Abstract
Surface finish and increased productivity (increased material removal rate (MRR)) are two premier performance parameters in machining and determination of optimal cutting parameters influencing the machining performance is paramount for different cutting conditions, cutting tools and workpiece materials. This study focuses on optimising a set of cutting parameters (cutting speed, feed rate and depth of cut) during dry turning operation of AISI H13 tool steel using a brazed uncoated tungsten carbide tip as the cutting tool material. Taguchi's L9 orthogonal array was employed for minimising surface roughness and maximising MRR based on signal-to-noise ratio results. Analysis of variance (ANOVA) conducted showed that the feed rate affected the performance responses (surface roughness and MRR) the most. Additionally, artificial neural network (ANN) and regression models were developed for both surface roughness and MRR, which showed promising prediction capability for calculating the surface roughness and MRR at any combination of cutting speed, feed and depth of cut.
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