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Statistical analysis of different machining characteristics of EN-24 alloy steel during dry hard turning with multilayer coated cermet inserts
Das A, Patel S.K, , Biswal B.B.
Published in Elsevier BV
Volume: 134
Pages: 123 - 141
This research article describes the experimental and statistical analysis of flank wear, material removal rate, tool tip temperature, surface roughness parameters (i.e. Ra, Rz and Rt), chip morphology, chip thickness and dimensional deviations (i.e. circularity and cylindricity). Taguchi's L27 orthogonal array has been selected for experimental design and ANOVA (analysis of variance) has been used to study the significance of cutting parameters on the responses. The experimental study exhibits that depth of cut is the predominant machining parameter influencing surface roughness followed by feed rate and cutting speed. Similar result was found for flank wear and dimensional deviations. However, cutting speed was found to be the most crucial input parameter for tool the tip temperature whereas feed rate was the most notable input variable for material removal rate (MRR). Abrasion and chipping are two major wear mechanisms found for flank wear in this study. During chip morphology study using scanning electron microscope material side flow, serrations, shear band, shear cracks, smooth and rough surfaces are observed. Further, the effect of flank wear on surface roughness parameters, dimensional deviations and effect of MRR on different patterns of crater wear were studied. For each response, mathematical model was developed with regression analysis and the models having higher R-Sq values show favorable relationship between predicted and experimented values. Finally, the optimal combination of machining parameters has been obtained using neuro-genetic algorithm. © 2018 Elsevier Ltd
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