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

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 (F_d) through digital image analysis. Predictive models for F_d 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 F_d 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.