Conference Papers

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

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Author: search.filters.author.Naik, G.M.Subject: search.filters.subject.Wire EDM

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    An investigation on effects of wire-EDT machining parameters on surface roughness of INCONEL 718
    (Elsevier Ltd, 2019) Naik, G.M.; Anjan, B.N.; Badiger, R.I.; Bellubbi, S.; Mishra, D.
    This paper studied the effects of machining parameters on surface roughness of wire EDT of INCONEL 718 super alloy. The investigated machining parameters were rotational speed, pulse-on time, pulse-off time, servo voltage, wire feed rate and flushing pressure. Analysis of variance (ANOVA) technique was used to find out the most significant parameters affecting the surface roughness. Results from ANOVA show that pulse-on time is significant variables to surface roughness of wire-EDT INCONEL 718 alloy. The surface roughness of the test specimen increased as these variables increased. Lastly, regression model was developed using a regression method to formulate the machining parameters to the surface roughness. The developed model was validated with an optimal setting parameters and the maximum prediction error of the model was less than 8%. © 2019 Elsevier 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