Browsing by Author "Shettigar, A.K."

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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.
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.
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.
Analysis of Materials Expansion Properties for Computation of Thermal Error Compensation Values for Machine Tool Applications
(Springer Nature, 2024) Shanmugaraj, V.; Shruthi, G.; Shettigar, A.K.; Krishna, P.
One of the major causes of the total geometric inaccuracy of the machine is the thermo-mechanical error due to the deformation of machine tools, which is caused by both internal and external heat sources. Understanding the factors driving this is crucial to bring down errors to negligible values on machine tools. There are many different thermal factors, and it is a combination of all of these influences and their histories that determines the actual temperature on the distribution on the elements of machine tools. The expansion properties of the machine tool elements are analyzed in the computation of thermal expansion of these elements. Neural network as a part of artificial intelligence is widely used for this type of application as the data captured from the process is highly nonlinear. Giving the right data for the neural network training is at most important as this decides about the quality of neural network training. As the data is huge enough considering various conditions existing in the machining environment, the proper data pre-processing only will make the training much more effective. This paper’s main aim is to study the thermal expansion properties by properly analyzing the data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Application of neural network for the prediction of tensile properties of friction stir welded composites
(2017) Shettigar, A.K.; Prabhu, S.; Malghan, R.; Rao, S.; Herbert, M.
In this paper, an attempt has been made to apply the neural network (NN) techniques to predict the mechanical properties of friction stir welded composite materials. Nowadays, friction stri welding of composites are predominatally used in aerospace, automobile and shipbuilding applications. The welding process parameters like rotational speed, welding speed, tool pin profile and type of material play a foremost role in determining the weld strength of the base material. An error back propagation algorithm based model is developed to map the input and output relation of friction stir welded composite material. The proposed model is able to predict the joint strength with minimum error. � 2017 Trans Tech Publications, Switzerland.
Application of neural network for the prediction of tensile properties of friction stir welded composites
(Trans Tech Publications Ltd ttp@transtec.ch, 2017) Shettigar, A.K.; Prabhu B, S.; Malghan, R.; Rao, S.S.; Herbert, M.
In this paper, an attempt has been made to apply the neural network (NN) techniques to predict the mechanical properties of friction stir welded composite materials. Nowadays, friction stri welding of composites are predominatally used in aerospace, automobile and shipbuilding applications. The welding process parameters like rotational speed, welding speed, tool pin profile and type of material play a foremost role in determining the weld strength of the base material. An error back propagation algorithm based model is developed to map the input and output relation of friction stir welded composite material. The proposed model is able to predict the joint strength with minimum error. Â© 2017 Trans Tech Publications, Switzerland.
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.
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.
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. © 2017
Biofuel Production, Performance, and Emission Optimization A Comprehensive Approach to Modelling and Optimization
(Springer, 2025) Gowdru Chandrashekarappa, P.G.C.; Ajith, B.S.; Jagadish; Shettigar, A.K.; Samuel, O.D.
This book explores the urgent quest for sustainable energy solutions by examining potential renewable energy sources that meet global demands. As fossil fuels deplete at an alarming rate, this book addresses the critical challenges in selecting sustainable feedstocks and optimizing processes for industrial-scale biodiesel production. With a focus on Garcinia-gummi-gutta seeds as a promising feedstock, the book provides a detailed analysis of oil extraction, biofuel conversion, and the practical application of biodiesel in diesel engines. Key concepts explored include selecting and optimizing transesterification variables, engine performance, and emission characteristics. The authors employ cutting-edge tools such as statistical design of experiments and artificial intelligence to offer insights into biodiesel production’s physics, kinetics, and mechanics. Readers will discover experimental results, intelligent modeling techniques, and optimization strategies that enhance biodiesel yield and engine efficiency while minimizing emissions. This resource is designed for engineers and researchers in renewable energy and biofuel production. It offers a systematic framework from feedstock selection to engine optimization, making it invaluable for those seeking to advance their knowledge in sustainable energy solutions. Whether you’re a novice or a seasoned professional, this book provides the tools and insights needed to drive innovation in biodiesel production at an industrial scale. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
Comparison of Response Surface Methodology (RSM) and Machine Learning Algorithms in Predicting Tensile Strength and Surface Roughness of AA8090/B4C Surface Composites Fabricated by Friction Stir Processing
(Springer Science and Business Media Deutschland GmbH, 2024) Adiga, K.; Herbert, M.A.; Rao, S.S.; Shettigar, A.K.; Shrivathsa, T.V.; Tapariya, R.
Friction stir processing is an innovative solid-state process, widely utilized for surface composite fabrication, material property enhancement, and microstructural modification. Rotational speed, traverse speed, groove width, and axial force are key FSP parameters that improve the characteristics of surface composites (SCs). This work makes use of FSP to fabricate AA8090/B4C SCs by altering parameters within ranges. Response variables include ultimate tensile strength (UTS) and surface roughness (SR). Central composite design (CCD) of response surface methodology (RSM) leads trials, establishing a mathematical relationship between input parameters and UTS/SR. The modelsâ€™ adequacy is validated using ANOVA, which investigates the impact of input parameters on UTS and SR. This study also looks into machine learning regression methodologies for UTS and SR forecasting in AA8090/B4C SCs. The ML algorithms are evaluated by utilizing performance metrics like coefficient of determination (R2) and root mean squared error (RMSE). Predicted UTS and SR values from RSM are compared with machine learning outcomes. Â© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Control factor optimization for friction stir processing of AA8090/SiC surface composites
(Elsevier B.V., 2024) Adiga, K.; Herbert, M.A.; Rao, S.S.; Shettigar, A.K.
Friction Stir Processing is a state-of-the-art technology for microstructure refinement, material property enhancement, and surface composites fabrication. This investigation concentrates on AA8090/SiC surface composites produced via friction stir processing. Experiments were conducted by varying the following friction stir processing parameters: Tool rotational speed (800–1400 rpm), Tool traverse speed (25–75 mm/min), and Groove width (1.0–1.8 mm). Response measures encompassed Ultimate Tensile Strength and surface roughness. Central Composite Design of Response Surface Methodology designed the experiments and mathematical relationships established between input parameters and ultimate tensile strength and surface roughness. Analysis of variance was used to test the model's adequacy. The models examined individual and interaction effects of input factors on ultimate tensile strength and surface roughness of surface composites. A combinations of input parameters was identified that yields the maximum ultimate tensile strength and minimum surface roughness. The current work employs the friction stir processing approach to synthesis near-net-shaped surface composites without additional machining by systematically optimizing process parameters. Results indicate that increasing tool rotational speed produces well-finished AA8090/SiC surface composites with decreased strength. In contrast, increased tool traverse speed and groove width generate surface composites with rougher surfaces and higher strength. Surface and contour plots further explored the influence of parameter interactions on responses. © 2024 The Authors
Development of machine learning regression models for the prediction of tensile strength of friction stir processed AA8090/SiC surface composites
(Institute of Physics, 2024) Adiga, K.; Herbert, M.A.; Rao, S.S.; Shettigar, A.K.; Vasudeva, T.V.
Friction Stir Processing is a state-of-the-art technology for microstructure refinement, material property enhancement, and fabrication of surface composites. Machine learning approaches have garnered significant interest as prospective models for modeling various production systems. The present work aims to develop four machine learning models, namely linear regression, support vector regression, artificial neural network and extreme gradient boosting to predict the influence of FSP parameters such as tool rotational speed, tool traverse speed and groove width on ultimate tensile strength of friction stir processed AA8090/SiC surface composites. These models were developed through Python programming and the original dataset was divided into 80% for the training phase and 20% for the testing phase. The performance of the models was evaluated by root mean squared error, mean absolute error and R2. Based on the results and graphical visualization, it was observed that the XGBoost model outperformed other models with high accuracy in predicting UTS of AA8090/SiC surface composites. © 2024 The Author(s). Published by IOP Publishing Ltd.
Electro-discharge machining of microholes on 3d printed Hastelloy using the novel tool-feeding approach
(KeAi Publishing Communications Ltd., 2025) Korgal, A.; Shettigar, A.K.; P, N.K.; Kumar, N.; Bindu Madhavi, B.M.
Hastelloy, a nickel-based superalloy renowned for its exceptional resistance to corrosion at high temperatures, is widely used in sectors such as nuclear, aerospace, chemical processing, and pharmaceuticals. Microelectrical discharge machining (?-EDM) is crucial for generating microholes and channels on Hastelloy. Since it effectively addresses difficulties like work hardening, high strength & wear resistance, and low thermal conductivity in traditional machining. Microholes play a major role in many critical components for precise control of fluids in fuel injectors, managing heat in turbine blades, controlled gas exchange, etc. The current research investigates the drilling of 8:1 aspect ratio microholes machined by 400 ?m diameter electrodes. This study investigated the influence of tool material (tungsten carbide, carbide drill bit, and brass) on ?-EDM performance. Compared to tungsten carbide and carbide drill bits, brass exhibited significantly lower electrode wear, leading to more precise microholes with reduced overcut and taper angle. However, brass also required a substantially longer machining time. Carbide drill bits offered a balance between wear resistance, machining time, and overcut/taper angle. © 2024 The Authors
Experimental investigations on the milling characteristics of Cu alloys and additively manufactured CuCrZr
(Institute for Problems in Mechanical Engineering, Russian Academy of Sciences, 2025) Mundla, S.R.; Vendan, S.A.; Paul, C.P.; Shettigar, A.K.; Jambagi, S.C.
Copper is one of the widely used materials in various fields such as automotive, electronics, aviation, etc. The inherent property of copper makes it useful in wide variety of applications. The features on different components using the copper material can be made using different manufacturing techniques. However, post processing is one of the inevitable steps in any manufacturing process. Machining is one of the widely used post processing. There are multiple varieties of milling process. Among them, end milling process is widely used for making the slots. Three important process parameters in end milling process are depth of cut, cutting rate and feed rate. In this experimental approach, the copper is subjected to end milling operation by varying the aforementioned input parameters. In this fast-moving world, any manufacturing industry aims to produce the features with good dimensional accuracy with minimal amount of tool wear. Hence, the output responses selected are surface roughness and the tool wear. This research investigates the machining behavior of pure copper (Cu) and additively produced CuCrZr alloys to assess how fabrication methods affect processability. Pure copper, recognized for exceptional thermal / electrical conductivity, is compared against additively manufactured CuCrZr, which retains copper’s advantages while offering improved strength and wear resistance through alloy composition. During the milling process the following parameters such surface quality, cutting forces, tool degradation, and removal rates are reviewed through proper analysis. Compared to commercial copper, CuCrZr is more difficult to machine because it requires precise control over machining parameters to attain superior surface quality during milling. It is found that the CS and FR parameters balance material removal rate while controlling surface quality in both materials. As–built CuCrZr finds demand in high–performance applications such as heat exchangers, rocket engine components, and electrical contacts wherein strength, excellent thermal conductivity and additive manufacturability are critical. © S.R. Mundla, S. Arungalai Vendan, P. Paul, A.K. Shettigar, S.C. Jambagi, 2025.
Exploring welding parameter effects on friction stir weld joints in aluminum 8006 alloy using response surface methodology
(MIM RESEARCH GROUP, 2025) Chandrakumar, C.; Sogalad, I.; Shettigar, A.K.; Korgal, A.; Nagaral, M.
This study aims to develop mathematical models that can predict the characteristics related to mechanics, such as microhardness and impact resistance, of Aluminum that has been friction stir-welded 8006 alloy joints with 95% confidence. The four process parameters tool tilt angle, welding speed, tool pin shape, and rotating speed were systematically varied across three levels. Following a response surface and central composite design approach that is face-centered, the influence of different factors on the mechanical properties of aluminum 8006 alloy joints was assessed. The highest impact toughness of 58 joules was observed in joints specially prepared by a cylindrical threaded pin profile tool with a 1° tilt angle operating at 800 RPM and a feed rate of 20 mm/min and test was conducted at room temperature. Additionally, it was investigated how process factors affected impact toughness by ANOVA and the results revealed that the tool pin geometry is identified as the most significant process variable on impact toughness, contributing 52.52%, thereafter the tool tilt angles (15.53%), rotating speed (8.80%), and welding speeds (5.84%). The findings showed that, for impact toughness, the tool tilt angle and pin shape were more important than welding speed and tool rotation speed, but the tool pin profile and the welding speed showed governance over rotational speed and angle of tilt in case of hardness. The joints achieved a maximum hardness of 166 VHN at stir zone of the welded specimen made from a threadless taper pin tool for the speed of 1200 rpm, the tool was tilted at 2 degrees while welding at a speed of 40 mm/min. Finally, the effects of process parameters on the microstructure of friction stir welded Aluminum 8006 alloys were addressed and discussed. © 2025 MIM Research Group. All rights reserved.
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
Influence of welding process parameters on microstructure and mechanical properties of friction stir welded aluminium matrix composite
(2017) Prabhu, S.; Shettigar, A.K.; Rao, K.; Rao, S.; Herbert, M.
In this study, the effect of process parameters on microstructure and mechanical properties of friction stir welded aluminium matrix composites(AMC) have been explored. The results indicated that the recrystallized grain size at the bottom of the weld region is smaller than that at the top region due to difference in the heat transfer at the weld region. The joint strength of AMCs depends on proper selection of process parameters like tool rotational speed and welding speed. If process parameter values are beyond the optimal value, the joint strength decreases due to formation of defects. Maximum tensile strength is obtained for rotational speed of 1000 rpm and welding speed of 80mm/min. � 2017 Trans Tech Publications, Switzerland.
Influence of welding process parameters on microstructure and mechanical properties of friction stir welded aluminium matrix composite
(Trans Tech Publications Ltd ttp@transtec.ch, 2017) Prabhu B, S.; Shettigar, A.K.; Karthik, K.; Rao, S.S.; Herbert, M.
In this study, the effect of process parameters on microstructure and mechanical properties of friction stir welded aluminium matrix composites(AMC) have been explored. The results indicated that the recrystallized grain size at the bottom of the weld region is smaller than that at the top region due to difference in the heat transfer at the weld region. The joint strength of AMCs depends on proper selection of process parameters like tool rotational speed and welding speed. If process parameter values are beyond the optimal value, the joint strength decreases due to formation of defects. Maximum tensile strength is obtained for rotational speed of 1000 rpm and welding speed of 80mm/min. Â© 2017 Trans Tech Publications, Switzerland.
Investigating fretting wear behavior of LPBF processed AlSi10Mg alloy under variable frequency and heat treatment conditions
(Elsevier B.V., 2025) Nanjundaiah, R.S.; Rao, S.S.; Praveenkumar, K.; Selvan, C.P.; Ram Prabhu, T.; Sahay, S.; Manivasagam, G.; Shettigar, A.K.; Gowdru Chandrashekarappa, G.C.
This study examines the fretting wear behavior of AlSi10Mg alloy processed via Laser Powder Bed Fusion (LPBF) under different oscillation frequencies (5 Hz, 10 Hz, and 15 Hz) and heat treatment conditions (as-built, stress-relieved, T5, and T6) under a consistent load of 100 N. The fabricated and heat-treated samples were analyzed using X-ray diffraction, hardness testing, and residual stress measurements to evaluate dislocation density, hardenability, and the nature of residual stress. Fretting wear behavior was further assessed through evaluations of the coefficient of friction (COF), worn surface morphology, and wear volume loss using scanning electron microscopy (SEM) and 3D profilometry to understand the mechanism. Results indicated that the as-built samples exhibited superior resistance against fretting wear across all tested frequencies, a phenomenon attributed to their refined microstructure and higher dislocation density (FWHM: 0.213). The results show that lower frequencies primarily result in adhesive wear, with the oxide layer providing some protection, but higher frequencies accelerate abrasive and fatigue wear due to enhanced crack propagation and thermal softening. © 2025 The Authors