Conference Papers

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Author: search.filters.author.Rao, S.S.Subject: search.filters.subject.Turning

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    A pressured steam JET approach to tool wear minimization in cutting of metal matrix composites
    (Trans Tech Publications Ltd, 2007) Anjaiah, D.; Shetty, R.; Pai B, R.; Vijaya, M.V.; Rao, S.S.
    Metal matrix composites (MMCs) have been found to possess tremendous prospective engineering applications that require materials offering a combination of lightweight with considerably enhanced mechanical and physical properties. However, the applications of MMCs are limited by their poor machinability which is a result of their highly abrasive nature that causes excessive wear to the cutting tools. In this study, an investigation into the mechanism of the tool wear in cutting of MMCs is carried out. It is found that during cutting of an MMC, the tool cutting edge will impact on the reinforcement particles. The impacted particles will then either be dislodged from the matrix, doing no harm to the tool, or be embedded into the matrix, ploughing on the tool flank and causing excessive tool flank wear. According to this tool wear mechanism, a pressured steam jet approach is developed for the minimization of the tool wear by preventing the impacted reinforcement particles from being embedded in the workpiece matrix. Experimental tests for cutting of SiC-aluminum MMC using cubic boron nitride (KB-90) and polycrystalline diamond (KP-300) tool inserts with the aid of the pressured steam jet are conducted. The results show that from full factorial design of experiments the effect of the pressured steam jet plays a significant role on the tool wear followed by tool inserts and depth of cut. The working mechanism of the pressured steam jet method and the experimental testing results are discussed in detail.
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    Prediction of surface finish and optimization of machining parameters in turning
    (2012) Prasad, D.; Krishna, P.; Rao, S.S.
    Surface roughness plays a crucial role in the functional capacity of machined parts. In this work, experiments were carried out on a conventional lathe for different cutting parameters namely feed, spindle speed, depth of cut and tool nose radius according to Taguchi Design of Experiments. Radial acceleration readings were taken with an accelerometer. Optimum cutting parameters and their level of significance were found using Taguchi analysis (ANOVA). Regression analysis was carried out to identify whether the experimental roughness values have fitness characteristic with the process parameters. Recurrence Plots (RP) were obtained using the sensor signals which determine surface roughness qualitatively and Recurrence Quantification Analysis (RQA) technique was used to quantify the RP obtained. Surface finish was predicted using a feed forward back propagation neural network with RQA parameters, cutting parameters and acceleration data as inputs to the network. The validity and reliability of the methods were verified experimentally. © (2012) Trans Tech Publications.