Institution
National Institute of Technology, Karnataka
Education•Mangalore, Karnataka, India•
About: National Institute of Technology, Karnataka is a education organization based out in Mangalore, Karnataka, India. It is known for research contribution in the topics: Computer science & Corrosion. The organization has 5017 authors who have published 7057 publications receiving 70367 citations.
Topics: Computer science, Corrosion, Cloud computing, Microstructure, Alloy
Papers published on a yearly basis
Papers
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TL;DR: In this article, a highly birefringent hybrid cladding photonic crystal fiber (PCF) is proposed, which exhibits very high bireringence in the order of 10 - 2 at communication wavelengths.
29 citations
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TL;DR: Forward and reverse modelling of squeeze casting process by utilizing the neural network-based approaches have shown that both models are capable to make better predictions, and the models can be used by any novice user without knowing much about the mechanics of materials and the process.
Abstract: The present work deals with the forward and reverse modelling of squeeze casting process by utilizing the neural network-based approaches. The important quality characteristics in squeeze casting, namely surface roughness and tensile strength, are significantly influenced by its process variables like pressure duration, squeeze pressure, and pouring and die temperatures. The process variables are considered as input and output to neural network in forward and reverse mapping, respectively. Forward and reverse mappings are carried out utilizing back propagation neural network and genetic algorithm neural network. For both supervised learning networks, batch training is employed using huge training data (input-output data). The input-output data required for training is generated artificially at random by varying process variables between their respective levels. Further, the developed model prediction performances are compared for 15 random test cases. Results have shown that both models are capable to make better predictions, and the models can be used by any novice user without knowing much about the mechanics of materials and the process. However, the genetic algorithm tuned neural network (GA-NN) model prediction performance is found marginally better in forward mapping, whereas BPNN produced better results in reverse mapping.
29 citations
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TL;DR: In this article, CTPO produced by thermal pyrolysis (400 °C, 0.2 bar, 4 rpm, 5 h) of scrap automotive tires in a rotating autoclave reactor (8-tons) has been upgraded using silica gel as adsorbent and petroleum ether as diluent.
29 citations
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TL;DR: In this article, the compressive behavior of 3D printed three-phase syntactic foams under quasi-static strain rates (0.001, 0.01 and 0.1) is investigated.
29 citations
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TL;DR: In this paper, the influence of mixed convection in a steady incompressible laminar boundary layer flow for an exponentially decreasing free stream velocity in presence of surface mass transfer and heat source or sink is explored.
29 citations
Authors
Showing all 5100 results
Name | H-index | Papers | Citations |
---|---|---|---|
Ajay Kumar | 53 | 809 | 12181 |
Bhiksha Raj | 51 | 359 | 13064 |
Alexander P. Lyubartsev | 49 | 184 | 9200 |
Vijay Nair | 47 | 425 | 10411 |
Sukumar Mishra | 44 | 405 | 7905 |
Arun M. Isloor | 38 | 261 | 6272 |
Vinay Kumaran | 36 | 262 | 4473 |
M. C. Ray | 30 | 115 | 2662 |
Airody Vasudeva Adhikari | 30 | 119 | 2832 |
Ian R. Lane | 27 | 129 | 2947 |
D. Krishna Bhat | 26 | 95 | 1715 |
Anurag Kumar | 26 | 126 | 2276 |
Soma Biswas | 25 | 127 | 2195 |
Chandan Kumar | 25 | 66 | 1806 |
H.S. Nagaraja | 23 | 90 | 1609 |