B. V. Chaluvaraju

B. V. Chaluvaraju

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B. V. Chaluvaraju

Department of Physics, Bangalore Institute of Technology, Bangalore, India

Research Focus & Metrics

Academic Career Length

12Years (2015-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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25 March 2021 in Symmetry
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Journal Article
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CAChandrashekar Anjinappa
Chandrashekar Anjinappa
BCB. V. Chaluvaraju
B. V. Chaluvaraju
Asif AfzalAsif Afzal
Asif Afzal
DVDenis Vinnik
Denis Vinnik
AKAbdul razak kaladgi
Abdul razak kaladgi
VTVineet Tirth
Vineet Tirth
Nano aluminum oxide was prepared by the combustion method using aluminum nitrate as the oxidizer and urea as a fuel. Characterization of synthesized materials was performed using SEM (scanning electron microscope), powder XRD (X-ray diffraction), FTIR (Fourier transform infrared spectroscopy), and TEM (transmission electron microscope). Al-Mg/Al2O3 (2, 4, 6, and 8 wt%) metal matrix nanocomposites were prepared by liquid metallurgy route-vertex technique. The homogeneous dispersion of nano Al2O3 particles in Al-Mg/Al2O3 metal matrix nanocomposites (MMNCs) was revealed from the field emission SEM analysis. The reinforcement particles present in the matrix were analyzed through energy-dispersive X-ray spectroscopy method. The properties (corrosion and mechanical) of the fabricated composites were evaluated. The mechanical and corrosion properties of the prepared nanocomposites initially increased and then decreased with the addition of nano Al2O3 particles in Al-Mg Matrix. The studies show that, the presence of 6 wt% of nano Al2O3 particles in the matrix improved the properties of other combinations of nano Al2O3 in the Al-Mg matrix material. Further, the Al-Mg/Al2O3 (6 wt%) MMNCs are joined by friction stir welding and evaluated for microstructural, mechanical, and corrosion properties. Al-Mg/Al2O3 MMNCs may find applications in the marine field. The response surface method (RSM) was used for the optimization of tensile strength, Young’s modulus, and microhardness of the synthesized material which resulted in a 95% of statistical confidence level. Artificial neural network (ANN) analysis was also carried out which perfectly predicted these two properties. The ANN model is optimized to obtain 99.9% accurate predictions by changing the number of neurons in the hidden layer.

14 October 2015 in Journal of Materials Science: Materials in Electronics
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BCB. V. Chaluvaraju
B. V. Chaluvaraju
SGSangappa K. Ganiger
Sangappa K. Ganiger
MVMurugendrappa Malalkere Veerappa
Murugendrappa Malalkere Veerappa

11 April 2017 in Journal of Materials Science: Materials in Electronics
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Journal Article
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BCB. V. Chaluvaraju
B. V. Chaluvaraju
URU. P. Raghavendra
U. P. Raghavendra
TPT. S. Pranesha
T. S. Pranesha
MVMurugendrappa Malalkere Veerappa
Murugendrappa Malalkere Veerappa

30 October 2018 in Materials Today: Proceedings
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Journal Article
Closed access
BBB.M. Bharathesh
B.M. Bharathesh
VAV.B. Aaditya
V.B. Aaditya
APAkhil D. Prabhu
Akhil D. Prabhu
BCB.V. Chaluvaraju
B.V. Chaluvaraju
URU.P. Raghavendra
U.P. Raghavendra
MVMurugendrappa Malalkere Veerappa
Murugendrappa Malalkere Veerappa

16 September 2022 in Journal of Materials Science: Materials in Electronics
Verified
Journal Article
Closed access
PDF file
BCB. V. Chaluvaraju
B. V. Chaluvaraju
SGSangappa K. Ganiger
Sangappa K. Ganiger
MVMurugendrappa Malalkere Veerappa
Murugendrappa Malalkere Veerappa

30 October 2018 in Materials Today: Proceedings
Verified
Journal Article
Closed access
APAkhil D. Prabhu
Akhil D. Prabhu
BBB.M. Bharathesh
B.M. Bharathesh
VAV.B. Aaditya
V.B. Aaditya
BCB.V. Chaluvaraju
B.V. Chaluvaraju
URU.P. Raghavendra
U.P. Raghavendra
MMM.V. Murugendrappa
M.V. Murugendrappa