications because of their physical, mechanical and tribological properties such as high wear and seizure resistance, high stiffness, high strength, controlled thermal expansion coefficient and damping capacity. All these properties can be obtained through alloying elements, cold working and heat treatment. These alloying elements are selected based on their effects and suitability. The Zn-Al alloy have been tested for the tribological performance and applied in various engineering applications. These alloys were found superior than traditional bearing materials like bronze, steel, plastics, cast iron etc. These alloys possess low density, low coefficient of friction, low cost. At high stress conditions, this alloy has limited applications due to its lower creep resistance, as compared to traditional aluminium alloys and other structural materials, especially at temperatures above 1000C. Due to this reason there is a major loss of market potential for this alloy; otherwise it is an excellent material. Dimensional instability was another problem, which is caused by the presence of metastable phases. It was found that, ZA alloys have the typical dendrite

   structure in the real casting conditions, wherein the dendrite size and interdendritic spacing depend on the casting parameters. We can overcome these problems to a great extent by replacing zinc with aluminium.

   The alloying elements may be classified as major and minor elements, microstructure modifiers or impurities; however the impurity elements in some alloys could be major elements in others. Also it has been shown that this problem could be reduced through alloying with different elements such as Cu, Si, SiC, Ni, Mn, and Mg etc [3]. SiC is most effective alloy addition towards improving mechanical and tribological properties of AZ alloys. However, the effects of SiC content on wear properties of these alloys have not been fully studied. On this background the aim of this experimentation work is to investigate the effect of SiC on the wear properties of AZ alloys and to decide the optimum SiC content for above properties.

2. TAGUCHI TECHNIQUE

   The Taguchi method, which is eective to deal with responses, was inuenced by multi-variables. This method drastically reduces the number of experiments that are required to model the response function compared with the full factorial design of experiments. The major advantage of this technique is to nd out the possible interaction between the parameters. The Taguchi technique is devised for process optimization and identication of optimal combination of the factors for a given response. This technique is divided into three main phases, which encompasses all experimentation approaches. The three phases are (1) the planning phase (2) the conducting phase and (3) the analysis phase. Planning phase is the most important phase of the experiment. This technique creates a standard orthogonal array to accommodate the eect of several factors on the target value and denes the plan of experiments. The experimental results are analyzed using analysis of means and variance to study the inuence of factors [7].

3. EXPERIMENTAL PROCEDURE

   1. Preparation of Alloy

      The alloys were prepared from commercially pure aluminum (99.7%), high purity zinc (99.9%) and electrolytic copper (99.9%). The liquid metallurgy technique was used to prepare composite specimens, because it is most economical to fabricate composites. In this process, matrix alloy was rstly superheated over its

      melting temperature and then temperature was lowered gradually below the liquidus temperature to keep the matrix alloy in the semi-solid state at this temperature, the preheated SiC particles were introduced into the slurry and mixed. The composite slurry temperature was increased to fully liquid state and automatic stirring was continued for 5 min at an average stirring speed of 300350 r/min. Alloys were melted in an electrical furnace and poured at a temperature of 7500C in to a steel mould at room temperature. The melt was then superheated above liquidus temperature and nally poured into the cast iron permanent mould of 15 mm in diameter and 150 mm in height [4, 7].

      Table 1 Process with their v a l u e s at three level parameters

      Level	rpm	Load (Kg)	Sliding distance (Km)	SiC (wt. %)
      -1	200	2	2	6
      0	400	4	4	9
      1	600	6	6	12

   2. SEM microstructure and XRD test

      In order to get micro structural details of the prepared samples, they were taken for SEM & XRD tests. The images of the microstructures of these samples up to a resolution of 1500X were obtained and different phases were identified as shown in the fig 1, 2, 3. Also XRD GRAPHS of these sample show presence of these phases at various peaks as shown in fig 4, 5, 6.

      Silicon carbide particle

      Fig. 1: SEM photograph of Al 25% Zn-2.5% Cu- 6 % SiC

      Silicon carbide particle

      Fig. 2: SEM photograph of Al 25% Zn-2.5% Cu- 9 % SiC

      Silicon carbide particle

      Fig. 3: SEM photograph of Al 25% Zn-2.5% Cu- 12 % SiC

      Counts

      Silicon Carbide; Aluminum Copper; Danbaite

      1

      400

      Aluminum Copper

      Aluminum Copper Zinc; Aluminum Copper

      300

      Silicon Carbide; Aluminum Copper

      200

      Copper Zinc

      Danbaite

      Aluminum, syn [NR]

      Aluminum, syn [NR]

      100

      0

      10 20 30 40 50 60 70 80 90 100 110

      Position [°2Theta]

      Fig. 4: XRD of Al – 25 Zn-2.5 Cu- 6 % SiC

      The above fig show X-ray diffraction results of sample A in which SiC peaks were seen at 34 and 38.And other peaks of phases Al, Cu, Z and their mixtures are also as shown in the figure.

      Counts

      Aluminum, syn [NR]; Aluminum Silicon; Silicon Carbide

      2

      800

      600

      Aluminum, syn [NR]; Aluminum Silicon; Silicon Carbide

      Aluminum, syn [NR]; Aluminum Silicon

      Aluminum Silicon; Copper Silicide; Zinc, syn

      Copper Silicide; Silicon Carbide; Zinc, syn

      400

      Copper Silicide; Zinc, syn

      Silicon Carbide

      Aluminum, syn [NR]

      200

      0

      10 20 30 40 50 60 70 80 90 100 110

      Position [°2Theta]

      Fig. 5: XRD of Al – 25 Zn-2.5 Cu- 9 % SiC

      The above fig show X-ray diffraction results of sample B in which SiC peaks were seen at 38, 65, 86 and 101 .And other peaks of phases Al, Cu, Zn and their mixtures are also as shown in the figure.

      Copper Zinc; Silicon Carbide; Aluminum Copper; Copper Zinc

      Counts

      3

      Copper Zinc

      400

      Aluminum Copper

      Alubmriansusm; ACluompipneurm; CCooppppeerrZinc; Copper Zinc

      Aluminum Copper; Copper Zinc; Silicon Carbide

      200

      0

      10 20 30 40 50 60 70 80 90 100 110

      Position [°2Theta]

      Fig 6: XRD of Al – 25 Zn-2.5 Cu- 12 % SiC

      The above fig show X-ray diffraction results of sample C in which SiC peaks were seen at 38 and 78 .And other peaks of phases Al, Cu, Zn and their mixtures are also as shown in the figure.

   3. Wear test experimental setup and procedure

      A single pin type pin-on-disc test apparatus was used to carry out dry sliding wear characteristics of the composite as per ASTM G99-95 standards. The tests were carried out at the room temperature under dry operating conditions. Wear specimen (pin) of size 10 mm diameter and 25 mm length was cut from as cast samples machined and then polished metallographically. A single pan electronic weighing machine with least count of 0.0001 g was used to measure the initial weight of the specimen. The cylindrical pin flat ended specimens of size 10 mm diameter and 25 mm length were tested against EN31 steel disc by applying the load. After running through a xed sliding distance, the specimens were removed, cleaned with acetone, dried and weighed to determine the weight loss due to wear. The dierence in the weight measured before and after test gave the sliding wear of the composite specimen and then the weight loss was calculated [7]. The sliding wear of the composite was studied as a function of the weight percentage of the sic composite, sliding distance, the applied load and the rpm.

   4. Plan of experiments

   Standard orthogonal array was used to conduct the experiments. The selection of the orthogonal array was based on the condition that the degrees of freedom for the orthogonal array should be greater than or equal to sum of those wear parameters. An L27 orthogonal array was chosen for the present experiments, which has 27 rows and 6 columns as shown in Table 3. The wear parameters chosen for the experiments were (1) rpm, (2) load, (3) sliding distance, (4) weight percentage of SiC . Table 3 indicates the factors and their level. The experiment consists of 27 tests (each row in the L27 orthogonal array) and the columns were assigned with parameters. The first column

   in table 3 was assigned to rpm; second column was assigned to load; Third column was assigned to sliding distance and fourth column was assigned to weight percentage of SiC and remaining columns were assigned to their interactions. For lower is the better performance objective, the response to be studied. ANOVA is performed to determine significant parameter and at last confirmation test is conducted to verify the optimal process parameter.

4. RESULTS AND DISCUSSION

   The tests were conducted with the aim of relating the influence of rpm, Load, Sliding distance and percentage of Sic with dry sliding wear of the composite. On conducting the experiments as per orthogonal array, the wear results for various combinations of parameters were obtained and are shown in Table 3. ANOVA results are shown in the Table 2.

   1. Analysis of variance

      The adequacy of the models is tested using the analysis of variance (ANOVA) technique. It is a statistical tool for testing null hypothesis for designed experimentation, where a number of different variables are

      being studied simultaneously. ANOVA issued to quickly analyze the variances present in the experiment with the help of fisher test (F test). This analysis was carried out for a level of significance of 5%, i.e. the level of confidence 95%. Table 2 shows the result of ANOVA analysis. One can observe from the ANOVA analysis that the value of P is less than 0.05 in all three parametric sources. Therefore it is clear that 1) rpm, (2) Load, (3) Sliding distance; (4)

      Weight Percentage of SiC has the influence on the wear of the composite. The last column in Table 4 shows the percentage contribution of each factor on total variation indicating their degree of influence on the result. One can observe from the ANOVA table that the Load (46.52%), Sliding distance (40.27%), rpm (4.76%) and SiC

      composition (2.63%) has great influence on the wear. The SiC composition is influencing comparatively less (2.63%), which indicates that there is no appreciable increase in wear by increasing the SiC content from 6 to 12 weight percent.

      Table 2: show ANOVA Results

      Source	DF	Seq SS	Adj MS	F	P	% Contribution	Significant/ Not Significant
      RPM	2	0.0259	0.01295	7.4	0.005	4.7652	Significant
      LOAD	2	0.252865	0.126433	72.2	0	46.523	Significant
      SLIDING DISTANCE	2	0.218891	0.109446	62.5	0	40.273	Significant
      SIC COMPOSITION	2	0.014341	0.00717	4.09	0.034	2.6385	Significant
      Error	18	0.031518	0.001751
      Total	26	0.543516

      Table 3: Show Orthogonal array of Taguchi for wear

      Run	rpm	Load(kg)	Sliding distance (km)	Composition %	Wear in g	SN Ratio
      1	200	2	2	6	0.097130	20.2529
      2	200	4	4	6	0.280170	11.0516
      3	200	6	6	6	0.602125	4.4063
      4	400	2	4	6	0.225070	12.9536
      5	400	4	6	6	0.506685	5.9052
      6	400	6	2	6	0.339425	9.3851
      7	600	2	6	6	0.434615	7.2379
      8	600	4	2	6	0.210205	13.5471
      9	600	6	4	6	0.551870	5.1633
      10	200	2	2	9	0.096575	20.3027
      11	200	4	4	9	0.253050	11.9359
      12	200	6	6	9	0.493750	6.1299
      13	400	2	4	9	0.181580	14.8186
      14	400	4	6	9	0.384080	8.3116
      15	400	6	2	9	0.326440	9.7239
      16	600	2	6	9	0.291120	10.7186
      17	600	4	2	9	0.267870	11.4415
      18	600	6	4	9	0.483175	6.3179
      19	200	2	2	12	0.074030	22.6118
      20	200	4	4	12	0.334410	9.5144
      21	200	6	6	12	0.459430	6.7556
      22	400	2	4	12	0.198555	14.0424
      23	400	4	6	12	0.392490	8.1234
      24	400	6	2	12	0.283050	10.9627
      25	600	2	6	12	0.342275	9.3125
      26	600	4	2	12	0.231735	12.7002
      27	600	6	4	12	0.528660	5.5362

      Main Effects Plot (data means) for SN ratios

      RPM LOAD

      14

      12

      10

      8

      200

      400

      SD

      600

      2

      4

      COMP

      6

      2

      4

      6

      6

      9

      12

      Signal-to-noise: Smaller is better

      14

      12

      10

      8

      Mean of Means

      Mean of SN ratios

      Figure 7.1

      Main Effects Plot (data means) for Means

      RPM LOAD

      0.4

      0.3

      0.2

      200

      400

      SD

      600

      2

      4

      COMP

      6

      2

      4

      6

      6

      9

      12

      0.4

      0.3

      0.2

      Figure 7.2

      The graph shows the Main Effect plot for S/N ratio. The level for a factor with the highest S/N ratio was the optimum level for response measured. From the plot, it is observed that the minimum wear was at the higher S/N values in the response graph. The optimal wear parameters were 200 rpm (level 1), 2 Kg Load (level 1), 2 Km Sliding distance (level 1) and 12 % of Sic content (level 3). From S/N ratio graph, it is observed that for dry sliding wear, the Load and Sliding distance has the greatest influence on the wear. Figures 7.1 and 7.2 show graphically the effect of control factors on wear. Process parameter settings with highest ratio always give the optimum quality with minimum variance. The graph show the change of ratio when setting of the control factor was changed from one level to another.

   2. Multiple L i n e a r Regression Models

      To establish the correlation between the wear parameters (1) rpm, (2) Load, (3), Sliding distance, (4) weight percentage of SiC and the dry sliding wear loss the wear multiple linear regression model was obtained using statistical software MINITAB R14.The terms that are statistically significant are included in the model. Final Equation obtained is as follows ,

      WEAR = – 0.133 + 0.000181 RPM + 0.0591 LOAD + 0.0550 SD – 0.00746 COMP ..(1)

      Substituting the recorded values of the variables for the above equation (1) the sliding wear of the material can be calculated. The positive value of the coefficient suggests that the sliding wear o f m a t e r i a l increases with their associated variables. Whereas the negative value of the coefficient suggest that the sliding wear of the material will decreases with the increase in associated variables. The magnitude of the variables indicates the weightage of each of these factors .It is observed from the Equation (1) that the Load has the more effect on wear of the composites, which is followed by Sliding distance, Weight percentage of SiC and rpm for the tested range of variables. The important factor affecting the sliding wear is the load and coefficient associated with it is positive (0.0591). This suggests that the Load increases the penetration ability of the fractured particles, will increase and remove the material on the pin surface. The coefficient of Sliding distance is positive (0.0550) which indicates that increase in wear weight loss with increasing the Sliding distance. The coefficient of SiC content is negative (- 0.00746) which indicates that Sliding wear of composite decreases with increasing Sic content. The coefficient of rpm is positive (0.000181) which indicates that Sliding wear increases with increasing rpm for the tested range.

5. CONFIRMATION TEST

   To test the efficiency of the model the confirmation tests were performed by selecting the set of parameters as shown in Table 4. Table 5 shows the comparison of wear results from the mathematical model developed in the present work

   (Eq.(1)), with values obtained experimentally. It can be observed from table 5 that the calculated error varies from 4% to 8% for wear. Therefore the multiple regression equation derived above correlate the evaluation of wear in the alloy with the degree of approximation.

   Table 4: Show Parameters used in the confirmation wear test

   Test	Rpm	Load (Kg)	Sliding Distance (km)	SIC %
   1	600	4	4	6
   2	400	6	6	9
   3	600	2	2	12

   Table 5: Show Confirmation test results

   Test	Estimated Wear	Experimental Wear	Error %
   1	0.38724	0.36986	4.6990
   2	0.55686	0.60234	7.5505
   3	0.11428	0.12120	5.7095

6. CONCLUSION

Load is the wear factor that has the highest physical properties as well as statistical influence on the dry sliding wear of the composites (46.52%), the sliding distance (40.27%), rpm (4.76%) and SiC composition (2.63%) and for dry sliding wear of Aluminium zinc alloy metal matrix composites, the sliding distance (40.27%) has moderate influence on the wear. The rpm (4.76%) and weight percentage of SiC (2.63%) are the wear factor tat has least influence on dry sliding wear of the composites.

The highest wear resistance was obtained with 12% SiC composition. Wear resistance of tested alloy increased with increasing SiC content. Hardness of alloy increased with SiC content and 12% of SiC is the harder material than 9 and 6% of SiC. The confirmation tests shows that the error associated with dry sliding wear of the composite varies from 4% to 8%. The SEM & XRD test shows the presence of SiC content in all three samples.

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