Faculty Publications
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Item Influence of Process Variables on the Ultimate Tensile Strength of Friction Stir Welded AA6061 Matrix Composite(Springer Nature, 2021) Shettigar, A.; Prabhu B, S.R.; Herbert, M.A.; Rao, S.S.The present study is focused on the application of the friction stir welding process (FSW) to weld aluminium matrix composites (AMCs). Joints are formed by varying FSW process variables such as tool revolving speed (TRS), tool traverse speed (TTS) and the tool pin geometry (TPG). Influence of these parameters on the ultimate tensile strength (UTS) of the joints is investigated. Process variable optimization is done using Taguchi L18 orthogonal array design. Optimum process variables are determined and confirmed by confirmation tests based on the analysis of variance. © 2021, Springer Nature Singapore Pte Ltd.Item Prediction of surface finish and optimization of machining parameters in turning(2012) Prasad, D.; Krishna, P.; Rao, S.S.Surface roughness plays a crucial role in the functional capacity of machined parts. In this work, experiments were carried out on a conventional lathe for different cutting parameters namely feed, spindle speed, depth of cut and tool nose radius according to Taguchi Design of Experiments. Radial acceleration readings were taken with an accelerometer. Optimum cutting parameters and their level of significance were found using Taguchi analysis (ANOVA). Regression analysis was carried out to identify whether the experimental roughness values have fitness characteristic with the process parameters. Recurrence Plots (RP) were obtained using the sensor signals which determine surface roughness qualitatively and Recurrence Quantification Analysis (RQA) technique was used to quantify the RP obtained. Surface finish was predicted using a feed forward back propagation neural network with RQA parameters, cutting parameters and acceleration data as inputs to the network. The validity and reliability of the methods were verified experimentally. © (2012) Trans Tech Publications.Item Influence of welding process parameters on microstructure and mechanical properties of friction stir welded aluminium matrix composite(Trans Tech Publications Ltd ttp@transtec.ch, 2017) Prabhu B, S.; Shettigar, A.K.; Karthik, K.; Rao, S.S.; Herbert, M.In this study, the effect of process parameters on microstructure and mechanical properties of friction stir welded aluminium matrix composites(AMC) have been explored. The results indicated that the recrystallized grain size at the bottom of the weld region is smaller than that at the top region due to difference in the heat transfer at the weld region. The joint strength of AMCs depends on proper selection of process parameters like tool rotational speed and welding speed. If process parameter values are beyond the optimal value, the joint strength decreases due to formation of defects. Maximum tensile strength is obtained for rotational speed of 1000 rpm and welding speed of 80mm/min. © 2017 Trans Tech Publications, Switzerland.Item Taguchi method of optimization of process variables for ultimate tensile strength of friction stir welded joint of Al-Ce-Si-Mg aluminium alloy plates(Elsevier Ltd, 2021) D’Souza, A.D.; Rao, S.S.; Herbert, M.A.A novel method of welding known as friction stir welding or FSW has found wide applications in metal joining industries now a days. There are various process input variables which affect the output quality characteristic or response of the weld joint produced. Speed of tool rotation, tool feed and tool pin contour are important input process variables and ultimate tensile stress (UTS) of the FSW joint is the output response considered in this research. The Taguchi method of Design of Experiments type optimization was used to find the level and contribution of each of these input process variables on output quality characteristic of the FSW butt joint produced on plates of Al-Ce-Si-Mg aluminium alloy material. Taguchi L9 orthogonal array type design was utilized to bring down the number of experiments from 27 to 9 for a 3 factor, 3 levels each, analysis. The results of Taguchi analysis indicated that the speed of tool rotation at 1200 rpm, tool feed at 15 mm/min and triangular shape pin tool gave the peak output UTS. The ANOVA tests demonstrated that the contribution percentage of the tool pin contour at 41%, speed of tool rotation at 29%, and tool feed at 22%, showing that the tool pin contour has maximum bearing on the output quality characteristics, i.e., UTS of the FSW joint. The optimum UTS predicted by the Taguchi analysis was 88.29 MPa, whereas the confirmation experiments gave a result of 80.54 MPa, which was well inside the confidence interval predicted by the Taguchi design. © 2021 Elsevier Ltd. All rights reserved.Item Process parameter optimization for ultimate tensile strength of friction stir welded joint of Al-10Mg-8Ce-3.5Si aluminium alloy plates using Taguchi technique(Elsevier Ltd, 2022) D’Souza, A.D.; Rao, S.S.; Herbert, M.A.The Friction stir welding (FSW) process has become a popular method of joining metals, due to its clean and efficient nature of producing welds. The input process parameters: the tool rotation speed, tool feed and tool pin shape are the deciding parameters for an optimum output quality characteristic, the Ultimate tensile stress (UTS) of the weld joint. Here in this research, the Taguchi full factorial design technique is discussed for maximizing the UTS of the weld joint formed in Al-10Mg-8Ce-3.5Si aluminium alloy plates. The ANOVA of means and Signal to Noise ratios for UTS was used to assess the influence of each of the input process parameters on output UTS. The main effect plots of the ANOVA results demonstrated that, the tool rotation speed at level 2 or 1000 rpm, the tool feed at level 3 or 20 mm/min and tool pin shape at level 1 or triangular cross section, gave the optimum results for output UTS. The ANOVA for UTS also showed the percentage contribution of input process parameters; the shape of tool pin as 60.06%, the tool feed as 15.42% and shape of tool pin as 2.41%. The UTS value predicted by the Taguchi analysis was at 108.47 MPa which was in good agreement with the experimentally obtained value of 106.84 MPa. A nonlinear regression equation was developed by correlating the input process parameters, which could be used to predict the optimum UTS results. © 2022 Elsevier Ltd. All rights reserved.Item Application of particle swarm optimization and response surface methodology for machining parameters optimization of aluminium matrix composites in milling operation(Springer Verlag service@springer.de, 2017) Malghan, R.L.; Karthik, K.M.C.; Shettigar, A.K.; Rao, S.S.; D’Souza, R.J.Face milling is extensively used machining operation to generate the various components. Usually the selection of the process parameters are incorporated by trial and error method, literature survey and the machining hand book. This kind of selection of process parameters turns out to be very tedious and time-consuming. In order to overcome this there is a need to develop a technique that could be able to find the optimal process parameters for the desired responses in machining. The present paper illustrates an application of response surface methodology (RSM) and particle swarm optimization (PSO) technique for optimizing the process parameters of milling and provides a comparison study among desirability and PSO techniques. The experimental investigations are carried out on metal matrix composite material AA6061-4.5%Cu-5%SiCp to study the effect of process parameters such as feed rate, spindle speed and depth of cut on the cutting force, surface roughness and power consumption. The process parameters are analyzed using RSM central composite face-centered design to study the relationship between the input and output responses. The interaction between the process parameters was identified using the multiple regression technique, which showed that spindle speed has major contribution on all the responses followed by feed rate and depth of cut. It has shown good prediction for all the responses. The optimized process parameters are acquired through multi-response optimization using the desirability approach and the PSO technique. The results obtained from PSO are closer to the values of the desirability function approach and achieved significant improvement. © 2016, The Brazilian Society of Mechanical Sciences and Engineering.Item An Efficient Approach to Optimize Wear Behavior of Cryogenic Milling Process of SS316 Using Regression Analysis and Particle Swarm Techniques(Springer, 2019) Karthik, M.C.; Malghan, R.L.; Shettigar, S.; Rao, S.S.; Herbert, M.A.The present work is an endeavor to carry out a machining using LN2 in face milling operations and to produce the milling samples with excellent wear resistance property. The output response (wear rate) depends on appropriate choice of speed, feed, and depth of cut. The experimental data are conducted (collected) for SS316 as per central composite design. The present work exemplifies an employment of conventional and nonconventional strategies for optimizing the milling factors of cryogenically treated samples in face milling to achieve the desired wear (response). The results of nonlinear regression (desirability strategy) and nonconventional [particle swarm optimization, (PSO)] optimization techniques are compared, and PSO is found to outperform the desirability function approach. The present work even highlights the effect and results of LN2 on wear in contrast to wet condition. © 2018, The Indian Institute of Metals - IIM.Item Parameter investigation and optimization of friction stir welded AA6061/TiO2 composites through TLBO(Springer Science and Business Media Deutschland GmbH, 2022) Prabhu B, S.R.; Shettigar, A.; Herbert, M.A.; Rao, S.S.This paper explicates the joining of AA 6061/TiO2 composites by the friction stir welding (FSW) process. FSW experiments were conducted as per the three factors, three-level, central composite ivy– face-centered design method. Mathematical relationships between the FSW process parameters, namely tool geometry, welding speed, and tool rotational speed, and the output responses such as hardness, yield strength, and ultimate tensile strength were established using response surface methodology. Adequacies of established models were assessed through the analysis of variance method. Further, the paper elucidates the application of the teaching–learning-based optimization (TLBO) algorithm to identify the optimal values of input variables and to obtain an FSW joint with superior mechanical properties. The optimized experimental condition obtained from the TLBO yields an FSW joint with a UTS of 174 MPa, yield strength of 120 MPa, and hardness of 126HV. The study revealed that the result of the TLBO algorithm matched the findings of the FSW experiments. © 2021, The Author(s).Item A grasshopper optimization algorithm-based movie recommender system(Springer, 2024) Ambikesh, G.; Rao, S.S.; Chandrasekaran, K.A movie recommendation system functions as a specialized information system, providing users with personalized suggestions aligned with their movie preferences. Employing advanced algorithms and data analysis methods, these systems scrutinize variables such as users' viewing history and preferences to formulate personalized recommendations. Our proposed methodology, termed GOA-k-means, amalgamates the Grasshopper Optimization Algorithm (GOA) with k-means clustering to navigate the dynamic nature of user preferences. Facilitating real-time calibration, GOA-k-means yields recommendations that adapt to users' shifting interests. We developed our model utilizing a dataset of one million records from Movielens, pre-processed via z-score normalization and subjected to Principal Component Analysis (PCA) for feature extraction. In comparison to conventional techniques, GOA-k-means demonstrated superior performance in metrics such as precision, recall, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), establishing itself as a valuable tool for augmenting user engagement in the entertainment industry. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.