Conference Papers

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

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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.