Optimisation of wear and mechanical behaviour of hot-rolled Al7049/n-B ₄ C nanocomposite using Taguchi and ANN modelling approach

Next-generation aerospace and automotive components require significant weight reduction without compromising physical properties, mechanical characteristics, or safety standards. This study demonstrates that hot-rolling and increased n-B4C nanoparticles enhance the hardness (82 VHN), tensile strength (136 MPa), machining force (70.35%), and corrosion resistance (36.11%) of stir-cast aluminium (Al-7049) matrix composites. The dry sliding wear characteristics of as-cast and hot-rolled n-B4C/Al-7049 composites were analysed under varying conditions using Taguchi Design of Experiments (DOE) and Artificial Neural Networks (ANN). ANOVA study revealed that n-B4C (74.20%) was the most important factor affecting wear resistance, followed by hot rolling (13.05%) and load applied (N) (9.34%). The confirmatory tests indicated that the wear rate of the produced nanocomposites exhibited a maximum deviation of 5.45%, which is within the acceptable range. Ultimately, the developed model accurately predicts nanocomposite behaviour for practical applications, thereby helping industries develop cost-effective manufacturing solutions.