Comparative study of surface roughness prediction using neural-network and quadratic-rotatable-central-composite-design

The act of sustainable manufacturing lies in the response's prediction analysis, such as surface roughness during machining operations with nano-lubricant. This research focuses on developing a mathematical model to predict experimental results AA8112 alloys obtained end-milling process an eco-friendly study employed vegetable oil base cutting fluid (copra oil) and Titanium-dioxide (TiO2) nanoparticles additive. was carried out five parameters. analysis done backpropagation feed-forward neural network (BPNN) quadratic rotatable central composite design (QRCCD). show that BPNN predicted 99.85%, QRCCD 91.1%. error percentage from both analyses is 0.2% 0.9% QRCCD. Therefore, application has proven viable predicting operations. It will also improve industry's productivity eliminate high rate waste materials machining.