Faculty Publications
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Item A review on machining of titanium based alloys using EDM and WEDM(Institute of Problems of Mechanical Engineering mpm@def.ipme.ru, 2014) Manjaiah, M.; Narendranath, S.; Basavarajappa, S.The aim of this review is to present the consolidated information about the contributions of various researchers on the application of EDM and WEDM on titanium materials and subsequently identify the research gaps. The literature survey has been carried out from three perspectives such as application of EDM and WEDM on titanium materials, utilization of tools and techniques for correlating experimental results and application of products produced by EDM and WEDM. Three main research areas has been identified. First, the application of EDM and WEDM on titanium materials mainly TiNi based alloys. Second, the utilization of advanced tools and techniques such as artificial neural network (ANN), advanced particle swarm optimization (PSO) and tabu enhanced genetic algorithm (GA). Third, the study and analysis of surface integrity in EDM and WEDM on titanium materials. In addition, the paper has also evolved the future research directions. The paper has been concluded by indicating the future research directions for the research gaps identified during this literature survey.Item ANN and RSM modeling methods for predicting material removal rate and surface roughness during WEDM of Ti50Ni40Co10 shape memory alloy(AMSE Press 16 Avenue Grauge Blanche Tassin-la-Demi-Lune 69160, 2017) Soni, H.; Narendranath, S.; Ramesh, M.R.Present study exhibits the comparison between experimental and predicted values. Where response surface method (RSM) and artificial neural network (ANN) were used as predictor for the prediction of wire electro discharge machining (WEDM) responses such as the material removal rate (MRR) and surface roughness (SR) during the machining of Ti50Ni40Co10 shape memory alloy. It has been noticed from the literature survey that pulse on time and servo voltage are most important process parameters for the machining of TiNiCo shape memory alloy, hence there are five levels of these process parameters were chosen for the present study. For the present study selected alloy has been developed through vacuum arc melting and L-25 orthogonal array has been created by using Taguchi design of experiment (DOE) for experimental plan. During the present study ANN predicted values have been found to very close to experimental values compare to RSM predicted values, hence it can be say that ANN predictor gives more accurate values compare to RSM predicted values. © 2017 AMSE Press. All rights reserved.Item Condition monitoring of single point cutting tools based on machine learning approach(International Institute of Acoustics and Vibrations P O Box 13 Auburn AL 36831, 2018) Gangadhar, N.; Kumar, H.; Narendranath, S.; Sugumaran, V.This paper presents the use of multilayer perceptron (MLP) for fault diagnosis through a histogram feature extracted from vibration signals of healthy and faulty conditions of single point cutting tools. The features were extracted from the vibration signals, which were acquired while machining with healthy and different worn-out tool conditions. Principle component analysis (PCA) used to select important extracted features. The artificial neural network (ANN) algorithm was applied as a fault classifier in order to know the status of cutting tool conditions. The accuracy of classification with MLP was found to be 82.5 %, which validates that the proposed approach is an effective method for fault diagnosis of single point cutting tools. © 2018 International Institute of Acoustics and Vibrations. All Rights Reserved.Item Variation and artificial neural network prediction of profile areas during slant type taper profiling of triangle at different machining parameters on Hastelloy X by wire electric discharge machining(SAGE Publications Ltd, 2020) Manoj, I.V.; Narendranath, S.In the present research work, an in-house developed fixture is used to achieve taper profiles which avoids the disadvantages in convention tapering operation in wire electric discharge machining like wire bend, inaccuracies in taper, insufficient flushing, guide wear etc. A simple triangular profile was machined at 0°, 15° and 30° slant/taper angles. These taper profile areas are investigated for various machining parameters like wire guide distance, corner dwell time, wire offset and cutting speed override. It is observed that as the wire guide distance and cutting speed override increases, the profile area decreases. Whereas in case of wire offset, as offset increases the profile areas also increase. The corner dwell time parameter do not effect on the profile area. The taper profile areas measured highest at 30° followed by 15° and 0° slant angles. This is due to the workpiece placed at different angles during machining with the aid of fixture to obtain taper profile. The taper angle represents the angularity of slant triangular profiles. As the slant angle increases the variation in taper error also increases due to higher wire vibration. An artificial neural network model is developed for the prediction of these areas at a different slant angle. The model is validated experimentally where the errors in prediction ranged from 1% to 9%. In conclusion, it can be noticed that the machining parameters and slant angle influence on profiles irrespective of their dimensions. © IMechE 2020.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 Artificial neural network-based prediction assessment of wire electric discharge machining parameters for smart manufacturing(De Gruyter Open Ltd, 2023) Manoj, I.V.; Narendranath, S.; Mashinini, P.M.; Soni, H.; Rab, S.; Ahmad, S.; Hayat, A.Artificial intelligence (AI), robotics, cybersecurity, the Industrial Internet of Things, and blockchain are some of the technologies and solutions that are combined to produce "smart manufacturing,"which is used to optimize manufacturing processes by creating and/or accepting data. In manufacturing, spark erosion technique such as wire electric discharge machining (WEDM) is a process that machines different hard-to-cut alloys. It is regarded as the solution for cutting intricate parts and materials that are resistant to conventional machining techniques or are required by design. In the present study, holes of different radii, i.e. 1, 3, and 5 mm, have been cut on Nickelvac-HX. Tapering in WEDM is a delicate process to avoid disadvantages such as wire break, wire bend, wire friction, guide wear, and insufficient flushing. Taper angles viz. 0°, 15°, and 30° were obtained from a unique fixture to get holes at different angles. The study also shows the influence of taper angles on the part geometry and area of the holes. Next, the artificial neural network (ANN) technique is implemented for the parametric result prediction. The findings were in good agreement with the experimental data, supporting the viability of the ANN approach for the evaluation of the manufacturing process. The findings in this research provide as a reference to the potential of AI-based assessment in smart manufacturing processes and as a design tool in many manufacturing-related fields. © 2023 the author(s), published by De Gruyter.