Optimization of Process Parameters That Affects Hole Quality Characteristics in Drilling Of Syntactic Foams Using Artificial Neural Network Model and Particle Swarm Optimization

The drilling is known popular and common mechanical operation which is performed frequently during manufacturing of automotive and aerospace related parts, it is important to study the manufacturability of any novel engineering materials developed and subjected for machining more often as drilling a hole which is required for the purpose of assembly. In this regard the proposed work aims at study of drilling process parameters on newly developed class of polymer based composites. Cenosphere or fly ash acts as a filler material which is a by-product of coal combustion used along with epoxy to produce syntactic foam. The preparation of cenosphere based epoxy composites is done using hand layup technique. The controllable factors in the presented study identified such as cutting speed, feed rate and drill diameter are suspected to have effect on drilled hole quality characters, such as surface roughness and delamination factor. Syntactic foams of 40% by weight are fabricated. A complete factorial design has been selected to perform drilling experiments and recommended drilling quality aspects are analyzed using RSM based quadratic relationships. The relationship of RSM revealed that Ra and Fd are varying non-linearly for selected input variables. The optimization of selected parameters is performed and it is seen that Ra of 0.2 microns and Fd 0.99 are achieved at cutting speed of 55m/min, feed rate of 0.05mm/rev and using drilling tool of diameter 16mm.