Applied Engineering

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Estimation of Surface Roughness of Aluminum Reinforced Metal Matrix Composites

Received: Dec. 30, 2019    Accepted: Jan. 09, 2020    Published: Jan. 21, 2020
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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.

DOI 10.11648/j.ae.20200401.12
Published in Applied Engineering ( Volume 4, Issue 1, June 2020 )
Page(s) 7-13
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Surface Roughness, Metal Matrix, Composites, Feed Rate, Stir Casting

References
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[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.
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[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.
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[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.
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[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.
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[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.
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  • APA Style

    Jimoh Olugbenga Hamed, Ganiyu Ishola Agbaje, Abdullahi Ikani Bakwo, Bisola Abigail Olaniyi, Ismail Olusegun Lawal, et al. (2020). Estimation of Surface Roughness of Aluminum Reinforced Metal Matrix Composites. Applied Engineering, 4(1), 7-13. https://doi.org/10.11648/j.ae.20200401.12

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    ACS Style

    Jimoh Olugbenga Hamed; Ganiyu Ishola Agbaje; Abdullahi Ikani Bakwo; Bisola Abigail Olaniyi; Ismail Olusegun Lawal, et al. Estimation of Surface Roughness of Aluminum Reinforced Metal Matrix Composites. Appl. Eng. 2020, 4(1), 7-13. doi: 10.11648/j.ae.20200401.12

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    AMA Style

    Jimoh Olugbenga Hamed, Ganiyu Ishola Agbaje, Abdullahi Ikani Bakwo, Bisola Abigail Olaniyi, Ismail Olusegun Lawal, et al. Estimation of Surface Roughness of Aluminum Reinforced Metal Matrix Composites. Appl Eng. 2020;4(1):7-13. doi: 10.11648/j.ae.20200401.12

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  • @article{10.11648/j.ae.20200401.12,
      author = {Jimoh Olugbenga Hamed and Ganiyu Ishola Agbaje and Abdullahi Ikani Bakwo and Bisola Abigail Olaniyi and Ismail Olusegun Lawal and Adekunle Benjamin Falade},
      title = {Estimation of Surface Roughness of Aluminum Reinforced Metal Matrix Composites},
      journal = {Applied Engineering},
      volume = {4},
      number = {1},
      pages = {7-13},
      doi = {10.11648/j.ae.20200401.12},
      url = {https://doi.org/10.11648/j.ae.20200401.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ae.20200401.12},
      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.},
     year = {2020}
    }
    

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  • TY  - JOUR
    T1  - Estimation of Surface Roughness of Aluminum Reinforced Metal Matrix Composites
    AU  - Jimoh Olugbenga Hamed
    AU  - Ganiyu Ishola Agbaje
    AU  - Abdullahi Ikani Bakwo
    AU  - Bisola Abigail Olaniyi
    AU  - Ismail Olusegun Lawal
    AU  - Adekunle Benjamin Falade
    Y1  - 2020/01/21
    PY  - 2020
    N1  - https://doi.org/10.11648/j.ae.20200401.12
    DO  - 10.11648/j.ae.20200401.12
    T2  - Applied Engineering
    JF  - Applied Engineering
    JO  - Applied Engineering
    SP  - 7
    EP  - 13
    PB  - Science Publishing Group
    SN  - 2994-7456
    UR  - https://doi.org/10.11648/j.ae.20200401.12
    AB  - 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.
    VL  - 4
    IS  - 1
    ER  - 

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Author Information
  • African Regional Centre for Space Science and Technology Education in English, Obafemi Awolowo University Campus, Ile-Ife, Nigeria

  • African Regional Centre for Space Science and Technology Education in English, Obafemi Awolowo University Campus, Ile-Ife, Nigeria

  • Centre for Space Transport and Propulsion, Lagos State University Campus, Epe, Lagos, Nigeria

  • Engineering Space System, National Space Research and Development Agency, Abuja, Nigeria

  • Advanced Aerospace Engines Laboratory, Oka-Akoko, Ondo, Nigeria

  • African Regional Centre for Space Science and Technology Education in English, Obafemi Awolowo University Campus, Ile-Ife, Nigeria

  • Section