Optimizing the drilling performance of Syagrus romanzoffiana fiber biocomposites: minimizing delamination with RSM and ANN modeling

Abstract

This study evaluates the drilling performance of Syagrus romanzoffiana fiber-reinforced bio-epoxy biocomposites, focusing on reducing delamination for better industrial use. An experimental investigation was conducted on drilling composite laminates made by hand lay-up with 30% fiber content. The research examined how various factors—drill bit type (high-speed steel (HSS) and HSS coated with titanium nitride (HSS-TiN)), drill diameter (d, 5–10 mm), spindle speed (N, 800–1600 rpm), and feed rate (f, 50–150 mm/min)—influenced delamination damage, measured by the delamination factor (Fd) through digital image analysis. Predictive models for Fd were created using Response Surface Methodology (RSM) and an Artificial Neural Network (ANN). Results showed that the ANN model had higher predictive accuracy (R2 > 0.966, root mean square error (RMSE) < 0.032) than the quadratic RSM model. The analysis identified f as the most influential factor on delamination, followed by d. Additionally, HSS-TiN tools outperformed standard HSS bits. Optimization using a desirability function produced minimum Fd values of 1.02319 for HSS-TiN and 1.03199 for HSS at an f of 50 mm/min, N of 1419.49 rpm, and d of 10 mm. The RSM model was confirmed to be statistically significant through analysis of variance (p < 0.0001), which also revealed a notable interaction between f and N. These results indicate that the hole quality achievable in this biocomposite matches or surpasses that of carbon fiber-reinforced polymer and glass fiber-reinforced polymer under comparable dry drilling conditions. This evidence supports its potential for use in industries such as automotive, sporting goods, and non-critical aerospace components.