Optimisation of End Milling Parameters on Aluminium/SiC Composites Using Response Surface and Artificial Neural Network Methodologies

Abstract

In recent years, the utilization of metal matrix composites (MMC) have increased in engineering fields of automobile, aviation, aerospace, construction and microelectronics. In conjunction with innovations in these advanced materials, machining them to obtain good dimensional accuracy and surface integrity has become a challenge. In this present work, 15 % (by weight) SiC particle reinforced aluminium is synthesized by stir casting technique. The synthesized MMC is end milled using Ø16 mm carbide end mill in a CNC milling machine and the machining parameters explicitly cutting speed, feed rate and depth of cut are optimised for minimum surface roughness and total force acting on the tool using response surface methodology (RSM) and artificial neural network (ANN). The un-coated cutting tool insert was compared with the nanocomposite coated insert on tool wear at optimised cutting conditions.