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All Journal Journal Majelis Paspama BIOS: Jurnal Informatika dan Sains
Francis Chukwunonso Okeke
Department of Mechanical Engineering, Faculty of Engineering, Nnamdi Azikiwe University, Awka, Anambra state, Nigeria
Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Maximization of Material Removal Rate in the Machining of A356/Cow Horn Particle Composites Using Response Surface Methodology Sunday Chimezie Anyaora; Francis Chukwunonso Okeke; Ikenna Theophilus Odoh; Onyeka Noel Anyali; Chibuzo Ndubuisi Okoye
BIOS: Jurnal Informatika dan Sains Vol. 3 No. 2 (2025): BIOS: Jurnal Informatika dan Sains, October 2025
Publisher : Sean Institute

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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.
Management of Tool Wear Mechanisms in Machining Aluminium Alloy A356/Cow Horn Particle Composite Sunday Chimezie Anyaora; Chidozie Chukwuemeka Nwobi-Okoye; Francis Chukwunonso Okeke; Onyeka Noel Anyali; Ikenna Theophilus Odoh
Journal Majelis Paspama Vol. 3 No. 02 (2025): Journal Majelis Paspama, 2025
Publisher : Journal Majelis Paspama

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This work presents the modelling and optimization of the cutting parameters in machining operations of aluminium alloy A356/cow horn particles (CHp) composite. In order to enable manufacturers to maximize their gains from utilizing hard turning, an accurate model of the process must be constructed. In course of the work, an attempt was made to develop mathematical models for relating the Tool Wear Ratio (TWR) to machining parameters (feed rate, depth of cut and cutting speed). To achieve this, A356/cow horn particles (CHp) composite was used to investigate the tool wear using RSM with 19 runs. A design of experiment was generated using the Optimal custom design techniques in Response Surface Methodology (RSM) from the Design Expert Software 11.0. After the optimization, the results from the ANOVA tables of the tool wear, surface roughness and Material removal rate showed that some models were significant with the probability value (P-value) 0.0203, 0.0412. Tool wear ranged from 0.00011–0.00092 mg/mm, with the lowest at high feed rate (0.25 rev/mm), high cutting speed (900 RPM), and depth (1.5 mm). Feed rate (p = 0.0436), cutting speed (p = 0.0008), and depth of cut (p = 0.0137) significantly influenced tool wear. The regression model achieved strong fit (R² = 0.9952, Adj R² = 0.9714) with low error (Std. Dev. = 0.0001). Predicted versus actual plots confirmed reliability, with 95% CI (0.000196–0.000530 mg/mm) validating precision and stability. In order to enable manufacturers to maximize their gains from utilizing hard turning, an accurate model of the process have been constructed.
Co-Authors Chibuzo Ndubuisi Okoye Chidozie Chukwuemeka Nwobi-Okoye Ikenna Theophilus Odoh Onyeka Noel Anyali Sunday Chimezie Anyaora