Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Rao, S.S.Subject: search.filters.subject.ANOVA

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    Prediction of surface finish and optimization of machining parameters in turning
    (2012) Prasad, D.; Krishna, P.; Rao, S.S.
    Surface roughness plays a crucial role in the functional capacity of machined parts. In this work, experiments were carried out on a conventional lathe for different cutting parameters namely feed, spindle speed, depth of cut and tool nose radius according to Taguchi Design of Experiments. Radial acceleration readings were taken with an accelerometer. Optimum cutting parameters and their level of significance were found using Taguchi analysis (ANOVA). Regression analysis was carried out to identify whether the experimental roughness values have fitness characteristic with the process parameters. Recurrence Plots (RP) were obtained using the sensor signals which determine surface roughness qualitatively and Recurrence Quantification Analysis (RQA) technique was used to quantify the RP obtained. Surface finish was predicted using a feed forward back propagation neural network with RQA parameters, cutting parameters and acceleration data as inputs to the network. The validity and reliability of the methods were verified experimentally. © (2012) Trans Tech Publications.
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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.