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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. © 2023Item 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 Machining Parameter Optimization of Wire Electrical Discharge Machining for Ni50.3Ti29.7Hf20 Alloy Using TOPSIS and Grey Wolf Optimization Technique(Springer, 2025) Bhaskar, M.; Balaji, V.; Narendranath, S.; Sahu, R.K.Ni50.3Ti29.7Hf20 is an alloy with shape memory characteristics that can withstand high temperatures. It possesses remarkable strength, hardness, and exceptional corrosion resistance. SMAs are well-suited for various applications, including automotive sensors, automobiles, aerospace technologies, robotics, actuators, and MEMS devices. However, its unique properties make it difficult to machine using conventional methods. Wire EDM is an unconventional machining process suited for difficult-to-machine materials like Ni-Ti-Hf alloy, providing high accuracy and precision and minimizing the risk of material damage. This paper focuses on the optimization of machining parameters, namely Discharge time (PON), Pause time (POFF), Gap voltage (GV), and Wire travel speed (WS) during WEDM of Ni-Ti-Hf shape memory alloy utilizing the TOPSIS and GWO techniques. The aim is to obtain optimal machining parameters for improving the machined Ni-Ti-Hf alloy’s material removal rate (MRR) and surface roughness (Ra). The optimal machining parameters from GWO were PON = 123.8 µs, POFF = 50 µs, WS = 2, and GV = 25. The predicted values of material removal rate and surface roughness are 4.22 mm3/min and 3.62 µm, respectively. The experimental verification demonstrates the proposed optimization approach's effectiveness, as the predicted values correlate strongly with the actual values. © ASM International 2023.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. 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