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Optimization of Cutting Parameters for AISI H13 Tool Steel by Taguchi Method and Artificial Neural Network
Abstract
In the present study an attempt has been made to investigate the effect of cutting parameters (cutting speed, feed rate, and depth of cut) on surface roughness and material removal rate (MRR) during dry turning operation of AISI H13 tool steel as per Taguchi's experimental design technique using an L9 orthogonal array. Signal to noise ratio (S/N) results and Analysis of Variance (ANOVA) were employed in order to investigate the optimal and significant cutting characteristics of H13 tool steel respectively. This paper focuses on optimizing the cutting parameters for minimum surface roughness and maximum MRR of H13 tool steel using high speed steel (HSS) as the cutting tool during turning. The results indicated that feed has a significant influence on surface finish and depth of cut on MRR when turning operation was carried out with HSS cutting tool. An artificial neural network model and regression equations were also developed to obtain minimum surface roughness and maximum MRR at different cutting conditions.
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