Faculty Publications
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Item Effect of Rotational Speeds on the Cast Tube During Vertical Centrifugal Casting Process on Appearance, Microstructure, and Hardness Behavior for Al-2Si Alloy(Springer Boston, 2015) Rao, R.A.; Tattimani, M.S.; Rao, S.S.The flow of molten metal plays a crucial role in determining casting quality. During rotation of the mold, melt flow around its inner circumference determines the final configurations and properties of the cast tube. In this paper, Al-2Si alloy is cast in the vertical mold at the various rotational speeds of the mold. The uniform cylinder tube is formed at a rotational speed of 1000 rpm, while before and beyond this speed, irregular-shaped cast tube is formed. Finally, fine structured grain size with high hardness value is found in uniform cast tube compared with others. © 2014, The Minerals, Metals & Materials Society and ASM International.Item Understanding Melt Flow Behavior for Al-Si Alloys Processed Through Vertical Centrifugal Casting(Taylor and Francis Inc. 325 Chestnut St, Suite 800 Philadelphia PA 19106, 2015) Rao, R.A.; Tattimani, M.S.; Rao, S.S.The objective of this article is to investigate the appearance, microstructure, and hardness of Al-Si alloys Al-12Si and Al-17Si in vertical centrifugal casting process. During rotation of the mold, molten metal flow affects the formation of uniform cylinder. In this study, flow of molten metal for Al-Si alloys at different rotational speeds is focused. It is found that for Al-17Si alloy a uniform cast tube is observed for 1000 rpm, whereas for Al-12Si it is at 1200 rpm; above and below these speeds, irregular cast tubes are formed. Finally, fine structured grain size with high hardness value is found in a uniform cast tube in comparison with others. © 2015 Taylor & Francis Group, LLC.Item Evaluation of Microstructure, Hardness and Mechanical Properties of Friction Stir Welded Al–Ce–Si–Mg Aluminium Alloy(Korean Institute of Metals and Materials, 2020) D’Souza, A.D.; Rao, S.S.; Herbert, M.A.Abstract: A vast majority of the research on friction stir welding(FSW) is mainly focused on welding of aluminium alloys. The research studies in this paper are based on one such alloy known as Al–Ce–Si–Mg aluminium alloy, of which, the microstructure and other mechanical properties of the friction stir welded joints are yet to be studied. The plates of Al–Ce–Si–Mg aluminium alloy were friction stir welded using a non consumable, rotating tool with triangular profile pin and circular shoulder, with different combinations of tool rotation speeds and weld speeds. The microstructure, hardness and mechanical properties of the weld were analyzed. The microstructure of the weld zones revealed that, the average grain size at the bottom of the Nugget Zone (NZ) is 5 ± 0.12 ?m and gradually increases to 15 ± 0.23 ?m at the top of the NZ. In the TMAZ the grain size is 20 ± 0.14 ?m and is bigger compared to the NZ. In the HAZ, the grain size is around 37 ± 0.21 ?m and is bigger than that in the TMAZ. The maximum Vickers hardness value at the NZ center is 231.9 ± 2 Hv, and uniformly reduces to 100 ± 2.4 Hv in the TMAZ and 65 ± 1.3 Hv in the HAZ and then increases to 98 ± 1 Hv in the base material (BM). The maximum ultimate tensile strength (UTS) of FSW joint was found to be around 102.55 ± 3 MPa with elongation at fracture of 2.5%. The BM UTS was 154 ± 4.5 MPa. For a tool rotation speed of 800RPM and a weld speed of 20 mm/min a maximum joint efficiency of 67%. was obtained. Hence these were chosen as the optimum process parameters to join the alloy Al–Ce–Si–Mg by FSW. Graphic Abstract: [Figure not available: see fulltext.] © 2019, The Korean Institute of Metals and Materials.Item 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 AuthorsItem 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.