Conference Papers

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

Browse

Subject: search.filters.subject.ANOVA

Search Results

Now showing 1 - 5 of 5
  • No Thumbnail Available
    Item
    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.
  • No Thumbnail Available
    Item
    Effect of Lateral Vibrations during Directional solidification on Mechanical Properties of Al-18%wt Si Alloys
    (Elsevier Ltd, 2018) Ramesh Babu, N.; Ramesh, M.R.; Kiran Aithal, S.; Kotresh, K.
    Experimental Investigation on the effect of mold vibration on the mechanical properties of Al-18%WtSi have been carried out using Taguchi Technique, as the molten metal at 750°C is poured in to the insulated mold which is subjected to lateral vibrations, directional solidification takes place due to the presence of chill at the bottom which gives rise to a functionally graded alloy. Due to the excitation, silicon present in the molten metal precipitates along its length. The castings are prepared with varying chill materials and chill volumes without vibrations and with a vibrating frequency of 50Hz. ANOVA was applied to optimize the process parameters. Hardness and Tensile test for the samples was carried out. Improvements in the properties can be attributed to the grain refinement of the casting prepared under vibrations. © 2017 Elsevier Ltd.
  • No Thumbnail Available
    Item
    Assessment and Prediction of Specific Energy Using Rock Brittleness in Rock Cutting
    (Springer Nature, 2020) Raghavan, V.; Murthy, C.S.N.
    In this study, we used picks with point attack angles of 45°, 50°, 55°, and 65° and 45°, 55°, and 65° attack angles in rock cutting experiments. The main objective is to estimate specific energy during the cutting process based on rock brittleness and study the influence of attack angle on specific energy. From the experimental data, we compared the obtained results using multiple linear regressions and ANOVA to predict the specific energy and found that the model developed were statistically significant. R2 of the brittleness B4 is 0.79 in comparision with R2 of density, UCS, BTS and abrasivity as 0.74, 0.83, 0.84 and 0.73. Specific energy not only be predicted from density, UCS, BTS, abrasivity, it can also be predicted using rock brittleness. © 2020, Springer Nature Switzerland AG.
  • No Thumbnail Available
    Item
    Optimal Parameters Identification of Quarter Car Simulink Model for Better Ride Comfort and Road Holding
    (Springer Science and Business Media Deutschland GmbH, 2021) Puneet, P.; Hegale, A.; Kumar, H.; Gangadharan, K.V.
    Advancement in vehicle technology has explored many possibilities for improvement, keeping customer satisfaction in mind. One major criterion which a passenger always wishes to possess is ride comfort. But the suspension parameters suitable for a good ride comfort may not support another salient feature called road holding. Hence, in this work an attempt has been made to simultaneously improve the ride comfort and road holding, using a quarter car test model using MATLAB Simulink for a commercial light motor vehicle. Initially, a commercially available passive damper of light motor vehicle has been characterized using dynamic testing machine (DTM) in order to obtain its force–displacement behavior and damping nature. A design of experiment (DOE) has been conducted by taking vehicle velocity, sprung mass, spring stiffness and damping coefficient into consideration, for experimentation using quarter car model. Regression equations have been extracted for relating the problem parameters to both ride comfort and road holding. Analysis of variance (ANOVA) has been used to know the influence of each parameter toward the target response. In the later stage, response surface methodology optimization technique has been used in order to optimize the parameters for better ride comfort and road holding. Optimized parameters are substituted again in the quarter car model, to validate the results obtained during optimization. The present work concluded with an optimal ride comfort and road holding and proved the effectiveness of optimization technique in achieving so. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
  • No Thumbnail Available
    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.