EDP Sciences logo
Submit your paper
Search
Menu
Home All issues Volume 13 (2022) Int. J. Simul. Multidisci. Des. Optim., 13 (2022) 6 References
Issue
Int. J. Simul. Multidisci. Des. Optim.
Volume 13, 2022
Advances in Modeling and Optimization of Manufacturing Processes
Article Number 6
Number of page(s) 8
DOI https://doi.org/10.1051/smdo/2021033
Published online 06 January 2022
  1. R.G. Gould, The LASER, light amplification by stimulated emission of radiation, in the Ann Arbor Conference on Optical Pumping, the University of Michigan 15, 92 (1959 [Google Scholar]
  2. N. Taylor, LASER: The inventor, the Nobel laureate, and the thirty-year patent war (Simon and Schuster, 2002) [Google Scholar]
  3. E.C. Jameson, Electrical Discharge Machining (Society of Manufacturing Engineers, 2001) [Google Scholar]
  4. K.P. Rajurkar, D. Zhu, J.A. McGeough, J. Kozak, A. De Silva, New developments in electro-chemical machining, CIRP Ann. 48, 567–579 (1999) [CrossRef] [Google Scholar]
  5. M. Martins, R. Godina, C. Pimentel, F.J.G. Silva, J.C. Matias, A practical study of the application of SMED to electron-beam machining in automotive industry, Proc. Manufactur. 17, 647–654 (2018) [CrossRef] [Google Scholar]
  6. P.L. Guzzo, A.H. Shinohara, A.A. Raslan, A comparative study on ultrasonic machining of hard and brittle materials, J. Br. Soc. Mech. Sci. Eng. 26, 56–61 (2004) [Google Scholar]
  7. L. Alting, Manufacturing Engineering Processes (Marcel Dekker Inc., New York, 1982) [Google Scholar]
  8. Z. Jurkovic, M. Perinic, S. Maricic, M. Sekulic, V. Mandic, Application of modelling and optimization methods in abrasive water jet machining, J. Trends Dev. Mach. Assoc. Technol. 16, 59–62 (2012) [Google Scholar]
  9. N. Kumar, M. Shukla, Finite element analysis of multi-particle impact on erosion in abrasive water jet machining of titanium alloy, J. Comput. Appl. Math. 236, 4600–4610 (2012) [CrossRef] [Google Scholar]
  10. H. Li, J. Wang, An experimental study of abrasive waterjet machining of Ti-6Al-4V, Int. J. Adv. Manufactur. Technol. 81, 361–369 (2015) [CrossRef] [Google Scholar]
  11. R.V. Rao, P.J. Pawar, J.P. Davim, Parameter optimization of ultrasonic machining process using nontraditional optimization algorithms, Mater. Manufactur. Process. 25, 1120–1130 (2010) [CrossRef] [Google Scholar]
  12. D. Goswami, S. Chakraborty, Parametric optimization of ultrasonic machining process using gravitational search and fireworks algorithms, Ain Shams Eng. J. 6, 315–331 (2015) [CrossRef] [Google Scholar]
  13. R. Teimouri, H. Baseri, R. Moharami, Multi-responses optimization of ultrasonic machining process, J. Intell. Manufactur. 26, 745–753 (2015) [CrossRef] [Google Scholar]
  14. B. Acherjee, D. Maity, A.S. Kuar, Ultrasonic machining process optimization by cuckoo search and chicken swarm optimization algorithms, Int. J. Appl. Metaheur. Comput. 11, 1–26 (2020) [Google Scholar]
  15. D.H. Wolpert, K. Tumer, An Introduction to Collective Intelligence, Technical Report, NASA ARC-IC-99-63, NASA Ames Research Center, 1999 [Google Scholar]
  16. M.Y. Cheng, D. Prayogo, Symbiotic organisms search: a new metaheuristic optimization algorithm, Comput. Struct. 139, 98–112 (2014) [CrossRef] [Google Scholar]
  17. R.V. Rao, Advanced modeling and optimization of manufacturing processes (International Research and Development, Springer, 2011) [CrossRef] [Google Scholar]
  18. A.J. Kulkarni, I.P. Durugkar, M. Kumar, Cohort intelligence: a self-supervised learning behavior, in 2013 IEEE International Conference on Systems, Man, and Cybernetics (2013) pp. 1396–1400 [CrossRef] [Google Scholar]
  19. A.J. Kulkarni, H. Shabir, Solving 0–1 knapsack problem using cohort intelligence algorithm, Int. J. Mach. Learn. Cybern. 7, 427–441 (2016) [CrossRef] [Google Scholar]
  20. A.J. Kulkarni, M.F. Baki, B.A. Chaouch, Application of the cohort-intelligence optimization method to three selected combinatorial optimization problems, Eur. J. Oper. Res. 250, 427–447 (2016) [CrossRef] [Google Scholar]
  21. A.J. Kulkarni, G. Krishnasamy, A. Abraham, Cohort intelligence: a socio-inspired optimization method (Springer International Publishing, Switzerland, 2017), p. 114 [Google Scholar]
  22. M. Aladeemy, S. Tutun, M.T. Khasawneh, A new hybrid approach for feature selection and support vector machine model selection based on self-adaptive cohort intelligence, Exp. Syst. Appl. 88, 118–131 (2017) [CrossRef] [Google Scholar]
  23. O. Kulkarni, N. Kulkarni, A.J. Kulkarni, G. Kakandikar, Constrained cohort intelligence using static and dynamic penalty function approach for mechanical components design, Int. J. Parallel Emerg. Distrib. Syst. 33, 570–588 (2018) [CrossRef] [Google Scholar]
  24. A.S. Shastri, A. Nargundkar, A.J. Kulkarni, K.K. Sharma, Multi-Cohort intelligence algorithm for solving advanced manufacturing process problems, Neural Comput. Appl. 32, 15055–15075 (2020) [CrossRef] [Google Scholar]
  25. V. Gulia, A. Nargundkar, Optimization of process parameters of abrasive water jet machining using variations of cohort intelligence (CI), in Applications of Artificial Intelligence Techniques in Engineering (2019), pp. 467–474 [CrossRef] [Google Scholar]
  26. I.R. Kale, A.J. Kulkarni, Cohort intelligence algorithm for discrete and mixed variable engineering problems, Int. J. Parallel Emerg. Distrib. Syst. 33, 627–662 (2018) [CrossRef] [Google Scholar]
  27. I.R. Kale, A.J. Kulkarni, S.C. Satapathy, A socio-based cohort intelligence algorithm for engineering problems. Socio-cultural inspired metaheuristics, Stud. Comput. Intell. 828, 121–135 (2019) [CrossRef] [Google Scholar]
  28. I.R. Kale, A.J. Kulkarni, Cohort intelligence with self-adaptive penalty function approach hybridized with colliding bodies optimization algorithm for discrete and mixed variable constrained problems, Complex Intell. Syst. 7, 1565–1596 (2021) [CrossRef] [Google Scholar]
  29. D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning (Addison-Wesley, 1989) [Google Scholar]
  30. V.K. Jain, Advanced Machining Processes (Allied Publishers, 2009) [Google Scholar]
  31. R.V. Rao, Advanced Modeling and Optimization of Manufacturing Processes (International Research and Development, Springer, 2010) [Google Scholar]
  32. J. Wang, Abrasive Waterjet Machining of Engineering Materials (Trans Tech Publications Ltd, Switzerland, Zurich, 2003) [Google Scholar]
  33. M. Hashish, A modeling study of metal cutting with abrasive water jets, J. Eng. Mater. Technol. 106, 88–100 (1984) [CrossRef] [Google Scholar]
  34. N.K. Jain, V.K. Jain, K. Deb, Optimization of process parameters of mechanical type advanced machining processes using genetic algorithms, Int. J. Mach. Tools Manufact. 47, 900–919 (2007) [CrossRef] [Google Scholar]
  35. A.V. Mitrofanov, V.I. Babitsky, V.V. Silberschmidt, Finite element analysis of ultrasonically assisted turning of Inconel 718, J. Mater. Process. Technol. 153, 233–239 (2004) [CrossRef] [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.