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Sureshkumar, B.
- Analysis of Machining Parameters in Turning Operation on Duplex 2205 by using RSM for Vehicle Structure
Abstract Views :485 |
PDF Views:135
Authors
Affiliations
1 Dept. of Mech. Engg., K. Ramakrishnan College of Tech. Samayapuram, Trichy, Tamil Nadu, IN
1 Dept. of Mech. Engg., K. Ramakrishnan College of Tech. Samayapuram, Trichy, Tamil Nadu, IN
Source
International Journal of Vehicle Structures and Systems, Vol 11, No 1 (2019), Pagination: 113-116Abstract
Turning is the machining process carried out to make cylindrical parts. Since the process is economical and the flexibility of turning operation is high, the process has become highly versatile among the industrial scenario. The design of experiments concept along with response surface methodology is used to analyze the machining parameters such as spindle seed, feed rate and depth of cut, of the turning operation. Three levels of spindle speed, feed rate and depth of cut are used as input parameters and their corresponding responses such as material removal rate (M.R.R), surface roughness, feed force, thrust force and cutting force are considered as the output parameters. The main aim of this experimentation process is to identify the optimal process parameters to get high M R R and low surface roughness. During high spindle speed, the M R R is high and vice versa. Surface roughness is high when its corresponding spindle speed and depth of cut is high. A high spindle speed, the chip formation is continuous whereas in medium speed, discontinuous chip is formed. M.R.R is high when spindle speed, depth of cut and feed rate are high.Keywords
Turning, Tungsten Carbide, Response Surface Methodology, ANOVA Method.Full Text
References
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- J. Ashvin and J.I. Navanathi. 2013. Optimization of machining parameters of turning operations based on response surface methodology, Int. J. Industrial Engg. Computations, 46, 521-1529.
- H. Singh, S. Singh, H. Singh and S.K. Sharma. 2014. Optimization of machining parameters of Turning of EN 16 Steel, Int. J. Current Engg. and Tech;4(6), 4130-4133.
- J. Varma, L. Bajpai and P. Agarwal. 2012. Turning parameter optimization for surface roughness of A242 Type-1 alloy steel by Taguchi method, Int. J. Advanced Engg. and Tech;3(1);255-261.
- J. Taneja, M. Bector and R. Kumar. 2012. Application to Taguchi method for optimizing turning process by the effects of machining parameters, Int. J. Engg. and Advanced Tech;2(1);263-275.
- D. Lazarevic, M. Madiac, P. Jankovic and D. Lazarevic. 2012. Cutting parameters optimization for surface roughness in turning operation of polyethylene (PE) using Taguchi method, Tribology in Industry, 34(2), 294-297.
- K.M. Lavanya, R.K. Suresh, A.S.K. Priya and G. Krishnaiah. 2013. Optimization of process parameters in turning operation of AISI-1016 alloy steels with CBN using artificial neural networks, Int. J. Engg. Trends and Tech., 5(6), 294-297.
- C.J. Rao, D.N. Rao and P. Srihari. 2013. Influence of cutting parameters on cutting force and surface finish in turning operation, Proc. Int. Conf. Design and Manuf., 65, 1405-1415. https://doi.org/10.1016/j.proeng.2013.09.222.
- R. Geo and J. SherilD’cotha. 2014. Effect of turning parameters on power consumption in EN 24 alloy steel using different cutting tools, Int. J. Engg Research and general service, 2(6), 691-702.
- P. Sahoo. 2011. Optimization of turning parameters for surface roughness using RSM and GA, Advances in Production Engg and Management, 3(2), 197-208.
- A.V.N.L. Sharma, K. Venkatasubbaiah and P.S.N. Raju. 2013. Parametric analysis and multi objective optimization of cutting parameters in turning operation of EN353 – with CVD cutting tool using Taguchi method, Int. J. Engg. and Innovative Tech., 2(9), 283-289.
- T. DinQuazi and P.G. Moore. 2012. Optimization of turning parameters such as speed rate, feed rate, depth of cut for surface roughness by Taguchi method, Asian J. Engg. and Tech. innovations, 2, 05-24.
- T. Sivaprakasam, S. Satarudin and S. Hasan. 2007. Analysis of surface roughness by turning process using Taguchi method, J. Achievements in Materials and Manuf. Engg., 20(12), 503-506.
- S. Dinesh, M. Prabhakaran, A.G. Antony, K. Rajaguru and V. Vijayan. 2017. Investigation and optimization of machining parameters in processing AISI 4340 alloy steel with electric discharge machining, Int. J. Pure and Applied Mathematics, 117(16), 385-391.
- S. Dinesh and A.G. Antony, K. Rajaguru and V. Vijayan. 2017. Experimental investigation and optimization of material removal rate and surface roughness in centerless grinding of magnesium alloy using grey relational analysis, Mechanics and Mech. Engg., 21(1), 17-28.
- B.R. Krishnan, V. Vijayan and G. Senthilkumar. 2018. Performance analysis of surface roughness modelling using soft computing approaches, Appl. Math. Inf. Sci. 12(6), 1209-1217. http://doi10.18576/amis/120616.
- AFSORP: Adaptive Fish Swarm Optimization-Based Routing Protocol for Mobility Enabled Wireless Sensor Network
Abstract Views :175 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Engineering, Annamalai University, Cuddalore, Tamil Nadu, IN
2 Department of Computer Science, Dr. N.G.P. Arts and Science College, Coimbatore, Tamil Nadu, IN
3 Department of Computer Science and Applications, Sankara College of Science and Commerce, Coimbatore, Tamil Nadu, IN
4 Department of Computer and Information Science, Annamalai University, Cuddalore, Tamil Nadu, IN
1 Department of Computer Science and Engineering, Annamalai University, Cuddalore, Tamil Nadu, IN
2 Department of Computer Science, Dr. N.G.P. Arts and Science College, Coimbatore, Tamil Nadu, IN
3 Department of Computer Science and Applications, Sankara College of Science and Commerce, Coimbatore, Tamil Nadu, IN
4 Department of Computer and Information Science, Annamalai University, Cuddalore, Tamil Nadu, IN
Source
International Journal of Computer Networks and Applications, Vol 10, No 1 (2023), Pagination: 119-129Abstract
Advances in information and communication technology and electronics have led to a surge in interest in mobility-enabled wireless sensor networks (MEWSN). These minuscule sensor nodes collect data, process it, and then transmit it via a radio frequency channel to a central station or sink. Most of the time, MEWSNs are placed in hazardous or difficult-to-access locations. To increase the lifespan of a network, available resources must be utilized as efficiently as possible. The whole network connection collapses if even one node loses power, rendering the deployment's goals moot. Therefore, much MEWSN research has focused on energy efficiency, with energy-efficient routing protocols being a key component. This paper proposes an Adaptive Fish Swarm Optimization-based Routing Protocol (AFSORP) for identifying the best route in MEWSN. AFSORP functions based on the natural characteristics of fish. The two most important steps in AFSORP are chasing and blocking, which respectively seek the optimal route and choose the appropriate route to send data from the source node to the destination node. Standard network performance measurements are used to assess AFSORP with the help of the GNS3 simulator. The results show that AFSORP performs better than the existing routing methods.Keywords
Routing, Mobility, WSN, MEWSN, Optimization, Fish, Energy.Full Text
References
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- J. Ramkumar and R. Vadivel, “Performance Modeling of Bio-Inspired Routing Protocols in Cognitive Radio Ad Hoc Network to Reduce End-to-End Delay,” Int. J. Intell. Eng. Syst., vol. 12, no. 1, pp. 221–231, 2019, doi: 10.22266/ijies2019.0228.22.
- J. Ramkumar and R. Vadivel, “Multi-Adaptive Routing Protocol for Internet of Things based Ad-hoc Networks,” Wirel. Pers. Commun., vol. 120, no. 2, pp. 887–909, Apr. 2021, doi: 10.1007/s11277-021-08495-z.
- R. Jaganathan and R. Vadivel, “Intelligent Fish Swarm Inspired Protocol (IFSIP) for Dynamic Ideal Routing in Cognitive Radio Ad-Hoc Networks,” Int. J. Comput. Digit. Syst., vol. 10, no. 1, pp. 1063–1074, 2021, doi: 10.12785/ijcds/100196.
- A. Behura and M. R. Kabat, “Chapter 13 - Optimization-based energy-efficient routing scheme for wireless body area network,” in Cognitive Data Science in Sustainable Computing, S. Mishra, H. K. Tripathy, P. K. Mallick, A. K. Sangaiah, and G.-S. B. T.-C. B. D. I. with a M. A. Chae, Eds. Academic Press, 2022, pp. 279–303. doi: https://doi.org/10.1016/B978-0-323-85117-6.00016-9.
- M. F. Carsancakli, M. A. Al Imran, H. U. Yildiz, A. Kara, and B. Tavli, “Reliability of linear WSNs: A complementary overview and analysis of impact of cascaded failures on network lifetime,” Ad Hoc Networks, vol. 131, p. 102839, 2022, doi: https://doi.org/10.1016/j.adhoc.2022.102839.
- V. Kavitha and K. Ganapathy, “Galactic swarm optimized convolute network and cluster head elected energy-efficient routing protocol in WSN,” Sustain. Energy Technol. Assessments, vol. 52, p. 102154, 2022, doi: https://doi.org/10.1016/j.seta.2022.102154.
- A. Sundar Raj and M. Chinnadurai, “Energy efficient routing algorithm in wireless body area networks for smart wearable patches,” Comput. Commun., vol. 153, pp. 85–94, 2020, doi: https://doi.org/10.1016/j.comcom.2020.01.069.
- A. S. Toor and A. K. Jain, “Energy Aware Cluster Based Multi-hop Energy Efficient Routing Protocol using Multiple Mobile Nodes (MEACBM) in Wireless Sensor Networks,” AEU - Int. J. Electron. Commun., vol. 102, pp. 41–53, 2019, doi: https://doi.org/10.1016/j.aeue.2019.02.006.
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- X. Fu, H. Yao, and Y. Yang, “Exploring the invulnerability of wireless sensor networks against cascading failures,” Inf. Sci. (Ny)., vol. 491, pp. 289–305, 2019, doi: https://doi.org/10.1016/j.ins.2019.04.004.
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- A. Bereketli, M. Tümçakır, and B. Yeni, “P-AUV: Position aware routing and medium access for ad hoc AUV networks,” J. Netw. Comput. Appl., vol. 125, pp. 146–154, Jan. 2019, doi: 10.1016/J.JNCA.2018.10.014.
- D. Adhikari, D. Datta, and R. Datta, “Impact of BER in fragmentation-aware routing and spectrum assignment in elastic optical networks,” Comput. Networks, vol. 172, p. 107167, May 2020, doi: 10.1016/J.COMNET.2020.107167.
- J. Liu et al., “QMR:Q-learning based Multi-objective optimization Routing protocol for Flying Ad Hoc Networks,” Comput. Commun., vol. 150, pp. 304–316, 2020, doi: https://doi.org/10.1016/j.comcom.2019.11.011.
- H. Zemrane, Y. Baddi, and A. Hasbi, “Mobile AdHoc networks for Intelligent Transportation System: Comparative Analysis of the Routing protocols,” Procedia Comput. Sci., vol. 160, pp. 758–765, 2019, doi: https://doi.org/10.1016/j.procs.2019.11.014.
- J. Wang, H. Zhang, X. Tang, and Z. Li, “Delay-tolerant routing and message scheduling for CR-VANETs,” Futur. Gener. Comput. Syst., vol. 110, pp. 291–309, 2020, doi: https://doi.org/10.1016/j.future.2020.04.026.
- P. Chithaluru, R. Tiwari, and K. Kumar, “AREOR–Adaptive ranking based energy efficient opportunistic routing scheme in Wireless Sensor Network,” Comput. Networks, vol. 162, p. 106863, 2019, doi: https://doi.org/10.1016/j.comnet.2019.106863.
- Lingaraj M and Prakash A, “Power Aware Routing Protocol (PARP) to Reduce Energy Consumption in Wireless Sensor Networks,” Int. J. Recent Technol. Eng., vol. 7, no. 5, pp. 380–385, Jan. 2019, Accessed: Apr. 07, 2021. [Online]. Available: https://www.ijrte.org/wp-content/uploads/papers/v7i5/E1969017519.pdf
- F. Al-Salti, N. Alzeidi, K. Day, and A. Touzene, “An efficient and reliable grid-based routing protocol for UWSNs by exploiting minimum hop count,” Comput. Networks, vol. 162, p. 106869, Oct. 2019, doi: 10.1016/J.COMNET.2019.106869.
- K. Patil, M. Jafri, D. Fiems, and A. Marin, “Stochastic modeling of depth based routing in underwater sensor networks,” Ad Hoc Networks, vol. 89, pp. 132–141, 2019, doi: https://doi.org/10.1016/j.adhoc.2019.03.009.
- B. Chakraborty, S. Verma, and K. P. Singh, “Temporal Differential Privacy in Wireless Sensor Networks,” J. Netw. Comput. Appl., vol. 155, p. 102548, 2020, doi: https://doi.org/10.1016/j.jnca.2020.102548.
- Minimizing Energy Consumption in Vehicular Sensor Networks Using Relentless Particle Swarm Optimization Routing
Abstract Views :136 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science, Skyline University, NG
2 Department of Computer Science, Dr. N.G.P. Arts and Science College, Tamil Nadu, IN
3 Department of Computer Science and Applications, Sankara College of Science and Commerce, Tamil Nadu, IN
4 Department of Computer Science and Engineering, Annamalai University, Tamil Nadu, IN
5 Department of Computer and Information Science, Annamalai University, Tamil Nadu, IN
1 Department of Computer Science, Skyline University, NG
2 Department of Computer Science, Dr. N.G.P. Arts and Science College, Tamil Nadu, IN
3 Department of Computer Science and Applications, Sankara College of Science and Commerce, Tamil Nadu, IN
4 Department of Computer Science and Engineering, Annamalai University, Tamil Nadu, IN
5 Department of Computer and Information Science, Annamalai University, Tamil Nadu, IN
Source
International Journal of Computer Networks and Applications, Vol 10, No 2 (2023), Pagination: 217-230Abstract
Increasing traffic issues, particularly in highly populated nations, have prompted recent interest in Vehicular Sensor Networks (VSNETs) from academics in several fields. Accident rates continue to rise, highlighting the need for a highly functional Smart Transport System (STS). Improvements to the STS should not be spread thin across the board but should concentrate on improving traffic flow, maintaining system reliability, and decreasing vehicle carbon dioxide and methane emissions. Current routing protocols for VSNETs consider various scenarios and approaches to provide safe and effective vehicle-to-infrastructure communication. The reliability of vehicle connections during data transmission has not been well explored. This paper proposes a Relentless Particle Swarm Optimization based Routing Protocol (RPSORP) for VSNET to use vehicle kinematics and mobility to identify vehicle location, send routing information packets to road-side devices, and choose the most reliable path for travel. RPSORP optimizes local and global search to minimize energy consumption in VSNET. The RPSORP is evaluated in the GNS3 simulator using Throughput, Packet Delivery, Delay, and Energy Consumption metrics. RPSORP has superior performance than state-of-the-art routing protocols.Keywords
VSNET, Routing, Swarming, PSO, Local-Search, Global-Search.Full Text
References
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