Journal Articles
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Item A Review of Optimization and Measurement Techniques of the Friction Stir Welding (FSW) Process(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Prabhakar, D.A.P.; Korgal, A.; Shettigar, A.K.; Herbert, M.A.; Gowdru Chandrashekarappa, M.P.G.; Pimenov, D.Y.; Giasin, K.This review reports on the influencing parameters on the joining parts quality of tools and techniques applied for conducting process analysis and optimizing the friction stir welding process (FSW). The important FSW parameters affecting the joint quality are the rotational speed, tilt angle, traverse speed, axial force, and tool profile geometry. Data were collected corresponding to different processing materials and their process outcomes were analyzed using different experimental techniques. The optimization techniques were analyzed, highlighting their potential advantages and limitations. Process measurement techniques enable feedback collection during the process using sensors (force, torque, power, and temperature data) integrated with FSW machines. The use of signal processing coupled with artificial intelligence and machine learning algorithms produced better weld quality was discussed. © 2023 by the authors.Item Advances in micro electro discharge machining of biomaterials: a review on processes, industrial applications, and current challenges(Taylor and Francis Ltd., 2024) Korgal, A.; Shettigar, A.K.; Karanth P, N.; Prabhakar, D.A.P.Micro Electro-Discharge Machining is a precision machining process that uses electrical discharge to produce small-scale components with high accuracy. A metal workpiece is machined in this process by repeatedly generating spark between a tool electrode and the workpiece, removing material in a controlled manner. The significance of µ-EDM lies in its ability to produce highly accurate and complex components with a high surface finish, making it ideal for use in various industries, including aerospace, medical, and electronics. The critical parameters to the success of µ-EDM include the electrical discharge energy, voltage, current, pulse duration, and spark gap between the tool electrode and workpiece, including the shape and size of the tool electrode. This review article discusses the µ-EDM process used to machine biological materials and also examines the µ-EDM, dry µ-EDM procedure, and the features of biomedical materials for biocompatibility, 3D micro shape machining with tool wear composition, and thin film coating for microelectrodes. The impact of introducing nanoparticles to dielectric fluids is further clarified in this article. This study addresses the prospective future research subjects and application areas for the µ-EDM process in order to fulfill the demanding criteria for biomaterials and their usage in the production of bioimplants. © 2024 Taylor & Francis Group, LLC.Item Microstructural characterization and hardness evaluation of friction stir welded composite AA6061-4.5Cu-5SiC (Wt.%)(Defense Scientific Information and Documentation Centre, 2013) Shettigar, A.K.; Salian, G.; Herbert, M.A.; Rao, S.Recent developments in advanced materials research have led to the emergence of new materials having features like low density, high strength to weight ratio, excellent mechanical properties, heat and corrosion resistance. In friction stir welding (FSW), a non-consumable rotating welding tool is used to generate the frictional heat and plastic deformation of the material in the welding zone, which is in the solid state. The advantages of FSW as compared to the fusion welding are high joint strength, less defect weld, uniform distribution of grain structure in the weld zone and low power consumption. AA6061 with 4.5 % weight of copper and 5 % weight of SiC composite material has been prepared to conduct experiment and carry out characterization, evaluation of the mechanical properties. Micro-structural characterization of the weld zone is carried out by scanning electron microscope (SEM). Evaluation of hardness was also carried out across the weld zone. A successful method for FSW of AA6061-4.5(wt.%) Cu-5(wt.%) SiC has been developed. © 2013, DESIDOC.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 Back propagation genetic and recurrent neural network applications in modelling and analysis of squeeze casting process(Elsevier Ltd, 2017) Gowdru Chandrashekarappa, M.; Shettigar, A.K.; Krishna, P.; Parappagoudar, M.B.Today, in competitive manufacturing environment reducing casting defects with improved mechanical properties is of industrial relevance. This led the present work to deal with developing the input-output relationship in squeeze casting process utilizing the neural network based forward and reverse mapping. Forward mapping is aimed to predict the casting quality (such as density, hardness and secondary dendrite arm spacing) for the known combination of casting variables (that is, squeeze pressure, pressure duration, die and pouring temperature). Conversely, attempt is also made to determine the appropriate set of casting variables for the required casting quality (that is, reverse mapping). Forward and reverse mapping tasks are carried out utilizing back propagation, recurrent and genetic algorithm tuned neural networks. Parameter study has been conducted to adjust and optimize the neural network parameters utilizing the batch mode of training. Since, batch mode of training requires huge data, the training data is generated artificially using response equations. Furthermore, neural network prediction performances are compared among themselves (reverse mapping) and with those of statistical regression models (forward mapping) with the help of test cases. The results shown all developed neural network models in both forward and reverse mappings are capable of making effective predictions. The results obtained will help the foundry personnel to automate and précised control of squeeze casting process. © 2017Item Microstructure evolution and mechanical properties of friction stir welded AA6061/rutile composite(Institute of Physics Publishing helen.craven@iop.org, 2019) Prabhu B, S.R.; Shettigar, A.K.; Herbert, M.A.; Rao, S.S.Present study explores the Friction stir welding (FSW) of rutile reinforced AA6061matrix composite using various combination of tool traverse speeds (60, 75 and 90 mm min-1), rotational speeds (750, 1000 and 1250 rpm) and tool pin profiles (Threaded cylindrical and Square profiled pin). FSW process variables have significant impact in controlling the mechanical properties of the joint by limiting the welding defects. It has been inferred from the study that tool rotational speed and tool traverse speed majorly affects the microstructure, joint quality, hardness and joint strength. The weld area showed the presence of four distinct regions usually found in FSW of aluminium matrix composites. The weld region exhibited fine equiaxed grains and uniformly distributed tiny reinforced rutile particles. Tool having square profiled pin shows improved joint properties in comparison with tool having threaded cylindrical pin. © 2019 IOP Publishing Ltd.Item Microstructure and mechanical properties of rutile-reinforced AA6061 matrix composites produced via stir casting process(Nonferrous Metals Society of China B12 Fuxing Road Beijing 100814, 2019) Prabhu B, S.R.; Shettigar, A.K.; Herbert, M.A.; Rao, S.S.A novel process of fabricating aluminium matrix composites (AMCs) with requisite properties by dispersing rutile particles in the aluminum matrix was studied. A novel bi-stage stir casting method was employed to prepare composites, by varying the mass fractions of the rutile particles as 1%, 2%, 3% and 4% in AA6061 matrix. The density, tensile strength, hardness and microstructures of composites were investigated. Bi-stage stir casting method engendered AMCs with uniform distribution of the reinforced rutile particles in the AA6061 matrix. This was confirmed by the enhancement of the properties of AMCs over the parent base material. Rutile-reinforced AMCs exhibited higher tensile strength and hardness as compared with unreinforced parent material. The properties of the composites were enhanced with the increase in the mass fraction of the rutile particles. However, beyond 3 wt.% of rutile particles, the tensile strength decreased. The hardness and tensile strength of the AMCs reinforced with 3 wt.% of rutile were improved by 36% and 14% respectively in comparison with those of matrix alone. © 2019 The Nonferrous Metals Society of ChinaItem Artificial bee colony, genetic, back propagation and recurrent neural networks for developing intelligent system of turning process(Springer Nature, 2020) Shettigar, A.K.; Gowdru Chandrashekarappa, G.C.M.; Ganesh, G.R.; Vundavilli, P.R.; Parappagoudar, M.B.Intelligent manufacturing requires significant technological interventions to interface manufacturing processes with computational tools in order to dynamically mold the systems. In this era of the 4th industrial revolution, Artificial neural network (ANNs) is a modern tool equipped with a better learning capability (based on the past experience or history data) and assists in intelligent manufacturing. This research paper reports on ANNs based intelligent modelling of a turning process. The central composite design is used as a data-driven modelling tool and huge input–output is generated to train the neural networks. ANNs are trained with the data collected from the physics-based models by using back-propagation algorithm (BP), genetic algorithm (GA), artificial bee colony (ABC), and BP algorithm trained with self-feedback loop. The ANNs are trained and developed as both forward and reverse mapping models. Forward modelling aims at predicting a set of machining quality characteristics (i.e. surface roughness, cylindricity error, circularity error, and material removal rate) for the known combinations of cutting parameters (i.e. cutting speed, feed rate, depth of cut, and nose radius). Reverse modelling aims at predicting the cutting parameters for the desired machining quality characteristics. The parametric study has been conducted for all the developed neural networks (BPNN, GA-NN, RNN, ABC-NN) to optimize neural network parameters. The performance of neural network models has been tested with the help of ten test cases. The network predicted results are found in-line with the experimental values for both forward and reverse models. The neural network models namely, RNN and ABC-NN have shown better performance in forward and reverse modelling. The forward modelling results could help any novice user for off-line monitoring, that could predict the output without conducting the actual experiments. Reverse modelling prediction would help to dynamically adjust the cutting parameters in CNC machine to obtain the desired machining quality characteristics. © 2020, Springer Nature Switzerland AG.Item Advantages of cryogenic machining technique over without-coolant and with-coolant machining on SS316(IOP Publishing Ltd, 2021) Karthik, M.; Malghan, R.L.; Shettigar, A.K.; Herbert, M.A.; Rao, S.S.The analysis concentrated towards the influence of speed of the spindle along with a cryogenic (LN2) cooling technique in treating SS316 usingCNC(Computerized numerical control) milling machine. An comparative study path was set and anlyised among three states i.e. Dry (Without coolant), wet (With coolant) and cryogenic (With liquid LN2) machining using coated carbide inserts. The coolant used in case of wet machining was water-soluble, referred to as cutting fluid. The experimental range falls in 3 different levels of spindle speed (SS), such as low level (1000 rpm), medium level (2000 rpm), and high level (3000 rpm), respectively. Meanwhile, feed rate (FR) and depth of cut (DOC) were reserved steadily with 450 mm min-1, 1 mm separately. This vital focus is towards cryogenic (LN2) machining effects and its perception of machinability on SS316, such as tool wear -TW(?m), cutting force-CF (N), cutting temperature-CT (oC) and surface roughness-Ra (?m). The experiments were conducted and documented with cryogenic (LN2) techniques to establish the fairness and practicability of the method to compare with without-coolant (dry) and with-coolant (wet) machining. The attained statistical results in comparison of LN2 method over without-coolant and with-coolant machining concerned to test cases for CF- Fx (N), CT(oC), Ra (?m) andFW(?m) are 53.21%-34.20%, 65.88%-44.51%, 75.43%-44.27%,&59.76%-23.10%, respectively. © 2021 IOP Publishing Ltd.Item Fretting wear behavior on LPBF processed AlSi10Mg alloy for different heat treatment conditions(Elsevier Editora Ltda, 2024) Nanjundaiah, R.S.; Rao, S.S.; Praveenkumar, K.; Prabhu, T.R.; Shettigar, A.K.; Gowdru Chandrashekarappa, M.; Linul, E.To widen the industrial application of additively manufactured (AM) parts, the study of fretting wear behavior is essential, as it ensures the safety and reliability that drive innovation in design and materials. This study explores the fretting wear behavior of the as-built and heat-treated state of AlSi10Mg alloy fabricated, viz., laser powder bed fusion (LPBF). Initially, the as-built and T5, T6, and stress-relieved (SR) heat-treated samples were examined using scanning electron microscopy (SEM) to gain insights into the microstructural changes. The as-built samples exhibited a higher hardness level (135 HV) primarily due to the presence of very fine microstructure of the α-Al cellular matrix with embedded Si. The α-Al cellular structure dissolved with various heat treatments, and Si particles coarsened. The hardness decreased to 85, 79, and 67 HV for the T5, T6, and SR conditions, respectively. Subsequently, fretting tests were conducted on the samples, applying various normal loads of 10, 50, and 100 N. Further, the samples were characterized by the coefficient of friction (COF), worn surface morphology, and wear volume loss. The investigation showed that the as-built material showed less wear volume loss under all loading conditions than the heat-treated conditions. Furthermore, the T5 heat treated sample had a lower wear volume when compared to the T6 and SR heat-treated samples. The heat-treated sample exhibits compressive stress, whereas the LPBF processed, the as-built sample shows tensile stress. © 2024 The Authors