Machinability Studies and Artificial Neural Network Modeling of Turning Al-SiC (10p) Metal Matrix Composites

The paper presents the results of an experimental investigation on the machinability of fabricated Aluminum metal matrix composite (A356/SiC/10p) during continuous turning of composite rods using medium grade Polycrystalline Diamond (PCD 1500) inserts. MMC’s are very difficult to machine and PCD tools are considered by far, the best choice for the machining of these materials. Experiments were conducted at LMW-CNC-LAL-2 production lathe using PCD 1500 grade insert at various cutting conditions and parameters such as surface roughness, specific power consumed, and tool wear were measured. Machining was continued till the flank wears land on the tool crossed 0.4 mm. The influences of cutting speed on the insert wear and built-up edges (BUEs) formation were studied. The present results reaffirm the suitability of PCD for machining MMCs. Though BUE formation was observed at low cutting speeds, at high cutting speeds very good surface finish and low specific power consumption could be achieved. An Artificial Neural Network (ANN) model has been developed for prediction of machinability parameters of MMC using feed forward back propagation algorithm. The various stages in the development of ANN models VIZ. selection of network type, input and output of the network, arriving at a suitable network configuration, training of the network, validation of the resulting network has been taken up. A 2-9-3 feed forward neural network has been successfully trained and validated to act as a model for predicting the machining parameters of Al-SiC (10p) -MMC. The ANN models after successful training are able to predict the surface quality; specific power consumption and tool wear for a given set of input values of cutting speed and machining time.