EXPERIMENTAL AND PREDICTIVE MODELLING OF SURFACE ROUGHNESS IN MACHINING ALUMINIUM ALLOY A356/COW HORN PARTICLE COMPOSITE

Authors

  • Sunday Chimezie Anyaora Department of Mechanical Engineering, Faculty of Engineering, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria
  • Steven Emenike Atuegbunam 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

Keywords:

Aluminium Alloy A356, Cow Horn Particles, Surface Roughness, Machining, Response Surface Methodology (RSM)

Abstract

The increasing demand for lightweight, cost-effective, and sustainable engineering materials has led to growing interest in natural particle-reinforced metal matrix composites. Aluminium Alloy A356, widely used in automotive and aerospace industries, was reinforced with cow horn particles to enhance mechanical performance while promoting waste utilization. The study utilized Aluminium Alloy A356 reinforced with cow horn particulates (0–20%) fabricated by spark plasma sintering at 550 °C under 30 MPa. High-Speed Steel (HSS) and High-Carbon Steel (HCS) cutting tools were employed for machining tests. Experiments were conducted on a Universal Turning Machining Centre with supporting equipment including surface testers, weighing balance, crucibles, stirrers, and moulds. Machining parameters cutting speed (500–900 RPM), depth of cut (0.5–1.5 mm), and feed rate (0.15–0.25 mm) were varied to study effects on tool wear rate, material removal rate, and surface roughness. Response Surface Methodology (RSM) was applied for optimization and process modelling. Results revealed that cutting speed and depth of cut significantly influenced surface roughness, while feed rate had minimal effect. Surface roughness ranged from 119.09 to 168.47 mm (mean 145.63 mm). Statistical analysis showed cutting speed (p = 0.0039) and depth of cut (p = 0.0384) significantly influenced roughness, while feed rate was insignificant. The predictive RSM model demonstrated strong accuracy (R² = 0.9818; Adj. R² = 0.8907), showing close agreement between predicted and actual values. The findings highlight the potential of A356/cow horn composites for sustainable manufacturing applications with optimized machinability.

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Published

22-03-2025

Issue

Vol. 5 No. 3 (2025)

Section

Articles