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Item Applications of reinforcement particles in the fabrication of Aluminium Metal Matrix Composites by Friction Stir Processing - A Review(EDP Sciences, 2022) Adiga, K.; Herbert, M.A.; Rao, S.S.; Shettigar, A.Composite materials possess advantages like high strength and stiffness with low density and prove their essentiality in the aviation sector. Aluminium metal matrix composites (AMMC) find applications in automotive, aircraft, and marine industries due to their high specific strength, superior wear resistance, and lower thermal expansion. The fabrication of composites using the liquid phase at high temperature leads to the formation of intermetallics and unwanted phases. Friction Stir Processing (FSP) is a novel technique of composite fabrication, with temperature below the melting point of the matrix, achieving good grain refinement. Many researchers reported enhancement of mechanical, microstructure, and tribological properties of AMMC produced by the FSP route. The FSP parameters such as tool rotational speed, tool traverse speeds are found to be having greater impact on uniform dispersion of particles. It is observed that the properties such as tensile strength, hardness, wear and corrosion resistance, are altered by the FSP processes, and the scale of the alterations is influenced significantly by the processing and tool parameters. The strengthening mechanisms responsible for such alterations are discussed in this paper. Advanced engineering materials like shape memory alloys, high entropy alloys, MAX phase materials and intermetallics as reinforcement material are also discussed. Challenges and opportunities in FSP to manufacture AMMC are summarized, providing great benefit to researchers working on FSP technique. ©Item A comprehensive review of friction stir techniques in structural materials and alloys: challenges and trends(Elsevier Editora Ltda, 2022) Prabhakar, D.A.P.; Shettigar, A.; Herbert, M.A.; Gowdru Chandrashekarappa, M.; Pimenov, D.Y.; Giasin, K.; Prakash, C.Friction-stir techniques are the potential alternative to fusion-based systems for processing and welding metallic alloys and other materials. This review explores the advantages, applications, limitations, and future directions of seven friction-based techniques namely, Additive Friction Stir Deposition (AFSD), Friction Stir Additive Manufacturing (FSAM), Friction Stir Welding (FSW), Friction Stir Processing (FSP), Friction Surfacing (FS), Friction Stir Spot Welding (FSSW), and Friction Stir Lap Welding (FSLW). The basic underlying principle of these processes uses friction as a thermal energy source to weld/process/deposit materials. The common control parameters of all friction stir processing techniques are axial force, rotational speed, and weld or traverse speed. In addition, tool profiles and tool dimensions are known to influence the weld quality. The tool's rotational speed and axial force generate friction between the workpiece and tool material interface, which could plasticize the material. The additive powder bed friction stir process (APBFSP) is another new solid-state manufacturing technique that focus on fabricating the polymer matrix nanocomposites (PNC). In this, a hollow tool like AFSD and the fundamental principle of FSP are combined. The said parameters affect the quantity of material getting deposited/welded. However, weld speed/traverse speed alters the weld quality, and higher traverse speed results in porosity and voids in the welded/deposited/processed region. The only difference between AFSD and other friction stir techniques (FSTs) is that in the AFSD technique, the hollow rotating tool comprises two protrusions with different tool profiles (cylindrical, threaded cylindrical, and tapered cylindrical, square) used. Threaded cylindrical profile and tool steel as the tool material is the most commonly employed in FSTs. Apart from that, tungsten carbide is preferred for hard materials. The working principles and process parameters of FSTs that affect the part quality are discussed in detail. The above review gives the reader an understanding of the domain of FSTs that can be researched further. A summary of some of the potential research works with objectives, process parameters, and outcomes is highlighted. This will provide the readers with an overview of the work carried out by researchers across the globe. Finally, the potential research gaps for future directions to be explored soon across the globe are outlined. © 2022 The Author(s).Item Development of a surface roughness prediction system for machining of hot chromium steel (AISI H11) based on artificial neural network(Medwell Journals medwellonline@gmail.com, 2010) Rai, R.; Shettigar, A.; Rao, S.S.; ShriramAn attempt have been made to apply the principles of artificial neural networks (ANN) towards developing a prediction model for surface roughness during the machining of high chromium steel through face milling process. Now a days, hot chromium steel is prominently used in die and mould industry as well as in press tools, helicopter rotor blades, etc. Initially, Taguchi design of experiments was applied while conducting the experiments to reduce the time and cost of experiment. Multilayer perceptron (MLP) network using Feed Forward Error Back propagation was chosen as the neural network architecture to describe the process model. The experiments were conducted on a C.N.C milling machine using carbide cutters. Pearson correlation coefficient was also calculated to analyze the correlation between the system inputs and selected system output i.e. surface roughness. The results of ANN modeling were substantiated by testing and validation of the resulting surface roughness values and the results have been encouraging. The outputs of Pearson correlation coefficient also showed a strong correlation between the feed per tooth and surface roughness, followed by cutting speed. © 2006-2010 Asian Research Publishing Network (ARPN).Item Microstructure and hardness of friction stir welded aluminium-copper matrix-based composite reinforced with 10 wt-% SiCp(Maney Publishing, 2014) Shettigar, A.; Veeresh Nayak, C.; Herbert, M.A.; Rao, S.S.In the present work, an attempt has been made to join aluminium-copper matrix-based composite reinforced with 10 wt-% SiCp, by the friction stir welding technique, at different combinations of tool rotational speed (710, 1000 and 1400 rev mm1) and welding speed (50, 63 and 80 mm min1) using square profiled friction stir welding tool. Welding parameters play a predominant role in improving the mechanical strength by minimising the defects. A good number of defect free joints were obtained at various combinations of rotational speed and welding speed. It has been observed that, rotational speed and welding speed have strong influence on microstructure, Vickers hardness and quality of welds. © W. S. Maney &Son Ltd 2014.Item Machining Parameters Optimization of AA6061 Using Response Surface Methodology and Particle Swarm Optimization(SpringerOpen, 2018) Lmalghan, R.; Karthik, K.; Shettigar, A.; Rao, S.; Herbert, M.The influence of cutting parameters on the responses in face milling has been examined. Spindle speed, feed rate and depth of cut have been considered as the influential factors. In accordance with the design of experiments (DOE) a series of experiments have been carried out. The paper exemplifies on the optimizing the process parameters in milling through the application of Response surface methodology (RSM), RSM-based Particle Swarm Optimization (PSO) technique and Desirability approach. These aforesaid techniques have been applied to experimentally establish data of AA6061 aluminium material to study the effect of process parameters on the responses such as cutting force, surface roughness and power consumption. By adopting the multiple regression techniques, the interaction between the process parameters are acquired. The optimal parameters have been found by adopting the multi-response optimization techniques, i.e. desirability approach and PSO. The performance capability of PSO and desirability approach is investigated and found that the values obtained from PSO are comparable with the values of desirability approach. © 2018, Korean Society for Precision Engineering and Springer-Verlag GmbH Germany, part of Springer Nature.Item Influence of Support Vector Regression (SVR) on Cryogenic Face Milling(Hindawi Limited, 2021) Karthik, R.M.C.; Malghan, R.L.; Kara, F.; Shettigar, A.; Rao, S.S.; Herbert, M.A.The paper aims to investigate the processing execution of SS316 in manageable machining cooling ways such as dry, wet, and cryogenic (LN2-liquid nitrogen). Furthermore, "one parametric approach"was utilized to study the influence and carry out the comparative analysis of LN2over dry and LN2over wet machining conditions. Response surface methodology (RSM) is incorporated to build a relationship model among the considered independent variables (spindle speed: (S, rpm), feed rate (F, mm/min), and depth of cut (doc) (D, mm)) and the dependent variable (surface roughness (Ra)). Since there is the involvement of more than one independent variable, the generation of regression equation is "multiple linear regression."Based on the attained coefficient value of the independent variable, the respective impact on surface roughness is identified. The results of comparative analysis of LN2over dry and LN2over wet machining states revealed that LN2 machining yielded better surface finish with up to 64.9%, 54.9% over dry and wet machining, respectively, indicating the benefits of LN2 for achieving better Ra. The benchmark function of the proposed mode hybrid-bias (BNN-SVR) algorithm showcases the propensity to emerge out of the local minimum and coincide with the optimal target value. The performance of the (BNN-SVR) is a prevalent new ability to fetch the partially trained weights from the BNN model into the SVR model, thus leading to the conversion of static learning capability to dynamic capability. The performances of the adopted prediction approaches are compared through a range of attained error deviation, i.e., (RA: 3.95%-8.43%), (BNN: 2.36%-5.88%), (SVR: 1.04%-3.61%), respectively. Hybrid-bias (BNN-SVR) is the best suitable prediction model as it provides significant evidence by attaining less error in predicting Ra. However, SVR surpasses BNN and RSM approaches because of the convergence factor and narrow margin error. © 2021 Rao M. C. Karthik et al.Item Experimental assessment of FSW process to join AA6061/Rutile composite and parametric optimization using TGRA(IOP Publishing Ltd, 2021) Prabhu B, S.R.; Shettigar, A.; Herbert, M.A.; Rao, S.S.Present study is focused on investigating the effect of various friction stir welding (FSW) process variables on AA6061/Rutile composites welding quality. FSWof composites was performed considering tool geometry (Tg), welding speed (Ws) and rotational speed (Ns) as ideal parameters for multi-response optimization. Experiments were designed based on the L9 orthogonal array. Analysis of variance (ANOVA) was utilized to evaluate the effects of these welding process variables on output responses namely hardness and ultimate tensile strength (UTS). Main effects plots were drawn to found out the optimal levels of these process parameters. Multi-response optimization of the welding process has been performed using Taguchi's grey relational analysis (TGRA). Analysis revealed that welding speed of 90mmmin-1, a tool with a square pin, and rotational speed of 1000 rpm produced an FSWjoint with excellent mechanical properties. Microstructure analysis revealed that refinement in the grain structure and redistribution of reinforced particles helped in improved joint strength. © 2021 IOP Publishing Ltd.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 Experimental analysis and optimization of plasma spray parameters on microhardness and wear loss of Mo-Ni-Cr coated super duplex stainless steel(Taylor and Francis Ltd., 2022) Gowdru Chandrashekarappa, M.; Pradeep, N.B.; Girisha, L.; Harsha, H.M.; Shettigar, A.Plasma spray coatings are one among the economic path to offer quick solutions for preventing the part (substrate) failures due to rapid wear. In the present work, Mo-Ni-Cr powder is used as a coating material on super duplex stainless steel to minimise the wear loss. The microhardness of the coating is affected by the factors (current, powder feed rate and standoff distance) of the plasma spray coating process. Taguchi method is followed for preliminary experimental plan, analysis, and to perform optimisation for maximum microhardness. The results showed that the current being the dominant effect followed by powder feed rate and standoff distance on the microhardness of coated samples. The optimised plasma spray condition resulted in the highest coating microhardness (i.e., 764.33 HV), which is 2.78 times higher than that of super duplex stainless steel (i.e., 275 HV). Taguchi experiments are conducted to know the factors (load, sliding speed and sliding distance) influence the wear loss of coated samples prepared for optimised plasma spray conditions. The applied load and sliding speed are found statistically significant, whereas the sliding distance is insignificant towards wear loss. The results of wear loss of the substrate (uncoated sample) and optimised condition of the coated sample are found equal to 18 mg, and 2.8 mg, respectively. © 2020 Engineers Australia.Item Influence of process variables on joint attributes of friction stir welded aluminium matrix composite(Taylor and Francis Ltd., 2022) Prabhu B, S.R.; Shettigar, A.; Gowdru Chandrashekarappa, M.; Herbert, M.A.; Rao, S.S.The microstructure and mechanical attributes of the friction stir welded aluminium metal matrix composites (AMCs) are reported in this paper. Impacts of friction stir welding (FSW) process variables on the mechanical properties are evaluated. Metallographic studies showed that variation in welding process variables’ plays a vital role in obtaining recrystallised equiaxed fine-grain structures. The formed joint region indicates a gradual reduction in grain size as it moves from top to bottom of the weld region due to variation in the heat generation. Process variables like tool movement along the joint direction and tool revolution speed govern the joint strength of AMCs. Beyond the optimum values of process variables, the weld quality and joint strength of the welded part deteriorate due to the inappropriate stirring of the material at the weld region. The highest joint strength obtained for tool movement along the direction was 80 mm/min, and the revolution of the tool was 1000 rpm. © 2020 Informa UK Limited, trading as Taylor & Francis Group.