Faculty Publications
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Item 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.Item 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).Item 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.