Maximization of Material Removal Rate in the Machining of A356/Cow Horn Particle Composites Using Response Surface Methodology

Sunday Chimezie Anyaora (Department of Mechanical Engineering, Faculty of Engineering, Nnamdi Azikiwe University, Awka, Anambra state, Nigeria)
Francis Chukwunonso Okeke (Department of Mechanical Engineering, Faculty of Engineering, Nnamdi Azikiwe University, Awka, Anambra state, Nigeria)
Ikenna Theophilus Odoh (Department of Mechanical Engineering, Faculty of Engineering, Nnamdi Azikiwe University, Awka, Anambra state, Nigeria)
Onyeka Noel Anyali (Department of Mechanical Engineering, Faculty of Engineering, Nnamdi Azikiwe University, Awka, Anambra state, Nigeria)
Chibuzo Ndubuisi Okoye (Department of Mechanical Engineering, Faculty of Engineering, Nnamdi Azikiwe University, Awka, Anambra state, Nigeria)

BIOS: Jurnal Informatika dan Sains
Vol. 3 No. 2 (2025): BIOS: Jurnal Informatika dan Sains, October 2025

Efficient machining of metal matrix composites is vital for enhancing productivity and reducing manufacturing costs in modern engineering applications. Aluminum alloy A356 reinforced with cow horn particles offers improved mechanical properties, but its machinability requires systematic optimization. The study utilized Aluminum alloy A356 reinforced with cow horn particles, fabricated via spark plasma sintering at 550 °C and 30 MPa. Composite samples (100×5 mm) were produced under vacuum with graphite dies. Machining experiments were conducted on a Universal Turning Machining Centre using HSS/HCS cutting tools, supported by equipment such as weighing balance, crucible, stirrer, hopper, mould, and lathe for dimensional accuracy. Process parameters included cutting speed (500–900 RPM), depth of cut (0.5–1.5 mm), and feed rate (0.15–0.25 mm/rev). Material Removal Rate (MRR) was measured using surface testers and weighing balance. Optimization employed Response Surface Methodology (RSM) and regression analysis for predictive modeling. Results showed that wear rate decreased with increased graphite content, with sample L having the lowest wear and sample I the highest. Response Surface Methodology (RSM) analysis revealed that material removal rate (MRR) ranged from 3.75 to 30.91 mm³/min, with a mean of 15.72. Feed rate, cutting speed, and depth of cut were significant (p < 0.05), while interaction effects were negligible. Feed rate exhibited a strong negative effect, while cutting speed and depth of cut had mixed influences. Model accuracy was validated (R² = 0.9932). Optimal conditions were found at moderate cutting speed and higher depth of cut. These findings validate RSM as an effective optimization tool for machining composites, supporting improved efficiency and performance in industrial applications.