Performance analysis of multi-spindle drilling of Al2024 with TiN and TiCN coated drills using experimental and artificial neural networks technique

Abstract

Multi-spindle drilling simultaneously produces multiple holes to save time and increase productivity. The assessment of hole quality is important in any drilling process and is influenced by characteristics of the cutting tool, drilling parameters and machine capacity. This study investigates the drilling performance of uncoated carbide, and coated carbide (TiN and TiCN) drills when machining Al2024 aluminium alloy. Thrust force and characteristics of hole quality, such as the presence of burrs and surface roughness, were evaluated. The results show that the uncoated carbide drills performed better than the TiN and TiCN coated tools at low spindle speeds, while TiCN coated drills produced better hole quality at higher spindle speeds. The TiN coated drills gave the highest thrust force and poorest hole quality when compared with the uncoated carbide and TiCN coated carbide drills. Additionally, a multi-layer perceptron neural network model was developed, which could be useful for industries and manufacturing engineers for predicting the surface roughness in multi-hole simultaneous drilling processe