Faculty Publications
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Item Influence of process parameters on material removal rate and surface roughness in WED-machining of Ti50Ni40Cu10 shape memory alloy(Inderscience Publishers, 2016) Manjaiah, M.; Narendranath, S.; Basavarajappa, S.; Gaitonde, V.N.Among the shape memory alloys (SMAs), TiNi SMAs have been typically used as the functional elements in the larger part of the industries due to exceptional properties like super elasticity and shape memory effect. However, traditional machining of these alloys is fairly complex due to these properties. The non-traditional machining process like electric discharge machining (EDM) exhibits outstanding capability in machining of these alloys. The poor selection of machining parameters may cause increased roughness of workpiece and lesser material removal rate. Hence, an effort has been made in the present work to explore the effects of three process parameters, such as pulse on time, pulse off time and servo voltage in wire electric discharge machining (WEDM) of Ti50Ni40Cu10 shape memory alloy (SMA) using zinc coated brass wire electrode on material removal rate and surface roughness using response surface methodology (RSM)-based mathematical models. The experiments were planned as per central composite design (CCD). The investigations revealed that pulse on time and servo voltage have predominant effects in maximising material removal rate and minimising surface roughness. The best combination of the process parameters for multi-response optimisation was obtained through desirability function. ©2016 Inderscience Enterprises Ltd.Item Modeling and Optimization of Wear Rate of AISI 2507 Super Duplex Stainless Steel(Springer Netherlands rbk@louisiana.edu, 2019) Davanageri, M.B.; Narendranath, S.; Kadoli, R.The present work attempts to study the parameters influencing wear, namely, applied load, heat-treated temperature, sliding velocity, and sliding distance using statistical Design of Experiments (DOE) and Response Surface Methodology (RSM). The wear behavior of super duplex stainless steel was evaluated under dry sliding conditions. A three-level Central Composite Design (CCD) based non-linear model was used to establish input-output relationship based on the collected experimental input-output data. Surface plots were used to study the influence of applied load, heat-treated temperature, sliding distance, and sliding velocity on the wear rate of super duplex stainless steel. The wear rate was observed to vary nearly non-linearly with applied load and linearly with the rest of the input parameters. Analysis of Variance (ANOVA) was conducted to test the statistical adequacy of the non-linear model developed. Applied load and heat-treated temperature were found to have a more positive contribution towards the wear rate than other parameters. Although the sliding velocity had a negligible effect, its interaction with applied load and heat-treated temperature had a significant impact on the wear rate. The regression equation developed was tested for its prediction precision with the help of 20 test cases. Further, attempts were also made to determine the optimum combination of input parameters that minimize the wear rate using the Desirability Function Approach (DFA). The objective of minimizing the wear rate was met with the highest desirability value of 1. Confirmation experiments were conducted for the determined optimal set of input parameters of 20 test cases resulting in an average absolute percent deviation in prediction of 6.34% and 5.58%. © 2018, Springer Science+Business Media B.V., part of Springer Nature.