Faculty Publications
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Item 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.Item Predictive Intelligent System Development for Disease Classification in Diagnostic Applications(Springer Science and Business Media Deutschland GmbH, 2024) Shrivathsa, T.V.; Rao, S.S.; Karanth, P.N.; Adiga, K.; Mahabala, M.; Dakappa, P.H.; Prasad, K.With ever increasing explosion in information domain and demand for highest accuracy in medical diagnosis, the existence of a reliable, accurate prediction system is the need of the hour. In this work, an effective prediction system has been developed for accurate classification of undifferentiated ailments using a unique approach. Prediction of undifferentiated diseases at an early stage always helps in better diagnosis. Illnesses like tuberculosis, non-tubercular bacterial infection, dengue fever, non-infectious diseases have regular manifestation of fever. In present work, the uniqueness lies in the use of only temperature data of the patient being referred in predicting the nature of fever, with highest degree of accuracy, instead of several self-defined parameters over limited interval of time. The system has been developed based on artificial intelligent technique, and optimization has been achieved by assessing the performance of different classifiers available. Using prediction model with classifiers, decision can take over comparative results between different classifier algorithms. A result of predictive system defines the combination of good classifier and system developed. Accuracy score and other salient parameters describe the complete picture of the system. Predictive model development in this work proved to be one of the best assistant tools to a doctor to take call over the disease crucial period. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Item Applications of reinforcement particles in the fabrication of Aluminium Metal Matrix Composites by Friction Stir Processing - A Review(EDP Sciences, 2022) Adiga, K.; Herbert, M.A.; Rao, S.S.; Shettigar, A.Composite materials possess advantages like high strength and stiffness with low density and prove their essentiality in the aviation sector. Aluminium metal matrix composites (AMMC) find applications in automotive, aircraft, and marine industries due to their high specific strength, superior wear resistance, and lower thermal expansion. The fabrication of composites using the liquid phase at high temperature leads to the formation of intermetallics and unwanted phases. Friction Stir Processing (FSP) is a novel technique of composite fabrication, with temperature below the melting point of the matrix, achieving good grain refinement. Many researchers reported enhancement of mechanical, microstructure, and tribological properties of AMMC produced by the FSP route. The FSP parameters such as tool rotational speed, tool traverse speeds are found to be having greater impact on uniform dispersion of particles. It is observed that the properties such as tensile strength, hardness, wear and corrosion resistance, are altered by the FSP processes, and the scale of the alterations is influenced significantly by the processing and tool parameters. The strengthening mechanisms responsible for such alterations are discussed in this paper. Advanced engineering materials like shape memory alloys, high entropy alloys, MAX phase materials and intermetallics as reinforcement material are also discussed. Challenges and opportunities in FSP to manufacture AMMC are summarized, providing great benefit to researchers working on FSP technique. ©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.Item Optimization of process parameters for friction stir processing (FSP) of AA8090/boron carbide surface composites(Springer Science and Business Media Deutschland GmbH, 2024) Adiga, K.; Herbert, M.A.; Rao, S.S.; Shettigar, A.K.Friction Stir Processing (FSP) is an innovative and promising technique for microstructure refinement, material property enhancement, and surface composite production. The current study describes the fabrication of AA8090/boron carbide surface composites (SCs) by FSP. Experimental studies were conducted by varying the FSP parameters, specifically the rotational speed (800–1400 rpm), traverse speed (25–75 mm/min), and groove width (1–1.8 mm). Ultimate Tensile Strength (UTS), Surface Roughness (SR), and Percentage Elongation (El) were used as response measures. Experiments were planned based on the central composite design (CCD) of Response Surface Methodology (RSM) and a mathematical relationship between the input parameters and UTS, SR and El, and were obtained by RSM. The model adequacy was tested using analysis of variance (ANOVA). The models enabled the examination of individual and interaction effects of input parameters on the UTS, SR, and El of the produced SCs. AA8090/boron carbide SC strength was optimal of 366 MPa at 800 rpm, 75 mm/min, and 1.8 mm and optimal 21.13% elongation at 1400 rpm, 25 mm/min, and 1 mm. A smoother surface with 0.82-μm roughness was optimal at 1400 rpm, 25 mm/min, and 1.2 mm. The present study uses the FSP method to synthesize near-net-shaped SCs without further machining by systematically selecting process parameters. The study shows that the increase in rotational speed during AA8090/boron carbide SC fabrication produces composites with a good surface finish, lower UTS, and good ductility. However, the increase in the other two parameters, namely, traverse speed and groove width, produces low ductile composites with rougher surfaces and higher strengths. Graphical abstract: (Figure presented.) © International Institute of Welding 2024.Item 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 AuthorsItem Investigation of the effect of process parameters on the mechanical properties of friction stir additive manufactured (FSAM) AA8090 alloy(Elsevier B.V., 2025) D A P, P.; Shettigar, A.K.; Herbert, M.A.; Korgal, A.; Adiga, K.Friction Stir Additive Manufacturing (FSAM), an emerging technique, falls under the category of sheet lamination additive manufacturing. It employs a layer-by-layer fabrication where all the plates should be flat and of the same size. This process was developed to fabricate near-net-shaped components and refined microstructures. FSAM has been extensively used in the fabrication of aluminum alloys for aerospace applications. In this work, FSAM has been carried out for AA8090 aluminum alloy. AA8090 is the second-generation Al-Li alloy with 2.3 % Li, lightweight, 10 % lower density and 11 % higher modulus than the existing commercial 2014 and 2024 Al alloy. The experiments were carried out at rotational speed (1000 – 2000 rpm), traverse speed (45–55 mm/min) and 1° constant tilt angle. The macrostructure and microstructure analysis were carried out. This was followed by microhardness and tensile test analysis. The microhardness was carried out at nine points on each layer and tensile specimen was made according to ASTM E8 standard. The maximum reduction in grain size, which is 62 %, maximum hardness value 113 HV and maximum tensile value 346.8 MPa were observed at 2000 rpm. The size of the grains decreased from the top layer into the bottom layers. The maximum hardness for all the experiments was observed in the re-stir zone of the specimens. It was concluded that with increase in process parameters, better mechanical and microstructural properties can be achieved. The fractography analysis showed the presence of dimples and tear ridges indicating a ductile fracture. © © 2025. Published by Elsevier B.V.