Faculty Publications
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Item Behavioral study of alumina nanoparticles in pool boiling heat transfer on a vertical surface(2011) Hegde, R.N.; Reddy, R.P.; Rao, S.S.Experiments were carried out to investigate the pool boiling of alumina-water nanofluid at 0.1 g/l to 0.5 g/l of distilled water, and the nucleate pool boiling heat transfer of pure water and nanofluid at different mass concentrations were compared at and above the atmospheric pressure. At atmospheric pressure, different concentrations of nanofluids display different degrees of deterioration in boiling heat transfer. The effect of pressure and concentration of nanoparticles revealed significant enhancement in heat flux and deterioration in pool boiling. The heat transfer coefficient of 0.5 g/l alumina-water nanofluid was compared with pure water and clearly indicates deterioration. At all pressures the heat transfer coefficients of the nanofluid were lower than those of pure water. Experimental observation revealed particles coating over the heater surface and subsequent SEM inspection of the heater surface showed nanoparticles coating on the surface forming a porous layer. To substantiate the nanoparticle deposition and its effect on heat flux, investigation was done by measuring the surface roughness of the heater surface before and after the experiment. While SEM images of the heater surface revealed nanoparticle deposition, surface roughness of the heater surface confirmed it. Based on the experimental investigations it can be concluded that an optimum thickness of nanoparticles coating favors an increase in heat flux. Higher surface temperature due to the presence of nanoparticles coating results in the deterioration of boiling heat transfer. © 2011 Wiley Periodicals, Inc.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 Application of back propagation algorithms in neural network based identification responses of AISI 316 face milling cryogenic machining technique(Taylor and Francis Ltd., 2022) Karthik, K.R.; Malghan, R.L.; Shettigar, A.; Rao, S.S.; Herbert, M.A.The paper explores the potential study of artificial neural network (ANN) for prediction of response surface roughness (Ra) in face milling operation with respect to cryogenic approach. The model of Ra was expressed as the main factor in face milling of spindle speed, feed rate, depth of cut and coolant type. The ANN is trained using four various back propagation algorithms (BPA). The emphasis of the paper is to investigate the performance and the accuracy of the attained results depicts the effectiveness of the trained ANN in identifying the predicted Ra. The incorporated various BPA in predicting the Ra. The performance comparative study is made among statistical (Response Surface Methodology (RSM)) and ANN (BPA–training algorithm) methods. The various incorporated BPA algorithms are Gradient Descent (GD), Scaled Conjugate Gradient Descent (SCGD), Levenberg Marquardt (LM) and Bayesian Neural Network (BNN). Afterwards the best suitable BPA is identified in predicting Ra for AISI 316 in face milling operation using liquid nitrogen (LN2) as cutting fluid. The outperformed BPA is identified based on the attained deviation percentage and time required for the training the network. © 2020 Engineers Australia.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 Optimization of process parameters for friction stir processing (FSP) of AA8090/boron carbide surface composites(Springer Science and Business Media Deutschland GmbH, 2024) Adiga, K.; Herbert, M.A.; Rao, S.S.; Shettigar, A.K.Friction Stir Processing (FSP) is an innovative and promising technique for microstructure refinement, material property enhancement, and surface composite production. The current study describes the fabrication of AA8090/boron carbide surface composites (SCs) by FSP. Experimental studies were conducted by varying the FSP parameters, specifically the rotational speed (800–1400 rpm), traverse speed (25–75 mm/min), and groove width (1–1.8 mm). Ultimate Tensile Strength (UTS), Surface Roughness (SR), and Percentage Elongation (El) were used as response measures. Experiments were planned based on the central composite design (CCD) of Response Surface Methodology (RSM) and a mathematical relationship between the input parameters and UTS, SR and El, and were obtained by RSM. The model adequacy was tested using analysis of variance (ANOVA). The models enabled the examination of individual and interaction effects of input parameters on the UTS, SR, and El of the produced SCs. AA8090/boron carbide SC strength was optimal of 366 MPa at 800 rpm, 75 mm/min, and 1.8 mm and optimal 21.13% elongation at 1400 rpm, 25 mm/min, and 1 mm. A smoother surface with 0.82-μm roughness was optimal at 1400 rpm, 25 mm/min, and 1.2 mm. The present study uses the FSP method to synthesize near-net-shaped SCs without further machining by systematically selecting process parameters. The study shows that the increase in rotational speed during AA8090/boron carbide SC fabrication produces composites with a good surface finish, lower UTS, and good ductility. However, the increase in the other two parameters, namely, traverse speed and groove width, produces low ductile composites with rougher surfaces and higher strengths. Graphical abstract: (Figure presented.) © International Institute of Welding 2024.