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

Abstrak

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