Faculty Publications

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Author: search.filters.author.Mukunda, P.G.Subject: search.filters.subject.Analysis of variance (ANOVA)

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    Experimental investigation on thermally enhanced machining of high-chrome white cast iron and to study its machinability characteristics using Taguchi method and artificial neural network
    (Springer-Verlag London Ltd, 2014) Ravi, A.M.; Murigendrappa, S.M.; Mukunda, P.G.
    Machining of hard-to-wear materials such as high-chrome white cast iron (HCWCI) and high-manganese steels is an uphill task when conventional route followed. Alternatively, thermally enhanced machining (TEM) can be used to minimize the tooling cost very effectively. This paper presents the detailed study of TEM of HCWCI in which the effect of cutting parameters and surface temperature of the stock material on machinability characteristics (cutting forces and surface roughness) are analyzed using ANOVA and artificial neural network (ANN). The experimental work was conducted to follow Taguchi techniques. HCWCI is finding newer applications in mining; mineral processing industries were the workpiece in the machining studies using cobalt-based cubic boron nitride insert tool. Localized heat was added at the tool-work interface which softens the metal and eases the machining operation. The influences of the control factors on the process responses have been analyzed using analysis of variance (ANOVA), and the results are correlated using ANN. Linear regression was used to establish the relation between the control parameters and the process responses. The results show that TEM causes easy shearing of the material, leading to the reduction in cutting forces with expected improvement in tool life and surprisingly good surface finish. The confirmation tests suggest both second-order regression and ANN which are better predictive models for quantitative prediction of TEM of HCWCI, and ANN is more accurate of the two. Also, it was proved that oxy-LPG flame heating is an economical option compared to laser-heated machining in hard turning process. © 2014 Springer-Verlag London.
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    Machinability investigations on high chrome white cast iron using multi coated hard carbide tools
    (Springer India sanjiv.goswami@springer.co.in, 2014) Ravi, A.M.; Murigendrappa, S.M.; Mukunda, P.G.
    This study investigated the performance of multilayer hard coated carbide tool and multi-response optimization of the turning process for an optimal parametric combination to yield the minimum cutting forces and machining power with a maximum material removal rate (MRR) using Taguchi and artificial neural network (ANN) methods. In recent times, high chrome white cast iron finds increasing applications in aerospace, mining, mineral process industries. Its machinability using carbide insert (TiC/TiCN/Al2O3) cutting tool has been studied. The influences of cutting parameters on the cutting forces, MRR and machining power of the process have been analyzed using analysis of variance and the results are correlated using ANN. Linear regression method was used to establish the relation between the cutting parameters and the process responses. The confirmation test reveals that, the accuracy of prediction of ANN is better than that of the regression analysis. In view of the good performance of the carbide tools (at optimum conditions), it can replace the cosly CBN, with improved economic benefits. © 2014 Indian Institute of Metals.
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    Experimental and Analytical Based Investigations on Machinability of High-Chrome White Cast Iron Using CBN Tools
    (Springer, 2015) Ravi, A.M.; Murigendrappa, S.M.; Mukunda, P.G.
    High-chrome white cast iron (HCWCI) is one of the hardest metals used in the process and mining industries faces tough challenge in metal cutting. Focusing on this issue, influence of cutting parameters (e.g., cutting speed, depth of cut, feed rate) on machinability characteristics (e.g., cutting forces, surface roughness, material removal rate, machining power) of HCWCI has been investigated by experimentally and analytically using cubic boron nitride (CBN) cutting tools. Experimentation is carried out in conjunction with the Taguchi techniques and the influence of each cutting parameter of the process has been analyzed by analytical tools; analysis of variance, regression technique and artificial neural networks (ANNs). The study reveals depth of cut has the highest contribution on the cutting forces, and cutting speed on surface roughness and machining power. The confirmation test identifies both regression and ANN techniques are the most effective tools to evaluate machinability characteristics of HCWCI. Further, the CBN cutting tool exhibits excellent performance in machining of HCWCI. © 2014, The Indian Institute of Metals - IIM.