Faculty Publications

Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736

Publications by NITK Faculty

Browse

Search Results

Now showing 1 - 4 of 4
  • Item
    A study on the influence of WEDM parameters on surface roughness, kerf width, and corrosion behavior of AZ31B Mg alloy
    (Elsevier Ltd, 2022) Chaitanya, V.H.; Sekar, P.; Narendranath, S.; Balaji, V.
    Wire electric discharge machining (WEDM) is a nontraditional machining process where the material is removed by the spark erosion technique. This technique is used to machine AZ31B, a biodegradable Magnesium alloy. In the present work impact of WEDM input parameters, namely pulse on time (Ton), pulse off time (Toff), servo voltage (SV), and wire feed (WF) on response characteristics is studied. The response characteristics considered are kerf width (KW), surface roughness (SR), and corrosion rate (CR). L9 orthogonal array by Taguchi's is employed as the design of experimentation. Taguchi's analysis implied that TON is the most influencing input parameter on the response characteristics. At a relatively lower TON setting (105 μs), comparatively lesser kerf width (335.894 μm), lower surface roughness (3.069 μm), and lower corrosion rate (0.95 mm/year) are exhibited by the machined specimens. From the main effects plots using signal-to-noise ratios, it is understood that the values of response characteristics increased with an increase in TON value. It is due to the increase in discharge at the more pulse on time duration. It is also understood that a surface with relatively better surface finish exhibited better corrosion resistance. With the help of regression equations, the relation between response characteristics and input parameters is built. © 2022 The Combustion Institute. Published by Elsevier Inc. All rights reserved.
  • Item
    Optimization of wire-EDM process parameters for Ni–Ti-Hf shape memory alloy through particle swarm optimization and CNN-based SEM-image classification
    (Elsevier B.V., 2023) M, R.V.; Balaji, V.; Narendranath, S.
    Shape memory alloys (SMAs) have the unique ability to regain their shape under specified conditions, making them extremely useful for a wide range of applications. However, non-traditional machining techniques could be more effective for high-temperature SMAs as they provide better shape recovery properties than conventional methods, necessitating this study's unconventional wire electric discharge machining procedure. The surface morphology of Ni–Ti-Hf-based alloys was examined using scanning electron microscopy. A convolutional neural network model was used to categorize SEM images based on the material removal rate, utilizing the pixel intensity histogram of the processed images. The study discovered that the material remelted as the discharge energy increased, leaving lumps and globules on the surface. The size of debris lumps, pores, and globules increased with increasing Ra values. Moreover, widening the inter-electrode gap by expanding the servo-voltage facilitated efficiently removing debris from the machined site. The TOPSIS method for particle swarm optimization was employed to find the optimal solutions, yielding TON = (124.239, 116.228), TOFF = (54.532, 40.781), Servo voltage = (36.216, 43.766), and Wire-Feed = (2.157, 8). These results were validated through experimentation, with minimal error percentages of 2.01%, 2.046% (MRR), and 3.86%, 1.611% (Ra) for both sets of input parameters, respectively. © 2023
  • Item
    Influence of wire-electric discharge machining process parameters on surface integrity of Ni-rich Ni-Ti-Hf alloys
    (Institute of Physics, 2023) Balaji, V.; Narendranath, S.
    Ni-Ti-Hf Shape memory alloys (SMAs) have shown promising results in high-temperature applications in aviation, space and energy exploration, actuators, etc. In the past decade, extensive work has been carried out to understand the behavior of High-Temperature SMAs (HTSMAs). NiTi-based SMAs are grouped as hard-to-machine materials, and machining these materials through traditional methods leads to high tool wear, dimensional inaccuracy, degradation of Shape Memory properties, etc. Therefore, Non-Conventional machining processes are a better choice for machining these alloys. It is evident from previous studies that Wire Electric Discharge Machining (WEDM) yields better results compared to other processes. The current study investigates the effect of WEDM input variables such as servo gap voltage (SV), spark-ON duration (TON), wire electrode feed rate (WF), and spark-OFF duration (TOFF) on the machining of Ni-Ti-Hf HTMSAs. The surface integrity of the machined samples was analyzed by investigating characteristics like machined surface morphology, machined surface quality, subsurface microhardness, and recast layer thickness. TON emerged as the most critical parameter for surface roughness and Material Removal Rate. Various defects like micro-cracks, micro-pores, craters, and globules were found on the machined surfaces, and approximately 30% harder surface was found near the machined region. The average thickness of the recast layer observed for Hf-15 and Hf-20 samples was around 12 μm and 50 μm for samples with lower and higher discharge energies, respectively. © 2023 IOP Publishing Ltd.
  • Item
    Process parametric and performance characteristics study of WED machined Ni-Ti-Hf high-temperature shape memory alloys: an experimental and artificial intelligence approach
    (Institute of Physics, 2025) Balaji, V.; Sahu, R.K.; Narendranath, S.
    In recent years, to meet the shortcomings of the conventional machining of Ni-Ti-Hf shape memory alloy (SMA), Wire Electric Discharge Machining (WEDM) as one of the unconventional machining methods, has emerged as the preferred method for processing SMAs. Therefore, in this study, according to the Response Surface Methodology-based Central Composite Design layout, WED machining of Ni-Ti-Hf SMA is carried out using the control parameters like spark time (SON), spark pause time (SOFF), gap voltage (Vg), and dielectric flow rate (FDL). A General Regression Neural Network (GRNN) model was used to predict the critical WEDM responses: material removal rate (MRR), surface roughness (Ra), and kerf width (KW). The GRNN model closely agrees with the experimental WEDM responses, resulting in a Mean Absolute Percentage Error below 2%. Field Emission Scanning Electron Microscopy result revealed a recast layer thickness of 10.64 ± 2.06 µm and 38.19 ± 9.55 µm for the samples with the lowest surface roughness (Ra) and highest MRR, respectively. The shape recovery test result shows a less than 4% reduction in recovery ratio post-WEDM. Further, electrochemical corrosion studies revealed that owing to these surface defects, the corrosion rate increased with higher discharge energy. The corrosion rate of the base material, low Ra sample, and high MRR sample were 0.0013 mm yr?1, 0.0128 mm yr?1, and 0.0334 mm yr?1, respectively. © 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.