V.Uma sankar, R.Ramanujam, S.Karthikeyan , M. Anthony Xavior
Keywords: Powder metallurgy; Metal matrix composites; Corrosion; Multi-response optimization; ANOVA; Grey relational analysis.
Abstract:
The present investigation focuses on finding the optimal powder metallurgical process parameters to prepare corrosion resistant AA6061–SiCp metal matrix composite. AA6061 alloy powder was homogenously mixed with various weight percentages of SiCp (5–15 wt %) and compacted at a pressure ranging from 350 to 550 MPa. The green compacts were sintered at temperatures between 400°C and 600°C with sintering time ranging from 1 to 3 hours. Taguchi’s L27 orthogonal array of experimental design was used. The effect of processing parameters such as reinforcement percentage, compacting pressure, sintering temperature and sintering time on the performance characteristics of sintered density and micro hardness were studied. Optimal levels of parameters were identified using grey relational analysis, and significant parameter was determined by analysis of variance. Experimental results indicate that multi-response characteristics such as density and micro hardness can be improved effectively through this approach.
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ISSN 1466-8858 Volume 16, Preprint 43 submitted 27 June 2013 A precursor experimental study on corrosion resistant AA6061-SiCp MMC prepared through PM process using ANOVA and Grey relational analysis - A novel approach V.Uma sankara*, R.Ramanujama, S.Karthikeyanb* , M. Anthony Xaviora a, School of Mechanical and Building Sciences, VIT University, Vellore 632 014, India b Centre for Nano biotechnology ,VIT University, Vellore 632014,India Corresponding author (skarthikeyanphd@yahoo.co.in) Abstract The present investigation focuses on finding the optimal powder metallurgical process parameters to prepare corrosion resistant AA6061–SiCp metal matrix composite. AA6061 alloy powder was homogenously mixed with various weight percentages of SiCp (5–15 wt %) and compacted at a pressure ranging from 350 to 550 MPa. The green compacts were sintered at temperatures between 400°C and 600°C with sintering time ranging from 1 to 3 hours. Taguchi’s L27 orthogonal array of experimental design was used. The effect of processing parameters such as reinforcement percentage, compacting pressure, sintering temperature and sintering time on the performance characteristics of sintered density and micro hardness were studied. Optimal levels of parameters were identified using grey relational analysis, and significant parameter was determined by analysis of variance. Experimental results 1 © 2013 University of Manchester and the authors. This is a preprint of a paper that has been submitted for publication in the Journal of Corrosion Science and Engineering. It will be reviewed and, subject to the reviewers’ comments, be published online at http://www.jcse.org in due course. Until such time as it has been fully published it should not normally be referenced in published work. ISSN 1466-8858 Volume 16, Preprint 43 submitted 27 June 2013 indicate that multi-response characteristics such as density and micro hardness can be improved effectively through this approach. Keywords: Powder metallurgy; Metal matrix composites; Corrosion; Multi-response optimization; ANOVA; Grey relational analysis. 1. Introduction Metal matrix composites (MMCs) have progressed as a grandiose material, which are widely used in current decade. Metal matrix composites have become necessary in various engineering applications, such as aerospace, marine, automobile, and applications because of their low density, specific strength and stiffness [1,2]. Nikhilesh Chawla and his co worker [3] investigated particulate reinforced aluminium metal matrix composite is one of the important composites among the metal matrix composites due to their low cost when compared to long fibre reinforced MMCs and due to their better properties than those of monolithic alloys Though lot of research and development has taken place in the filed of metal matrix composites by liquid processing the growth in composite manufacturing by powder metallurgy processing has not grown to that extent. In powder metallurgy technique, the reinforcements have been homogeneously mixed in the matrix. Functional performance and mass production was enhanced on account of homogeneity as reported by J.M.Torralbo et al 2 © 2013 University of Manchester and the authors. This is a preprint of a paper that has been submitted for publication in the Journal of Corrosion Science and Engineering. It will be reviewed and, subject to the reviewers’ comments, be published online at http://www.jcse.org in due course. Until such time as it has been fully published it should not normally be referenced in published work. ISSN 1466-8858 Volume 16, Preprint 43 submitted 27 June 2013 [4]. have studied the sintering process for aluminium-based composite in nitrogen atmosphere However, vacuum atmosphere was also used for sintering because sintering in air leads to oxidation, which reduces the strength of the composite [5, 6]. Powder processing of Aluminium matrix composites has the advantage over other processes due to its low tool consumption as reported by S.Muller et al [7]. It has already established that PM process is an effective from the point cost and power it is widely used to manufacture intricate mass production parts. The current study aims to use grey relational analysis for finding the optimum levels of parameters like reinforcement percentage, compacting pressure, sintering temperature and sintering time for maximum sintered density and micro hardness as first time in powder metallurgy processing of Al-SiCp MMC. These two properties are essential for understating from the point of enhancement of strength and wear resistance of MMCs and facilitate applications for a wide spectrum of industries. 2. Experimental details 2.1. Materials In this investigation a gas atomized Aluminium alloy powder as per AA6061 (average size of 35 microns) and SiC particles (average size of 35 microns) were used for production of Al-SiCp metal matrix composites (MMCs). The powders were mixed to achieve uniform distribution and then weighed with reinforcement percentage by weight of 5%, 10% and 15% of matrix alloy powder. The powder mixture was 3 © 2013 University of Manchester and the authors. This is a preprint of a paper that has been submitted for publication in the Journal of Corrosion Science and Engineering. It will be reviewed and, subject to the reviewers’ comments, be published online at http://www.jcse.org in due course. Until such time as it has been fully published it should not normally be referenced in published work. ISSN 1466-8858 Volume 16, Preprint 43 submitted 27 June 2013 mechanically alloyed in a ball mill in 15 hrs and with ball to powder ratio of 1:10. The mixed powders were compacted in a universal testing machine of 60T capacity uniaxially with hardened steel die and punch. The steel die wall and the punch were uniformly coated with Zinc stearate along with acetone to reduce wall friction. No lubricant was added with powders to avoid reduction in sintered density. Sintering was carried out in muffle furnace and under neutral atmosphere with nitrogen of 99 % purity. Sintering temperatures employed are 400ºC, 500ºC and 600ºC with sintering time from 1hr to 3 hr. Sintered density was measured as per ASTM B962 – 08. Measuring hardness of composites was carried out using Vicker’s micro hardness tester. Figure-1 shows the micrograph of the sintered Al-SiCp MMC sample work piece. The uniform distribution of SiCp in these composites was clearly visible. Al Matrix SiC Particles Fig. 1. Micrograph of Sintered Al-SiCp MMC Sample 4 © 2013 University of Manchester and the authors. This is a preprint of a paper that has been submitted for publication in the Journal of Corrosion Science and Engineering. It will be reviewed and, subject to the reviewers’ comments, be published online at http://www.jcse.org in due course. Until such time as it has been fully published it should not normally be referenced in published work. ISSN 1466-8858 2.2. Volume 16, Preprint 43 submitted 27 June 2013 Methods In recent years, Taguchi method is used because of its economical and effective technique for improving productivity as well as to get robust design in order to manufacture high quality products rapidly . A special design of orthogonal arrays for four factors at three levels can be applied to study the entire parameters with minimal experiment requirements [15]. The process parameters and levels are listed in Table 1. Each of the 27 trials / process designs was replicated twice and the average response values were used for the analysis. Table 2 showed the experimental arrangement and test results. Table 1 Process parameters and their levels Parameter Unit % Reinforcement Wt Compacting Level Level Level 5 10 15 MPa 350 450 550 Sintering °C 400 500 600 Sintering Time Hrs 1 2 3 3. Determination of optimal machining parameters 3.1. Grey Relational Analysis (GRA) Preliminary trials were conducted in order to normalize the raw data for the analysis . A linear normalization of the experimental findings for sintered density and micro hardness were performed in the range between zero and one (Table 2) , which is also 5 © 2013 University of Manchester and the authors. This is a preprint of a paper that has been submitted for publication in the Journal of Corrosion Science and Engineering. It will be reviewed and, subject to the reviewers’ comments, be published online at http://www.jcse.org in due course. Until such time as it has been fully published it should not normally be referenced in published work. ISSN 1466-8858 Volume 16, Preprint 43 submitted 27 June 2013 called grey relational generation. The normalized experimental results Xij can be expressed as: x ij = y ij − min y ij j max j y ij − min j (1) y ij Yij for the ith experimental results in the jth experiment. Table 3 shows the normalized results for sintered density and micro hardness. Basically, larger normalized results correspond to the improved performance of MMC’s and the bestnormalized results should be equal to unity. 6 © 2013 University of Manchester and the authors. This is a preprint of a paper that has been submitted for publication in the Journal of Corrosion Science and Engineering. It will be reviewed and, subject to the reviewers’ comments, be published online at http://www.jcse.org in due course. Until such time as it has been fully published it should not normally be referenced in published work. ISSN 1466-8858 Volume 16, Preprint 43 submitted 27 June 2013 Table 2 Experimental layout using an L27 orthogonal array and corresponding results Process parameter(s) Exp Mean value of response(s) Reinforcement Compaction Sintering Sintering Density Micro hardness % Pressure Temperature Time (gms/cc) (HV0.5) 1 5 350 400 1 2.540 51.31 2 10 450 500 2 2.449 47.37 3 15 550 600 3 2.443 56.88 4 5 450 500 2 2.168 49.27 5 10 550 600 3 2.421 41.50 6 15 350 400 1 2.284 47.44 7 5 550 600 3 2.482 62.05 8 10 350 400 1 2.205 36.00 9 15 450 500 2 2.331 52.70 10 15 350 500 3 2.130 38.84 11 5 450 600 1 2.310 46.05 12 10 550 400 2 2.662 65.98 13 15 450 600 1 2.475 46.90 14 5 550 400 2 2.508 65.40 15 10 350 500 3 2.255 39.70 16 15 550 400 2 2.486 56.01 17 5 350 500 3 2.332 40.70 18 10 450 600 1 2.431 57.02 19 10 350 600 2 2.211 42.50 20 15 450 400 3 2.432 56.08 21 5 550 500 1 2.497 59.40 22 10 450 400 3 2.585 50.40 23 15 550 500 1 2.563 61.64 24 5 350 600 2 2.277 38.67 25 10 550 500 1 2.563 67.31 26 15 350 600 2 2.288 31.67 27 5 450 400 3 2.453 54.94 No. 7 © 2013 University of Manchester and the authors. This is a preprint of a paper that has been submitted for publication in the Journal of Corrosion Science and Engineering. It will be reviewed and, subject to the reviewers’ comments, be published online at http://www.jcse.org in due course. Until such time as it has been fully published it should not normally be referenced in published work. ISSN 1466-8858 Volume 16, Preprint 43 submitted 27 June 2013 Also, the grey relational coefficient was calculated to express the relationship between the ideal (best) and the actual normalized experimental results. The grey relational coefficient ξij can be written as: min min x io − x ij + ξ max max i ξ ij = j i j x io − x ij + ξ max max i j x io − x ij x io − x ij (2) Where xi0 is the ideal normalized results for the ith performance characteristics and ξ is the distinguishing coefficient which is defined in the range 0 ≤ ξ ≤ 1. In the present study the value of ξ is assumed as 0.5 to give equal weightage for the responses. The grey relational grade is obtained after computing the average grey relational coefficient corresponding to each performance characteristics. The overall evaluation of the performance response is based on the grey relational grade, that is: (3) 1 m γ j = ∑ξij m i=1 Where γj is the grey relational grade for the jth experiment and m is the number of performance characteristics. 8 © 2013 University of Manchester and the authors. This is a preprint of a paper that has been submitted for publication in the Journal of Corrosion Science and Engineering. It will be reviewed and, subject to the reviewers’ comments, be published online at http://www.jcse.org in due course. Until such time as it has been fully published it should not normally be referenced in published work. ISSN 1466-8858 Volume 16, Preprint 43 submitted 27 June 2013 Table 3. Evaluated Grey relational coefficient and Grade for 27 groups Exp. No Normalized Values Grey relational Coefficients Grey relational grade Density Micro Hardness Density Micro Hardness Grey grade Rank 1 0.7706 0.5511 0.6855 0.5269 0.6062 9 2 0.5997 0.4405 0.5554 0.4719 0.5136 15 3 0.5884 0.7074 0.5485 0.6308 0.5896 10 4 0.0710 0.4938 0.3499 0.4969 0.4234 21 5 0.5465 0.2758 0.5244 0.4084 0.4664 17 6 0.2902 0.4425 0.4133 0.4728 0.4430 19 7 0.6622 0.8524 0.5968 0.7721 0.6845 5 8 0.1417 0.3740 0.3681 0.4441 0.4061 22 9 0.3786 0.5901 0.4459 0.5495 0.4977 16 10 0.0000 0.2012 0.3333 0.3850 0.3591 27 11 0.3383 0.4035 0.4304 0.4560 0.4432 18 12 1.0000 0.9627 1.0000 0.9305 0.9653 1 13 0.6485 0.4273 0.5872 0.4661 0.5267 14 14 0.7105 0.9464 0.6333 0.9032 0.7683 3 15 0.2350 0.2253 0.3952 0.3923 0.3937 25 16 0.6692 0.6829 0.6018 0.6120 0.6069 8 17 0.3797 0.2534 0.4463 0.4011 0.4237 20 18 0.5658 0.7113 0.5352 0.6339 0.5846 11 19 0.1523 0.3039 0.3710 0.4180 0.3945 24 20 0.5677 0.6849 0.5363 0.6134 0.5749 13 21 0.6898 0.7781 0.6172 0.6926 0.6549 6 22 0.8553 0.5255 0.7755 0.5131 0.6443 7 23 0.8139 0.8409 0.7288 0.7586 0.7437 4 24 0.2763 0.1964 0.4086 0.3836 0.3961 23 25 0.8139 1.0000 0.7288 1.0000 0.8644 2 26 0.2970 0.0000 0.4156 0.3333 0.3745 26 27 0.6071 0.6529 0.5600 0.5903 0.5751 12 9 © 2013 University of Manchester and the authors. This is a preprint of a paper that has been submitted for publication in the Journal of Corrosion Science and Engineering. It will be reviewed and, subject to the reviewers’ comments, be published online at http://www.jcse.org in due course. Until such time as it has been fully published it should not normally be referenced in published work. ISSN 1466-8858 Volume 16, Preprint 43 submitted 27 June 2013 Table 3 shows the grey relational grade for each experiment using L27 orthogonal array. The higher grey relational grade implies the better product quality; therefore, on the basis of grey relational grade, the process parameters influence can be predicted and the suitable values for each influencing factor may also be estimated.. The mean of the grey relational grade for each level of the parameter is summarized and shown in Table 4. In addition, the total mean of the grey relational grade for the 27 experiments is also calculated and listed in Table 4. The grey relational grade graph for the levels of the processing parameters (fig.2). Basically, the larger the grey relational grade, the better is the performance response. Table 4 Response table for the grey relational grade Processing Parameter Grey relational grade Level Level Level Max-Min Rank 1 2 3 % Reinforcement 0.5528 0.5814* 0.5240 0.0574 4 Compacting 0.4219 0.5315 0.7049* 0.2830 1 Sintering 0.6211* 0.5416 0.4956 0.1256 2 Sintering Time 0.5859* 0.5489 0.5235 0.0624 3 Total Mean Value of the Grey Relational Grade = 0.5528 10 © 2013 University of Manchester and the authors. This is a preprint of a paper that has been submitted for publication in the Journal of Corrosion Science and Engineering. It will be reviewed and, subject to the reviewers’ comments, be published online at http://www.jcse.org in due course. Until such time as it has been fully published it should not normally be referenced in published work. ISSN 1466-8858 3.2. Volume 16, Preprint 43 submitted 27 June 2013 Analysis of Variance The purpose of the variation analysis is to study the influence of processing factors that contribute the important characteristics [19]. This is accomplished by separating the total variability of the grey relational grades, which is measured by the sum of the squared deviations from the total mean of the grey relational grade, into contributions by each machining parameter and the error. First, the total sum of the squared deviations SST from the total mean of the grey relational grade γm can be calculated as: SS T = p ∑ j =1 (γ j −γ m )2 (4) Where p is the number of experiments in the orthogonal array and γj is the mean grey relational grade for the j th experiment. 11 © 2013 University of Manchester and the authors. This is a preprint of a paper that has been submitted for publication in the Journal of Corrosion Science and Engineering. It will be reviewed and, subject to the reviewers’ comments, be published online at http://www.jcse.org in due course. Until such time as it has been fully published it should not normally be referenced in published work. ISSN 1466-8858 Volume 16, Preprint 43 submitted 27 June 2013 Main effects plot (data means) for Means % Reinforcement Compacting Pressure 0.7 Grey relational grade 0.6 0.5 0.4 5 10 15 350 Sintering Temperature 450 550 Sintering Time 0.7 0.6 0.5 0.4 400 500 600 1 2 3 Fig. 2. Main effects plot for Grey relational grade The cumulative addition of the square of the deviation SST is classified into two factors Viz., consolidated processing parameter and its interaction effects and the square of the error . The actual part of each of the processing parameter in the total sum of the squared deviations SST may be utilized to assess the contribution of the processing parameter deviation results of this analysis. F- test [20] can also be used to determine which machining parameters have a significant effect on the performance characteristic. Table 7 shows the results of ANOVA analysis[16-19]. 12 © 2013 University of Manchester and the authors. This is a preprint of a paper that has been submitted for publication in the Journal of Corrosion Science and Engineering. It will be reviewed and, subject to the reviewers’ comments, be published online at http://www.jcse.org in due course. Until such time as it has been fully published it should not normally be referenced in published work. ISSN 1466-8858 Volume 16, Preprint 43 submitted 27 June 2013 Table 5 Results of the analysis of variance Source df SS MS F 0.84 % % Reinforcement 2 0.01484 0.0074 2.35 Compacting Pressure 2 0.3664 0.1832 Sintering Temperature 2 0.0726 0.0363 4.09 11.51 Sintering Time 2 0.0177 0.0088 1.00 2.80 Error 18 0.1596 0.0088 Total 26 0.6312 20.66 58.05 25.28 100.0 Results of analysis of variance (Table - 7) indicate that depth of cut is the most significant machining parameter for affecting the multiple performance characteristics (32.23%). 3.3. Confirmation Experiment Once the optimal level of processing parameters is selected the prediction and verification of predicted parameter level is carried out and the improvement of the performance characteristics using the optimal level of the processing parameters is evaluated. The estimated grey relational grade γˆ using the optimum level of the machining parameters can be calculated as q γˆ = γ m + ∑(γ j −γ m) i =1 (5) 13 © 2013 University of Manchester and the authors. This is a preprint of a paper that has been submitted for publication in the Journal of Corrosion Science and Engineering. It will be reviewed and, subject to the reviewers’ comments, be published online at http://www.jcse.org in due course. Until such time as it has been fully published it should not normally be referenced in published work. ISSN 1466-8858 Volume 16, Preprint 43 submitted 27 June 2013 Where γm is the total mean of the grey relational grade, γ j is the mean of the grey relational grade at the optimum level and q is the number of processing parameters that significantly affects the multiple performance characteristics. Based on Eq (5) the estimated grey relational grade using the optimal processing parameters can then be obtained. The results of the confirmation experiment using the optimal processing parameters sintered density was 2.692 and the microhardness was 71.98 HV0.5 and the grey relational grade value is 0.8611 which is 3.12% higher than the predicted mean value. It is clearly shown that multiple performance characteristics in the Al-SiC are greatly improved through this study. The contour plots obtained using the experimental data presented in fig 3 and 4 also inline with the grey analysis that the compacting pressure and sintering temperature which are significant process parameters yield maximum sintered density and microhardness. The results are have given understanding that higher reinforcement percentage and higher sintering temperature do not give maximum sintered density and microhardness, This is on account of the fact that as the reinforcement percentage increases compressibility is becoming poor and also higher sintering temperature combined with higher reinforcement leads reduction in inter particle distance of SiC which leads to poor bonding and probability of defect formation and increased porosity which hampers achieving the maximum sintered density and microhardness.[20,21] 14 © 2013 University of Manchester and the authors. This is a preprint of a paper that has been submitted for publication in the Journal of Corrosion Science and Engineering. It will be reviewed and, subject to the reviewers’ comments, be published online at http://www.jcse.org in due course. Until such time as it has been fully published it should not normally be referenced in published work. ISSN 1466-8858 Volume 16, Preprint 43 submitted 27 June 2013 Contour Plot of Density vs Rein.Per, Com.Pr 15.0 Density < 2.2 2.2 - 2.3 2.3 - 2.4 2.4 - 2.5 2.5 - 2.6 > 2.6 Rein.Per 12.5 10.0 7.5 5.0 350 400 450 Com.Pr 500 550 Fig.3. Contour plot for Density Vs Reinforcement and compacting pressure. Contour Plot of Microhardness vs Com.Pr, Sint.Tem 550 Microhardness < 35 35 - 40 40 - 45 45 - 50 50 - 55 55 - 60 60 - 65 > 65 Com.Pr 500 450 400 350 400 450 500 Sint.Tem 550 600 Fig.4 Contour Plot for Microhardness Vs compacting pressure and sintered temperature 15 © 2013 University of Manchester and the authors. This is a preprint of a paper that has been submitted for publication in the Journal of Corrosion Science and Engineering. It will be reviewed and, subject to the reviewers’ comments, be published online at http://www.jcse.org in due course. Until such time as it has been fully published it should not normally be referenced in published work. ISSN 1466-8858 4. Volume 16, Preprint 43 submitted 27 June 2013 Conclusion A precursor experimental analysis for obtaining corrosion resistant composites were tried out successfully using grey relational analysis for developing metal matrix composites by powder processing route. It has been also justified that 10% reinforcement with 550 MPa sintered at 400ºC for one hour resulted in maximizing the sintered density and microhardness. This encouraged applying the grey concept for optimizing multi response processing with multiple factors. ANOVA also showed that compacting pressure, sintering temperature, sintering time and reinforcement are impacting the objective achieving the sintered density and microhardness in the order. This justified that with optimization of the processing parameters yielded the desired results for obtaining corrosion resistant Al-SiC composites. Acknowledgement We are thankful to Dr.R.Krishnamurthy,IIT ,Madras, Mr.Jessu Joys, AMPAL, USA, Mr.Jeyan, Carborundum Universal Limited, Mr.Parthasarathy , Metmech Engineers Chennai for their kind guidance and help in this work. 16 © 2013 University of Manchester and the authors. This is a preprint of a paper that has been submitted for publication in the Journal of Corrosion Science and Engineering. It will be reviewed and, subject to the reviewers’ comments, be published online at http://www.jcse.org in due course. Until such time as it has been fully published it should not normally be referenced in published work. ISSN 1466-8858 Volume 16, Preprint 43 submitted 27 June 2013 References 1. M.K.Surappa, Alumnium matrix composites: challenges & opportunities, Sadhana Vol.28,Part 1,February 2003,p 319-334. 2. Warren.H,Hurt Jr,Daniel B.Miralce,Automotive applications of Metal matrix composites,p1029-1032. 3. Nikhilesh Chawla & Yu-Lui Shan,Mechancial behaviour of Particle reinforced metal matrix composites,Advanced Engineering Materials ,Vol 3,Issue 6,2001,pp357-370. 4. J.M.Torralbo,C.E.da costa and F.Velasco,,P/M Alumnium matrix composites:An overview,Journal of Materials Processing Technology,133,2003 p203- 206. 5.T.Pieczonka,T.Schubert,S.Baunack and B.Kieback,Sintering Behaviour of Alumnium in Different Atmospheres,Sintering ’05,p331-334 Edited by Knag and Suk-Joong. 6.A.Arokiaswamy,R.M.German,P.Wang,W.F.Hortemeyer,W.Morgan and S.J.park , Powder Metallurgy , 2010, p 1-6. 7. S.Muller,Th.Schubert,F.Fiedler,R.Stein,B.Kieback,L.Deter ,Properties of Sintered Alumnium Composites,Euro Pm-2011-Metal matrix composites,p1-5. 8. Brian Ralph·, H.C. Yuen· and W.B. Lee, The processing of metal matrix composites – an overview,Journal of Materials Processing Technology,63,1997,p339-353. 17 © 2013 University of Manchester and the authors. This is a preprint of a paper that has been submitted for publication in the Journal of Corrosion Science and Engineering. It will be reviewed and, subject to the reviewers’ comments, be published online at http://www.jcse.org in due course. Until such time as it has been fully published it should not normally be referenced in published work. ISSN 1466-8858 Volume 16, Preprint 43 submitted 27 June 2013 9.A.R.I. Khedera,G.S. Marahleh, D.M.K. Al-Jamea, Strengthening of Aluminum by SiC, Al2O3 and MgO, Jordan Journal of Mechanical and Industrial Engineering , Vol 5, Number 6, Dec. 2011 ISSN1995-6665 Pages 533 – 541. 10. V.V. Bhanu Prasad , B.V.R. Bhat , Y.R. Mahajan , P. Ramakrishnan , Structure_/property correlation in discontinuously reinforced aluminium matrix composites as a function of relative particle size ratio, Materials Science and Engineering A337 ,2002,p 179-186. 11.G.B. Schaffer,Powder Processing of Aluminium Alloys, Materials Forum, Vol 28, 2004, Edited by J.F Nie, A.J. Morton and B.C. Muddle, Published by Institute of Materials Engineering Australasia Ltd, p 65- 74. 12.W. M. Khairaldien, A.A. Khalil and M. R. Bayoumi, Production of Aluminum-Silicon Carbide Composites Using Powder processing at Sintering Temperatures above the Aluminum Melting Point, The Fourth Assiut University Int. Conf. on Mech. Eng. Advanced Tech. for Indus. Prod., MEATIP 4,Assiut, Egypt, 2006. 13. Everthon Rodrigues de Araujo, Sérvulo José Ferreira Alves , Francisco Ambrozio Filho ,Severino Leopoldino Urtiga Filho and Oscar Olimpio de Araujo Filho processing and Manufacturing of Metal Matrix Aluminum Alloys Composites reinforced by Silicon Carbide and Alumina Through Powder processing Techniques ,Eighth International Latin American Conference on Powder Technology, November 06 to 09, Costão do Santinho, Florianópolis, SC, Brazil, 325-330.2011. 18 © 2013 University of Manchester and the authors. 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Sornakumar , Multiresponse optimisation of sintering parameters of Al–Si alloy/fly ash composite using Taguchi method and grey relational analysis ,International Journal of Advanced Manufacturing Technology, March 2009,362-369. 19.Radhakrishnan Ramanujam, Nambi Muthukrishnan and Ramaswamy Raju, “Optimization of Cutting Parameters for Turning Al-SiC(10p) MMC Using ANOVA and Grey Relational Analysis”, International Journal Of Precision Engineering and Manufacturing , Vol. 12,2011, p .651-656. 19 © 2013 University of Manchester and the authors. This is a preprint of a paper that has been submitted for publication in the Journal of Corrosion Science and Engineering. It will be reviewed and, subject to the reviewers’ comments, be published online at http://www.jcse.org in due course. Until such time as it has been fully published it should not normally be referenced in published work. ISSN 1466-8858 Volume 16, Preprint 43 submitted 27 June 2013 20. Ileana Nicoleta Popescu, Simona Zamfir, Violeta Florina Anghelina, and Carmen Otilia Rusanescu, Processing by P/M route and characterization of new ecological Aluminum Matrix Composites (AMC),International Journal of Mechanics,Vol4,Issue 3,2010,43-52. 21.J.W. Kaczmar,, K. Pietrzak, W. WosinÂski, The production and application of metal matrix composite materials, Journal of Materials Processing Technology 106 ,2000,p 58-67. 20 © 2013 University of Manchester and the authors. This is a preprint of a paper that has been submitted for publication in the Journal of Corrosion Science and Engineering. It will be reviewed and, subject to the reviewers’ comments, be published online at http://www.jcse.org in due course. Until such time as it has been fully published it should not normally be referenced in published work.