Faculty Publications
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Item Parametric Analysis and Response Surface Optimization of Surface Roughness and Cutting Rate in the Machining Using WEDM(Springer Science and Business Media Deutschland GmbH, 2022) Manoj, I.V.; Narendranath, S.Nickelvac HX is an amalgamation of nickel, chromium, iron, molybdenum etc. As nickel-based alloys have high-temperature strength they can be used in many applications like afterburners, blades of turbines, turbocharges, submarines parts etc. Wire electric discharge machining a non-contact spark machining was found to be the most precise machining process. Among the WEDM parameters, different process parameters like servo voltage, pulse on time, cutting speed override and pulse off time were employed for the examination. It was noticed that both response characteristics increased with the increase in cutting speed override and pulse on-time. In the case of servo voltage and pulse off time, as it was increased the cutting rate and surface roughness diminished. The effects of cutting rate on surface roughness and microhardness were analyzed. The response surface optimization was employed for optimizing surface roughness and cutting rate as it controls product quality. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Investigation on the Effect of Variation in Cutting Speeds and Angle of Cut During Slant Type Taper Cutting in WEDM of Hastelloy X(Springer, 2020) Manoj, I.V.; Joy, R.; Narendranath, S.Nickel-based superalloys are classified under difficult to machine materials due to its higher affinity to tool materials and low thermal diffusivity. Wire electric discharge machining (WEDM) is a spark eroding technique for precise machining of such superalloys with complex machining geometries. Tapering in WEDM has many disadvantages like wire break, angular inaccuracies and dielectric distribution for better surfaces. In this paper, a unique method was developed and employed to achieve taper surface by tilting the workpiece using a slant type taper fixture for machining of tapered surfaces. Different aspects like cutting thickness, surface roughness, slant angle, surface crack density and width of cut were examined for five distinct cutting speed parameters at different angles, namely 0°, 15°, 30°, 45° and 60°. In the present research work, Hastelloy X was machined using zinc-coated copper wire and cutting speed was ranged between 0.16 and 2.49 mm/min. The slant angle was observed to be independent of cutting speed, and it was influenced by wire vibration, manufacturing imprecisions of slant fixture. It was found that as the cutting speed increases, surface crack density and surface roughness also increase. It was observed that both the parameters increased with the increase in the angle of cut from 0° to 60° although the cutting speed decreased. © 2019, King Fahd University of Petroleum & Minerals.Item Slant type taper profiling and prediction of profiling speed for a circular profile during in wire electric discharge machining using Hastelloy-X(SAGE Publications Ltd, 2021) Manoj, I.V.; Narendranath, S.Hastelloy-X a nickel-based alloy used in nozzles, flame holders, turbine blades, turbocharges, jet engine tailpipes, afterburner components etc. having complex tapering profiles. Wire electric discharge machining proves to be the most beneficial machining technique as it provides required accuracy for the components. In the present research, a slant type taper fixture is employed for achieving taper angles as convention tapering have many hindrances like wire bend, angular inaccuracy, guide wear, insufficient flushing and wire breakage etc. and machining a simple circular profile on Hastelloy-X. The behaviour of different output parameters like profiling speed, surface roughness, profile areas, microhardness and recast layer were investigated for various input parameters for machined taper components at 0°, 15° and 30°. The cutting speed override parameter influenced most on the profiling speed and surface roughness. The wire offset parameter was found to be the most significant factor in the case of circular profile areas that were machined. The variation of different output parameters to profiling/cutting speed and taper angle was also highlighted. It is found the recast layer decreased which indicated lesser thermal degradation at higher taper angles at different profiling parameters. This is also validated by the microhardness where the machined surface hardness of taper angular profiles was found to be greater than the 0° profiles. The artificial neural networks and adaptive neuro-fuzzy interference system were used for the prediction of profiling speed. The adaptive neuro-fuzzy interference system was found better in prediction as the percentage error varies between 0–5 per cent. In conclusion, the profiling speed influences both on the accuracy and surface of machined taper circular profiles. © IMechE 2021.Item Examination of Machining Parameters and Prediction of Cutting Velocity and Surface Roughness Using RSM and ANN Using WEDM of Altemp HX(Hindawi Limited, 2022) Manoj, I.V.; Soni, H.; Narendranath, S.; Mashinini, P.M.; Kara, F.The Altemp HX is a nickel-based superalloy having many applications in chemical, nuclear, aerospace, and marine industries. Machining such superalloys is challenging as it may cause both tool and surface damage. WEDM, a non-contact machining technique, can be employed in the machining of such alloys. In the present study, different input parameters which include pulse on time, wire span, and servo gap voltage were investigated. The cutting velocity, surface roughness, recast layer, and microhardness variations were examined on the WEDMed surface. The genetic algorithm was used to optimize the cutting velocity and surface roughness, thereby improving the overall quality of the product. The highest recast layer values were recorded as 25.8 μm, and the lowest microhardness was 170 HV. Response surface methodology and artificial neural network were employed for the prediction of cutting velocity and surface roughness. Artificial neural network prediction technique was the most efficient method for the prediction of response parameters as it predicted an error percentage lesser than 6%. © 2022 I. V. Manoj et al.Item Optimization and Prediction of Responses Using Artificial Neural Network and Adaptive Neuro-Fuzzy Interference System during Taper Profiling on Pyromet-680 Using Wire Electric Discharge Machining(Springer, 2023) Manoj, I.V.; Manjaiah, M.; Narendranath, S.In the present study, taper cutting is performed with the aid of a uniquely designed fixture. This is attempted to avoid the difficulties in tapering using wire electric discharge machining like wire break, dimensional error, guide wear, non-uniform flushing and low surface quality. An investigation of output parameters was made during taper machining using a fixture. The cutting rate (CR) and surface roughness (SR) were considered for response surface optimization (RSM) as they were important response parameters that indicate the quality of a machined component. It is observed that servo gap voltage and pulse act contrastingly on the output parameters. For achieving a trade-off of input parameters with output responses, RSM optimization is selected during taper profiling. There were 3-5% variations for both CR and SR when compared to experimental and RSM optimal values. The tapered profile slots of different angles like 0°, 15° and 30° were machined on Pyromet-680 using optimal machining parameters. The effect of different profiling parameters like wire distance between guides (WD), dwell time (DT), profile offset (PO) and cutting speed override (CO) on output responses like CR and SR was analyzed. Adaptive neuro-fuzzy interference system (ANFIS) and artificial neural network (ANN) models have been established for the prediction of the output responses. The validation is performed by experimentation, and the prediction errors ranged from 0 to 5% for both the responses CR and SR in ANFIS models. So ANFIS models proved to be the most efficient as there is an improvement of 45-50% in prediction compared to ANN models. © 2022, ASM International.