Optimization and prediction of machining parameters in nanoparticle-reinforced FMLs using AI techniques

This study focuses on optimizing and predicting the drilling performance of Fiber Metal Laminates (FMLs) reinforced with BaSO4 nanoparticles, achieved by adjusting parameters like spindle speed, feed rate, and tool diameter. Key responses-thrust force, torque, delamination, and surface roughness-were evaluated to enhance machinability. Using Central Composite Design, optimal parameters were identified: a spindle speed of 3000 rpm, feed rate of 10 mm/min, and tool diameter of 6 mm. Under these conditions, thrust force decreased by 51.92%, surface roughness improved to Ra = 2.3 mu m, and delamination reduced by 21%. A two-layer feed- forward neural network in MATLAB 2023a accurately predicted outcomes with a Mean Square Error (MSE) of 1.4025e-05, demonstrating high correlation with experimental data. The inclusion of BaSO4 nanoparticles significantly improved the FMLs' mechanical and thermal properties, enhancing machinability. This integrated approach of experimental optimization and predictive modeling provides a strong framework for precision machining of hybrid composites. The findings are especially promising for aerospace and automotive industries, where defect-free, high-quality FML machining is essential, positioning this method as a key advancement in nanoparticle-reinforced composite drilling.