Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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    Studies on the Effect of Process Parameters in Turning of Ti-6Al-4V Alloy Using Topsis
    (IOP Publishing Ltd, 2019) Rao, C.M.; Rao, S.S.; Herbert, M.A.
    Optimization of process parameters in turning process is the fundamental machining operation which leads to better machining performance. This study has applied Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method to obtain the optimum process parameters in turning of Ti-6Al-4V alloy using Polycrystalline Diamond (PCD) cutting tool insert. The process parameters chosen for optimization were cutting velocity, feed rate and depth of cut. The objective is to minimize cutting temperature, tool wear, and surface roughness. TOPSIS is employed to analyze the input parameters on output performance characteristics. Nine experiments were conducted under MQL (Minimum Quantity Lubrication) environment based on an L9 orthogonal array, respectively. The optimization results indicate turning Ti-6Al-4V alloy at cutting velocity of 150 m/min, the feed rate of 0.5 mm/rev and depth of cut of 0.5 mm as optimum parameters obtained by TOPSIS technique. From the Analysis of variance (ANOVA), it was identified that depth of cut parameter is the most influencing process parameter on turning performance characteristics. © Published under licence by IOP Publishing Ltd.
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    Optimizing machining responses of homologous TiNiCu shape memory alloys using hybrid ANN-GA approach
    (Elsevier Ltd, 2022) Roy, A.; Sachin, B.; Raghavendra, T.; Rao, C.M.; Naik, G.M.; Soni, H.; Mashinini, P.M.; Narendranath, S.
    Fabrication of shape memory alloys using wire electro discharge machining (WEDM) has gained popularity over the last few years. Most widely used machining parameters of WEDM process are pulse on time (Øon), pulse off time (Øoff), servo voltage (σ) and wire feed (ω). WEDM responses like material removal rate (MR), surface roughness (SR), kerf width (KW) and recast layer thickness (LT) have been evaluated by researchers to determine machining characteristics and are also considered for this study. These machining responses determine the quality of machining and are majorly influenced by thermal conductivity and melting temperature of the WEDM workpiece. Actuation behavior of shape memory alloys is a function of phase transformation characteristics which in turn depends on elemental composition of the selected alloys. Therefore, dissimilar machining responses of Ti50Ni40Cu10 and Ti50Ni25Cu25 have been observed even though similar machining input values were used. This study utilized artificial neural network (ANN) mapping to establish WEDM response function – which was used as fitness function to perform multi objective optimization using genetic algorithm (GA). It was found that ANN successfully predicted machining responses of selected homologous alloys and GA helped in identifying suitable input parameter values to optimize machining responses. © 2022