Volume 4, Issue 1, June 2020, Page: 7-13
Estimation of Surface Roughness of Aluminum Reinforced Metal Matrix Composites
Jimoh Olugbenga Hamed, African Regional Centre for Space Science and Technology Education in English, Obafemi Awolowo University Campus, Ile-Ife, Nigeria
Ganiyu Ishola Agbaje, African Regional Centre for Space Science and Technology Education in English, Obafemi Awolowo University Campus, Ile-Ife, Nigeria
Abdullahi Ikani Bakwo, Centre for Space Transport and Propulsion, Lagos State University Campus, Epe, Lagos, Nigeria
Bisola Abigail Olaniyi, Engineering Space System, National Space Research and Development Agency, Abuja, Nigeria
Ismail Olusegun Lawal, Advanced Aerospace Engines Laboratory, Oka-Akoko, Ondo, Nigeria
Adekunle Benjamin Falade, African Regional Centre for Space Science and Technology Education in English, Obafemi Awolowo University Campus, Ile-Ife, Nigeria
Received: Dec. 30, 2019;       Accepted: Jan. 9, 2020;       Published: Jan. 21, 2020
DOI: 10.11648/j.ae.20200401.12      View  474      Downloads  162
Abstract
There is a strong agitation from rocket designer for a highly reinforced metal matrix composites for rocket chamber to curtail the effect of high temperature and pressure from gaseous product of combustion process. This study has been designed to evaluate the surface roughness of an aluminum reinforced metal matrix composites produced by stir casting techniques at constant cutting speed of 1000 rpm, three (3) different feed rates at various aluminum weight ratio. Response surface methodology was adopted to formulate a surface roughness model in terms of metal matrix constituents such as aluminum, barite and zircon under three (3) different feed rate. The model adequacy was verified using analysis of variance. Also, the approach was used to optimize the effect of reinforced materials on surface roughness of the matrix composites. The increase in weight ratio of aluminum matrix reduces the surface roughness and vice versa. However, increase in barite, zircon weight ratios and feed rate increase the surface roughness. The optimum matrix chemical composition ratios of 0.9310, 0.0296, and 0.0394 for aluminum, barite, and zircon respectively with optimal desirability index of 0.903 shows the validity of the design. The F-values obtained at 95% confidence interval revealed that the selected model adequately represent the data for the matrix composites. Therefore, the study confirm the effectiveness of Response Surface Methodology as a tool in predicting surface roughness and provide materials with enhanced mechanical properties.
Keywords
Surface Roughness, Metal Matrix, Composites, Feed Rate, Stir Casting
To cite this article
Jimoh Olugbenga Hamed, Ganiyu Ishola Agbaje, Abdullahi Ikani Bakwo, Bisola Abigail Olaniyi, Ismail Olusegun Lawal, Adekunle Benjamin Falade, Estimation of Surface Roughness of Aluminum Reinforced Metal Matrix Composites, Applied Engineering. Vol. 4, No. 1, 2020, pp. 7-13. doi: 10.11648/j.ae.20200401.12
Copyright
Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Reference
[1]
Hufnagel, W. Key to aluminum Alloys, Aluminum Publication, Dusseldorf, Germany, 1999.
[2]
Ramachandran, T. R. “Advances in Aluminum Processing and its Automotive Application, “Workshop Lecture Notes, pp. 28–32, Indian Institute of Metals, Pune Chapter, 2006.
[3]
Dieter, G. Mechanical Metallurgy, SI Metric Edition, McGraw–Hill, London, UK, 1988.
[4]
Callister, W. D. Fundamentals of Materials Science and Engineering, John Wiley & Sons, Hoboken, NJ, USA, 2001.
[5]
Hirsch, J., Skrotzki, B. and Gottstein, G. Aluminum Alloys, Their Physical and Mechanical Properties, Wiley-VCH, Weinheim, Germany, 2008.
[6]
2010, http://aluminium.matter.org.uk/aluselect/06 composition browse.asp.
[7]
Kopeliovich, D. Wrought Aluminum-magnesium-silicon alloys (6xxx), 2010, http://www.substech.com/dokuwiki/doku.php=wroughtaluminum-magnesium-silicon alloys 6xxx.
[8]
2010, http://www.aluminum.org.
[9]
G. Gottstein, Physical Foundations of Materials Science, Springer, Berlin, Germany, 2004.
[10]
Humphreys, F. J. and Hatherly, M. Recrystallization and Related Annealing Phenomena, Elsevier, Oxford, UK, 2004.
[11]
Songmene, Balazinski, M. Machinability of Graphite Metal Matrix Composites as a function of reinforcing particles, Annals of the clrp 1999, vol. 48.
[12]
Heath PJ. Development in Applications of PCD tooling. J. Mater Process Technol, 2001; 116: 31–38.
[13]
Chamber AR. The Machinability of Light Alloy Metal Matrix Composites. Composites: Part A. 1996; 27: 143–147.
[14]
Huang, B. and Chen, J. C. An in Process Neural network based Surface Roughness Prediction System using a dynamometer in end Milling Operations. Int. J. Adv. Manuf Technol 2003; 21: 339–347.
[15]
Tosin, N. Determination of optimum parameters for multi Adv performance characteristics in Drilling by using grey relational analysis, Int J Manuf Technol, 2006: 28 (5-6); 450–455.
[16]
Rajesh, K. B., Sudhir, K. and Das, S. Effect of Machining Parameters on Surface Roughness and Tool Wear for 7075 Al alloy SiC Composite. International Journal of Adv Manuf Techno. 2006; 28 (5-6): 450–455.
[17]
Metin, K. Modelling the Effect of Surface Roughness Factors in the Machining of 2024 Al/Al203 Particles Composites based on Orthogonal Arrays. International Journal of Adv Manuf Techno. 2011; 55: 911–920.
[18]
Palanikumar, K. and Karthikeya, R. Optimal Machining Conditions for Turning of Particulate Metal Matrix Composites using Taguchi and Response Surface Methodologies. Machining Science and Technology. 2006; 10: 417–433.
[19]
Lou, S. J. and Chen, C. J. In-process Surface Roughness Recognition System in End-milling Operation. International Journal of Adv Manuf Techno. 1999; 15: 200–209.
[20]
Karthikeyan, R. Raghukanda, K, Naagarazan, R. S. and Pai, B. C. Optimizing the Milling Characteristics of Al-SiC Particulates Composites. Metal and Materials. 2000; 6: 539–547.
[21]
Ramulu, M., Kim, D. and Kao, H. Experimental Study of PCD Tool Performance in Drilling Al203/6061 Metal Matrix Composites. SME Technical paper. 2003; 171: 1–7.
[22]
Poddar, S. and Sudhir, K. N. Analysis of Properties of Aluminum–Graphite Metal Matrix Composites. International Journal of Engineering Research and Technology (IJERT). 2013, 2, 11, ISSN: 2278–0181.
[23]
Montgomery, D. C. (2009). Water Treatment Principles and Design, Wiley Interscience, New York, 1, 175 - 180.
[24]
Montgomery, D. C. (2005). Design and Analysis of Experiments: Response Surface Method and Designs, New Jersey: John Wiley and Sons, Inc, 8, 156 - 165.
[25]
Poddar, S. and Sudhir, K. N. (2013). Analysis of Properties of Aluminum-Graphite Metal Matrix Composites, International Journal of Engineering Research and Technology (IJERT), 2, 11, ISSN: 2278–0181.
[26]
Cocharan, W. G. and Cox, G. M. (1957). Experimental Designs (edited by R. A. Bradley, D. G. Kendall, J. S. Hunter and G. S. Watson), John Wiley and sons, New York, 6, 335–375.
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