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Träfflista för sökning "WFRF:(Anilkumar V.) "

Search: WFRF:(Anilkumar V.)

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1.
  • 2019
  • Journal article (peer-reviewed)
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2.
  • Anilkumar, V., et al. (author)
  • Impact of heat treatment analysis on the wear behaviour of al-14.2si-0.3mg-tic composite using response surface methodology
  • 2021
  • In: Tribology in Industry. - : Faculty of Engineering, University of Kragujevac. - 0354-8996 .- 2217-7965. ; 43:4, s. 590-602
  • Journal article (peer-reviewed)abstract
    • Al-14.2Si-0.3Mg Alloy reinforced with hard phased TiC coarse particulates (10 wt-%) was contrived using the liquid metallurgy route. The so fabricated aluminium metal matrix composites was made to undergo solutionising at 5250C for 12 hours in a heat treatment furnace and was subsequently water quenched to room temperature. The developed composite was then kept for age hardening at varying temperatures and time for enhanced tribological properties. A pin on disc Tribometer (ASTM-G99) was utilised to study the wear properties of the fabricated composite. Aging temperature (0C), applied load (N) and Aging time (hours) were chosen as the process parameters for analysing the material's resistance to wear. Using response surface methodology the influence of reinforcement in the wear properties of the composite was studied. The design of the regression equation was prepared and the impact of each experimental parameter was scrutinized. Results depict that with an increase in the aging temperature, aging time and load, there observed a variation in the materials wear properties. The worn-out surface of the metal matrix composite was then investigated with the help of the Scanning Electron Microscope (SEM).
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3.
  • Sivanantham, V., et al. (author)
  • Quantile correlative deep feedforward multilayer perceptron for crop yield prediction
  • 2022
  • In: Computers & electrical engineering. - : Elsevier. - 0045-7906 .- 1879-0755. ; 98
  • Journal article (peer-reviewed)abstract
    • Crop yield prediction is an essential one in agriculture. Crop yield protection is the science and practice of handling plant diseases, weeds, and other pests. Accurate information regarding the crop yield history is essential for making decisions regarding agricultural risk management. Many research studies have been undertaken for identifying crop productivity using various data mining techniques. However, the prediction accuracy of crop yields was not improved with minimum time consumption. To overcome the issues, a novel Quantile Regressive Empirical correlative Functioned Deep FeedForward Multilayer Perceptron Classification (QRECF-DFFMPC) Method is proposed for crop yield prediction. QRECF-DFFMPC Method comprises three layers such as input and output layer with one or more hidden layers. The input layer of deep neural learning receives several features and data from the dataset and then sent it to the hidden layer 1. In that layer, Empirical Orthogonal Function is used to select the relevant features with the help of orthogonal basis functions. After that, Quantile regression is used in the hidden layer 2 to analyze the features and produce the regression value for every data point. Then, the regression value of data points is sent to the output layer for improving the prediction accuracy and reducing the time complexity. Experimental evaluation is carried out on factors such as prediction accuracy, precision, and prediction time for several data points and the number of features. The result shows that the proposed technique enhanced the prediction accuracy and precision by 6% and 9% and reduces the prediction time by 32%, as compared to existing works. 
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  • Result 1-6 of 6

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