Faculty Publications

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Author: search.filters.author.Shettigar, A.

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    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.
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    Mechanical Properties and Microstructural Characteristics of Friction Stir Welded Aluminium Matrix Composite
    (Springer Nature, 2021) Subramanya, B.; Shettigar, A.; Herbert, M.A.; Rao, S.S.
    Nowadays, friction stir welding process appears a promising technique to weld difficult materials by conventional welding techniques. Present study aims to analyse the significance of process variables on the mechanical behaviour of aluminium matrix composite joined by friction stir welding (FSW) technique. FSW is carried out at different welding conditions using conventional threaded cylindrical tool (TC). Microstructural study indicates several tiny reinforced particles are uniformly distributed in the nugget region. Recrystallization and grin refinement are observed in the weld area. Nugget region exhibits higher hardness compared to the base material. Joint efficiency of up to 89% is obtained for the FS-welded composite. The fracture surface reveals that the matrix undergoes a ductile fracture whereas reinforced particles exhibit brittle fracture. © 2021, Springer Nature Singapore Pte Ltd.
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    Multi Response Optimization of Friction Stir Welding Process Variables using TOPSIS approach
    (Institute of Physics Publishing helen.craven@iop.org, 2018) Prabhu B, S.R.; Shettigar, A.; Herbert, M.; Rao, S.
    Being a solid state welding process, the friction stir welding (FSW) is extensively used these days, to join difficulty-to-weld materials such as aluminium alloys and its composites. This study emphasizes on friction stir welding of aluminium matrix composite (AMC) reinforced with silicon carbide particle. The FSW tool geometry and process variables play a vital role in governing the joint strength. Size of the grain and the hardness at the weld region influences the joint strength. Process variables such as tool revolving speed, tool traverse speed and the tool pin profile are optimized with multiple responses such as % elongation, tensile strength and hardness. In the present study a technique for order preference by similarity to ideal solution (TOPSIS) approach is used to solve multiple response optimization problems. The optimal solution reveals that the multiple response characteristics of the FS welded AMCs can be improved through the TOPSIS approach. © Published under licence by IOP Publishing Ltd.
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    Experimental investigation of joint properties of friction stir welded aluminium matrix composite
    (Elsevier Ltd, 2021) Prabhu B, S.R.; Shettigar, A.; Herbert, M.A.; Rao, S.S.
    The present study is focused on welding of particulate aluminium matrix composites using friction stir welding process and investigating the influence of various process variables on the joint properties. The microstructural study and mechanical behaviors such as tensile strength and hardness of the weld zone were measured. Microstructural studies showed that process variables play pivotal role in refinement of grains. Compared to the top of the weld region, smaller grains were formed at the bottom due to variation in the heating effect. Measurement of the tensile strength and hardness of the weld zone, indicated that process variables plays important role in controlling the joint properties. Beyond the optimum range of process variables, the joint strength of welded part deteriorates due to the insufficient stirring and lack of plasticization leads to defect formation. Joint welded with traverse speed of 100 mm/min and revolution speed of 1200 rpm exhibited better mechanical properties. © 2021 Elsevier Ltd. All rights reserved.
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    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. ©
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    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).
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    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.; Shriram
    An 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).
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    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.
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    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.
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    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.