Browsing by Author "Shettigar, A."

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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).
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.
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. ©
Development of a Convolutional Neural Network Model to Predict Coronary Artery Disease Based on Single-Lead and Twelve-Lead ECG Signals
(MDPI, 2022) Vasudeva, S.T.; Rao, S.S.; Karanth P, N.; Shettigar, A.; Mahabala, C.; Kamath, P.; Gowdru Chandrashekarappa, M.; Linul, E.
Coronary artery disease (CAD) is one of the most common causes of heart ailments; many patients with CAD do not exhibit initial symptoms. An electrocardiogram (ECG) is a diagnostic tool widely used to capture the abnormal activity of the heart and help with diagnoses. Assessing ECG signals may be challenging and time-consuming. Identifying abnormal ECG morphologies, especially in low amplitude curves, may be prone to error. Hence, a system that can automatically detect and assess the ECG and treadmill test ECG (TMT-ECG) signals will be helpful to the medical industry in detecting CAD. In the present work, we developed an intelligent system that can predict CAD, based on ECG and TMT signals more accurately than any other system developed thus far. The distinct convolutional neural network (CNN) architecture deals with single-lead and multi-lead (12-lead) ECG and TMT-ECG data effectively. While most artificial intelligence-based systems rely on the universal dataset, the current work used clinical lab data collected from a renowned hospital in the neighborhood. ECG and TMT-ECG graphs of normal and CAD patients were collected in the form of scanned reports. One-dimensional ECG data with all possible features were extracted from the scanned report with the help of a modified image processing method. This feature extraction procedure was integrated with the optimized architecture of the CNN model leading to a novel prediction system for CAD. The automated computer-assisted system helps in the detection and medication of CAD with a high prediction accuracy of 99%. © 2022 by the authors.
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).
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.
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.
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. Â© 2021 Elsevier Ltd. All rights reserved.
Influence of machine variables on the microstructure and mechanical properties of AA6061/TiO2 friction stir welds
(Taylor and Francis Ltd., 2023) Prabhu B, S.R.; Shettigar, A.; Herbert, M.A.; Rao, S.S.
The present work explicates the joining of TiO2 (rutile) particles reinforced aluminium matrix composites (AMCs) through the friction stir welding (FSW) technique. Joining of AMCs using conventional fusion welding techniques faces a lot of challenges, which can be overcome by the FSW process. The effect of the two most critical process variables, welding speed, and tool rotational speed on the grain structure of the joint and on the mechanical behaviours was evaluated. The study revealed that machine variables regulate the quantity of heat input, heat exposure duration, and rate of cooling, thereby, significantly altering the grain size in the weld region and mechanical behaviour of the joint. The tool rotational speed had a substantial impact on the joint strength, whereas tool traverse speed facilitates homogeneous dispersion of reinforced particles in the matrix. The ‘W’-shaped hardness variation profile was observed across the weld zone, showing the highest hardness in the weld stir zone. The UTS of the welded specimen, measured across the joint was almost equal to the parent material with fracture occurring at the interface of the heat-affected and the thermo-mechanically affected zone which was the weakest point in the weld region. © 2022 Informa UK Limited, trading as Taylor & Francis Group.
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.
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.
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.
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.
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.
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.
Multi Response Optimization of Friction Stir Welding Process Variables using TOPSIS approach
(2018) Prabhu, 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.
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. Â© Published under licence by IOP Publishing Ltd.
Optimization of FSW process parameters for maximum UTS of AA6061/rutile composites using Taguchi technique
(Sharif University of Technology, 2022) Prabhu B, S.R.; Shettigar, A.; Herbert, M.A.; Rao, S.S.
In the friction stir welding process, preferred joint property is vastly reliant on the selection of optimal welding conditions. The present study aims to use the Taguchi technique to find the optimal process conditions for achieving superior Ultimate Tensile Strength (UTS) in friction stir welded Aluminum Matrix Composite (AMC) joints. AMCs reinforced with rutile particles which have a potential application in the aerospace, automotive, and marine industries are used in the present work. Taguchi parametric design technique was used to identify the effect of rotational speed, tool traverse speed, and tool geometry on joint strength. Taguchi approach confined the optimum level of process variables and these variables were optimized. The investigation showed that the parameters within the selected value range will seriously affect the output. The predicted value of the output response was 155.48 MPa, which was validated by further experiments using the optimum process variables. Analysis Of Variance (ANOVA) results indicated that the UTS of the composite joint is mainly affected by the tool traverse speed followed by rotational speed, and tool geometry. The microstructural study unveiled that grain size is dependent on process variables and finer grains offer better joint properties. © 2022 Sharif University of Technology. All rights reserved.
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).