Institution
Vignan University
Education•Guntur, Andhra Pradesh, India•
About: Vignan University is a education organization based out in Guntur, Andhra Pradesh, India. It is known for research contribution in the topics: Control theory & CMOS. The organization has 1138 authors who have published 1381 publications receiving 7798 citations.
Topics: Control theory, CMOS, Cement, Machining, Wireless sensor network
Papers
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TL;DR: In this article, the effect of cutting parameters on work piece vibration, roughness on machined surface and volume of metal removed in boring of steel (AISI1040) was estimated.
96 citations
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TL;DR: Predictive models like response surface methodology, artificial neural network and support vector machine were used to predict the surface roughness and root mean square of work piece vibration.
Abstract: In this paper, statistical models were developed to investigate effect of cutting parameters on surface roughness and root mean square of work piece vibration in boring of stainless steel. A mixed level design of experiments was prepared with process variables of nose radius, cutting speed and feed rate. According to design of experiments, eighteen experiments were conducted on AISI 316 stainless steel with PVD coated carbide tools. Surface roughness, tool wear and vibration of work piece were measured in each experiment. A laser Doppler vibrometer was used to measure vibration of work piece in the form of acousto optic emission signals. These signals were processed and transformed in to different frequency zones using a fast Fourier transformer. Analysis of variance was used to identify significant cutting parameters on surface roughness and root mean square of work piece vibration. Predictive models like response surface methodology, artificial neural network and support vector machine were used to predict the surface roughness and root mean square of work piece vibration. Cutting parameters were optimized for minimum surface roughness and root mean square of work piece vibration using a multi response optimization technique.
96 citations
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94 citations
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01 Dec 2013TL;DR: The overview of some of the existing proactive, reactive and hybrid multicast routing protocols over WMNs with its strengths and weaknesses is given.
Abstract: Wireless Mesh Networks comes under the category of mobile ad-hoc networks with fixed positions of nodes to communicate to the internet through a single gateway or more than one gate way. In order to provide a multi point communication within the mesh network, a multicast routing protocol is required. Multicast routing is a key technology for modern communication networks. Multicast Routing becomes a prominent technology for wireless communication networks various multicast routing protocols are developed for internet and ad-hoc communications This paper gives the overview of some of the existing proactive, reactive and hybrid multicast routing protocols over WMNs with its strengths and weaknesses. The following multicast routing protocols are selected for their performance comparison; they are On Demand Multicast Routing Protocol(ODMRP), Multicast Ad hoc On Demand distance Vector (MAODV) Protocol, Multicast Open Shortest Path First (MOSPF), Ad hoc Multicasting Routing Protocol(AM Route) and Optimized Polymorphic Hybrid Multicast Routing Protocol(OPHMR). Among them, MOSPF and AM Route is a proactive routing protocol and OPHRM is a Hybrid routing protocol while MAODV and ODMRP are reactive multicast routing protocols.
93 citations
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TL;DR: In this article, the powder is annealed at three different temperatures (400 o C, 500 o C and 1000 o C) for one hour and X-ray diffraction patterns confirm the spinel structure of the samples.
91 citations
Authors
Showing all 1166 results
Name | H-index | Papers | Citations |
---|---|---|---|
Muthukaruppan Alagar | 40 | 316 | 5914 |
Ebenezer Daniel | 40 | 180 | 5597 |
P. B. Kavi Kishor | 30 | 123 | 3486 |
V. Purnachandra Rao | 26 | 59 | 1723 |
Muddu Sekhar | 24 | 135 | 1929 |
Anandarup Goswami | 23 | 44 | 5427 |
Reddymasu Sreenivasulu | 20 | 58 | 925 |
Murthy Chavali | 20 | 105 | 1699 |
Krishna P. Kota | 20 | 42 | 1172 |
Naveen Mulakayala | 17 | 39 | 937 |
Tondepu Subbaiah | 16 | 65 | 773 |
Bharat Kumar Tripuramallu | 15 | 34 | 574 |
Avireni Srinivasulu | 13 | 97 | 626 |
Abhinav Parashar | 13 | 29 | 375 |
Umesh Chandra | 13 | 39 | 550 |