EDP Sciences logo
Submit your paper
Search
Menu
Home All issues Volume 10 (2019) Int. J. Simul. Multidisci. Des. Optim., 10 (2019) A8 References
Open Access
Issue
Int. J. Simul. Multidisci. Des. Optim.
Volume 10, 2019
Article Number A8
Number of page(s) 11
DOI https://doi.org/10.1051/smdo/2019009
Published online 16 April 2019
  1. A. Osyczka, An approach to multicriterion optimization problems for engineering design. Comput. Methods Appl. Mech. Eng. 15 , 309–333 (1978) [CrossRef] [Google Scholar]
  2. B.S. Tong, D. Walton, The optimisation of internal gears. Int. J. Mach. Tools Manuf. 27 , 491–504 (1987) [CrossRef] [Google Scholar]
  3. S. Prayoonrat, D. Walton, Practical approach to optimum gear train design. Comput. Des. 20 , 83–92 (1988) [Google Scholar]
  4. K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6 , 182–197 (2002) [Google Scholar]
  5. R.V. Rao, Teaching learning based optimization algorithm. New York: Springer 2016 [CrossRef] [Google Scholar]
  6. T. Yokota, T. Taguchi, M. Gen, A solution method for optimal weight design problem of the gear using genetic algorithms. Comput. Ind. Eng. 35 , 523–526 (1998) [CrossRef] [Google Scholar]
  7. V. Savsani, R.V. Rao, D.P. Vakharia, Optimal weight design of a gear train using particle swarm optimization and simulated annealing algorithms. Mech. Mach. Theory 45 , 531–541 (2010) [CrossRef] [Google Scholar]
  8. S. Golabi, J.J. Fesharaki, M. Yazdipoor, Gear train optimization based on minimum volume/weight design. Mech. Mach. Theory 73 , 197–217 (2014) [CrossRef] [Google Scholar]
  9. D. Das, S. Bhattacharya, B. Sarkar, Decision-based design-driven material selection: a normative-prescriptive approach for simultaneous selection of material and geometric variables in gear design. J. Materials Design 92 , 787–793 (2016) [Google Scholar]
  10. N. Kostić, N. Marjanović, N. Petrović, A novel approach for solving gear train optimization. Int. J. Veh. Mech. Eng. Transp. Syst. 42 , 67–76 (2016) [Google Scholar]
  11. N. Marjanovic, B. Isailovic, V. Marjanovic, Z. Milojevic, M. Blagojevic, M. Bojic, A practical approach to the optimization of gear trains with spur gears, Mech. Mach. Theory. 53 , 1–16 (2012) [Google Scholar]
  12. A. Zolfaghari, M. Goharimanesh, A.A. Akbari, Optimum design of straight bevel gears pair using evolutionary algorithms, J. Brazilian Soc. Mech. Sci. Eng. 39 , 2121–2129 (2017) [Google Scholar]
  13. R. Sanghvi, A. Vashi, H.P. Patolia, R.G. Jivani, Multi-objective optimization of two-stage helical gear train using NSGA-II, J. Optim. 2014 , 670297 (2014) [Google Scholar]
  14. K. Tamboli, S. Patel, P.M. George, R. Sanghvi, Optimal design of a heavy duty helical gear pair using particle swarm optimization technique. Proc. Technol. 14 , 513–519 (2014) [CrossRef] [Google Scholar]
  15. D.F. Thompson, S. Gupta, A. Shukla, Tradeoff analysis in minimum volume design of multi-stage spur gear reduction units. Mech. Mach. Theory 35 , 609–627 (2000) [CrossRef] [Google Scholar]
  16. K. Deb, S. Jain, Multi-speed gearbox design using multi-objective evolutionary algorithms. J. Mech. Des. 125 , 609 (2003) [CrossRef] [Google Scholar]
  17. J. Stefanović-Marinović, S. Troha, M. Milovančević, An application of multicriteria optimization to the two-carrier two-speed planetary gear trains. Facta Univ. Ser. Mech. Eng. 15 , 85 (2017) [CrossRef] [Google Scholar]
  18. R. Arora, S.C. Kaushik, R. Kumar, R. Arora, Soft computing based multi-objective optimization of Brayton cycle power plant with isothermal heat addition using evolutionary algorithm and decision making. Appl. Soft Comput. J. 46 , 267–283 (2016) [CrossRef] [Google Scholar]
  19. R. Kumar, S.C. Kaushik, R. Kumar, R. Hans, Multi-objective thermodynamic optimization of an irreversible regenerative Brayton cycle using evolutionary algorithm and decision making. Ain Shams Eng. J. 7 , 741–753 (2016) [CrossRef] [Google Scholar]
  20. I. Akinci, D. Yilmaz, C. Murad, Failure of a rotary tiller spur gear. Eng. Falure Anal. 12 , 400–404 (2005) [CrossRef] [Google Scholar]
  21. A.L. Kapelevich, Direct gear design, 1st ed. New York: Taylor & Francis 2013 [CrossRef] [Google Scholar]
  22. G. Madhusudan, C.R. Vijayasimha, Approach to spur gear design. Comput. Des. 19 , 555–559 (1987) [Google Scholar]
  23. J. Arora, Introduction to optimum design, 3rd ed. Cambridge: Academic Press 2011 [Google Scholar]
  24. R.A. El-Sehiemy, A.A. Abou, E.L. Ela, A. Shaheen, A Multi-objective fuzzy-based procedure for reactive power-based preventive emergency strategy. Int. J. Eng. Res. Afr. 13 , 91–102 (2015) [CrossRef] [Google Scholar]
  25. A. Khan, K. Maity, Parametric optimization of some non-conventional machining processes using MOORA method. Int. J. Eng. Res. Afr. 20 , 19–40 (2016) [CrossRef] [Google Scholar]
  26. O.M. Koura, A.S. El-Akkad, Optimization of cutting conditions using regression and genetic algorithm in end milling. Int. J. Eng. Res. Afr. 20 , 12–18 (2016) [CrossRef] [Google Scholar]
  27. R. Arora, S.C. Kaushik, R. Arora, Thermodynamic modeling and multi-objective optimization of two stage thermoelectric generator in electrically series and parallel configuration. Appl. Therm. Eng. 103 , 1312–1323 (2016) [CrossRef] [Google Scholar]
  28. R. Arora, S.C. Kaushik, R. Kumar, R. Arora, Multi-objective thermo-economic optimization of solar parabolic dish Stirling heat engine with regenerative losses using NSGA-II and decision making. Int. J. Electr. Power Energy Syst. 74 , 25–35 (2016) [CrossRef] [Google Scholar]
  29. R. Arora, S.C. Kaushik, R. Kumar, Multi-objective thermodynamic optimization of solar parabolic dish Stirling heat engine with regenerative losses using NSGA-II and decision making. Appl. Sol. Energy 52 , 295–304 (2016) [Google Scholar]
  30. R. Arora, S.C. Kaushik, R. Kumar, Multi-objective thermodynamic optimisation of solar parabolic dish Stirling heat engine using NSGA-II and decision making. Int. J. Renew Energy Technol. 8 , 64–92 (2017) [Google Scholar]
  31. R. Arora, R. Arora, Multiobjective optimization and analytical comparison of single‐ and 2‐stage (series/parallel) thermoelectric heat pumps, Int. J. Energy Res. 42 , 1760–1778 (2018) [CrossRef] [Google Scholar]
  32. R. Arora, R. Arora, Multicriteria optimization based comprehensive comparative analyses of single-and two-stage (series/parallel) thermoelectric generators including the influence of Thomson effect. J. Renew Sustain. Energy 10 , 044701 (2018) [Google Scholar]
  33. R. Arora, S.C. Kaushik, R. Arora, Multi-objective and multi-parameter optimization of two-stage thermoelectric generator in electrically series and parallel configurations through NSGA-II. Energy 91 , 242–254 (2015) [CrossRef] [Google Scholar]
  34. R. Arora, S.C. Kaushik, R. Kumar, Multi-objective optimization of an irreversible regenerative Brayton cycle using genetic algorithm, in 2015 International Conference on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE). IEEE, pp. 340–346 (2015) [Google Scholar]
  35. R. Arora, S.C. Kaushik, R. Kumar, Multi-objective optimization of solar powered Ericsson cycle using genetic algorithm and fuzzy decision making, in 2015 International Conference on Advances in Computer Engineering and Applications (ICACEA). IEEE, pp. 553–558 (2015) [Google Scholar]
  36. A. Messac, Optimization in practice with MATLAB: for engineering students and professionals. Cambridge : Cambridge University Press (2015) [CrossRef] [Google Scholar]
  37. S.S. Rao, Engineering optimization: theory and practice, 4th ed. New York: John Wiley & Sons, Inc 2009 [CrossRef] [Google Scholar]
  38. R. Venkata Rao, Review of applications of TLBO algorithm and a tutorial for beginners to solve the unconstrained and constrained optimization problems. Decis. Sci. Lett. 5, 1–30 (2016) [Google Scholar]
  39. Available at http://gearsolutions.com/departments/materialsmatter-common-gear-failures/ [Google Scholar]
  40. C. Wang, S. Wang, G. Wang, Volume models for different structures of spur gear. Aust. J. Mech. Eng. 1–9 (2017). DOI: 10.1080/14484846.2017.1381373 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.