Faculty Publications
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Item Measurement of WEDM performance characteristics of aero-engine alloy using RSM-based TLBO algorithm(Elsevier B.V., 2021) Sharma, P.; Dupadu, D.; Narendranath, N.Alloy-706 is a newly introduced exceptional class of turbine wheel alloy for high-performance aero-engine. To precisely measure the WEDM performance of complex aero-engine parts, the RSM-based TLBO algorithm is proposed in the current study. Morphology, topography, recast layer thickness, and roughness parameters of machined surface are studied to examine the surface integrity of aero-engine components. Better surface morphology, smoother topography, low roughness value, and minimum recast layer are observed at low pulse duration, high pulse-off period, and high servo voltage. RSM is used for statistical modeling of removal rate and average roughness. Then, these models are used in TLBO algorithm for individual and multiple performance optimizations. The Pareto optimal solutions are obtained for lower roughness value and highest removal rate. The microscopic investigation represents a considerable number of melted droplets, micro-holes, and craters on the WEDM-cut surface due to high energy discharge pulses followed by improper flushing of molten material. © 2021 Elsevier LtdItem MOGA and TOPSIS-based multi-objective optimization of wire EDM process parameters for Ni50.3-Ti29.7-Hf20 alloy(Elsevier Ltd, 2023) Balaji, B.; Narendranath, N.Conventional machining techniques face challenges in processing Ni-Ti-Hf alloys, which exhibit superior properties and are increasingly considered promising materials for high-temperature shape memory actuator applications. Thus, this article focuses on investigating the effect of Wire Electric Discharge Machining (WEDM) input parameters, namely discharge time (TON), pause time (TOFF), gap voltage (SV), and wire travel speed (WF), on the surface quality and shape memory properties of these alloys. These parameters were optimized to obtain a better removal rate (MRR) and surface finish quality (Ra) by employing a hybrid approach of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Multi-Objective Genetic Algorithm (MOGA). TON emerged as the most influencing parameter for both MRR and Ra, and the sample machined using optimal parameter setting, which had a MRR of 5.287 mm3/min and Ra of 2.335 µm, showed better surface quality with fewer surface defects and irregularities, lower recast layer thickness of 10.057 µm, and better shape memory properties with less than 15 % deviation in their latent heat of transformation values and a less than 5ºC change in their austenite and martensite transformation temperature values, which indicates MOGA was successful in finding a trade-off between the two responses. © 2023 Elsevier Ltd