Faculty Publications

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    Multi-objective Optimization of FSW Process Variables of Aluminium Matrix Composites Using Taguchi-Based Grey Relational Analysis
    (Springer Nature, 2019) Prabhu B, S.R.B.; Shettigar, A.K.; Herbert, M.A.; Rao, S.S.
    Successful joining of aluminium alloys using friction stir welding (FSW) opens a new window research in extending this technique to join aluminium matrix composites (AMCs). Current research is focused on optimization of process variables for multiple responses simultaneously. Experiments were performed using tool pin profile, rotational speed (RS) and welding speed (WS) as ideal process variables for multi-objective optimization in FSW of AMCs. Tensile strength, macro-hardness and elongation are considered as multi-response behaviour. Grey relational grade for the chosen multiple responses are obtained using grey analysis. Analysis of variance was utilized to understand the influence of process variables on the grey relational grade. Analysis reveals that RS and WS were the most influencing process variables on the output responses. Confirmation experiments were performed at optimized process variables to validate the present study. Predicted values were in good agreement with the experimental results. © 2019, Springer Nature Singapore Pte Ltd.
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    Real-Time Geometric Error (Form) Compensation on a Vertical Milling Machine
    (Springer Nature, 2024) Shanmugaraj, V.; Shruthi, G.; Shettigar, A.K.; Krishna, P.
    High-performance CNC milling machines are required for manufacturing precise components as there is a demand for consistency and quality are growing. The vital factor which plays a key role in the precision components manufacturing is the machine tools performance itself. Mainly, the causes of displacement errors are the effect of form errors, forces due to cutting actions, dynamic behavior of machine, etc. This paper proposes a new methodology in measuring and compensation of geometric error on a vertical milling machine which has three linear axes. The straightness error measured, and compensated for the individual axis with respect to the other two linear axes is discussed in this paper. A new methodology of applying flatness error correction is implemented by taking the current position of an axis into account. This error correction is implemented in real time in a vertical milling machine fitted with a CNC controller. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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    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.
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    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.
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    Investigation on microstructure and mechanical properties of Friction Stir Welded AA6061-4.5Cu-10SiC composite
    (Institute of Physics Publishing michael.roberts@iop.org, 2016) Herbert, M.; Shettigar, A.K.; Nigalye, A.V.; Rao, S.S.
    The application of Metal Matrix Composites (MMCs) is restricted by the availability of properly developed fabrication methods. The main challenge here is the fabrication and welding of MMCs in a cost effective way. In the present study, synthesis of AA6061-4.5%Cu- 10%SiC composite was done by stir casting method. The joining of MMCs was performed by Friction Stir Welding (FSW) using a combination of square and threaded profile pin tool (CSTPP). Further, the welded composite was evaluated for microstructure and joint properties. The microstructural characterization showed uniform distribution of refined fine grains and numerous small particles at nugget zone. The hardness at the stir zone is higher than that of the base material. The tensile test revealed 96% joint efficiency in transverse direction. © Published under licence by IOP Publishing Ltd.
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    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. © 2017 Trans Tech Publications, Switzerland.
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    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. © 2017 Trans Tech Publications, Switzerland.
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    Study on Mechanical and microstructural characteristics of Friction Stir Welded Aluminium Matrix composite
    (Elsevier Ltd, 2020) Prabhu B, S.R.B.; Shettigar, A.K.; Herbert, M.A.; Rao, S.S.
    Aluminium matrix composites (AMCs) are part of advanced materials, possesses capabilities to serve the present industrial needs due to its superior properties. Potential use of these AMCs in a particular application is limited if it is unable to join properly. In the present study AMCs are prepared by stir casting technique and welded by friction stir welding (FSW) process. FSW is performed using combined threaded and square profiled pin (CTSP). Further the welded joints were examined for microstructure and joint strength. Tensile test indicates that joint efficiency of 97 % is obtained, normal to the weld direction. Compare to the base material the nugget zone of weld region shows higher hardness. The microstructural study reveals that uniform distribution of finer grains are visible at nugget zone. © 2018 Elsevier Ltd.
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    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 models’ 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. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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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.