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Research Article

Performance Optimization of Wet Turning of Aluminum Alloy 6351 Eggshell Reinforced Composite Using Response Surface Methodology

Authors

  • Bethel Mba Department of Mechanical Engineering, Michael Okpara University of Agriculture Umudike, Nigeria https://orcid.org/0000-0001-6589-7320

    mbabethelchidiadi@gmail.com

  • S. C. Nwoziri Department of Mechanical Engineering, University of Calabar, Nigeria
  • Franklin Onwuka Department of Mechanical Engineering, Federal University of Nigeria Nsukka, Nigeria
  • Uchenna Alozie Department of Mechanical Engineering, Federal Polytechnic Nekede, Nigeria
  • L. J. King Department of Mechanical Engineering, Federal University of Technology Owerri, Nigeria
  • Onyekachi Monday Okafor Department of Mechanical Engineering, University of Chester, United Kingdom https://orcid.org/0009-0008-3835-5321

Abstract

The limited understanding of key input parameters and material machinability has hindered the industry's full utilization of machining processes. These limitations make it challenging to meet machining response requirements and address various related issues. This study employs Response Surface Methodology (RSM) to explore the interaction between input parameters and responses during the wet turning of aluminum alloy eggshell reinforced composite (AAERC). Numerical optimization was used to determine the optimal combinations of process parameters, achieving the best results in terms of Material Removal Rate (MRR) and Surface Roughness (Ra). The AAERCs consisted of 85% aluminum alloy and 15% eggshell. To enhance wettability, 2% of equal-sized crushed magnesium powder was added to the molten metal. Improved wettability decreases surface tension, increases surface energy, and reduces the energy at the matrix-reinforcement interface. The developed regression equation model can predict Ra and MRR when input variables such as cutting speed (VC), feed rate (Fr), and depth of cut (DC) are known. The fit statistics for MRR and Ra indicate that the R² and adjusted R² values are 0.9461 and 0.8490 for Ra, and 0.9745 and 0.9286 for MRR, respectively. These values demonstrate that the models provide a strong fit for both responses. The parameters VC, Fr and DC, with P-values of 0.0003, 0.0017, and 0.0008 respectively, significantly influence MRR. Similarly, VC and Fr, with P-values of 0.0006 and 0.0583 respectively, significantly impact Ra. The optimization process results indicate that the optimal values for Ra and MRR are 1.0689µm and 1793.93mm³/min, respectively. These results are achieved when turning operations on AAERC are conducted using the input variables VC, DC, and Fr at 369.822rpm, 0.333554mm/min, and 0.235944mm, respectively.

Keywords:

Material Removal Rate Numerical Optimization Regression Equation Response Surface Methodology Surface Roughness

Article information

Journal

Scientific Journal of Engineering, and Technology

Volume (Issue)

2(1), (2025)

Pages

112-125

Published

22-05-2025

How to Cite

Mba, B., Nwoziri, S. C., Onwuka, F., Alozie, U., King, L. J., & Okafor, O. M. (2025). Performance Optimization of Wet Turning of Aluminum Alloy 6351 Eggshell Reinforced Composite Using Response Surface Methodology. Scientific Journal of Engineering, and Technology, 2(1), 112-125. https://doi.org/10.69739/sjet.v2i1.553

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