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, 2025)
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.