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Multiobjective optimization of multipass turning machining process using the Genetic Algorithms solution

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The study involves the development of multi-objective optimization model for turning machining process. This model was developed using a GA - based weighted-sum of minimum production cost and time criteria of multipass turning machining process subject to relevant technological/practical constraints. The results of the single-objective machining process optimization models for the multipass turning machining process when compared with those of multi-objective machining process model yielded the minimum production cost and minimum production time as $5.775 and 8.320 min respectively (and the corresponding production time and production cost as 12.996 min and $6.992, respectively), while those of the multi-objective machining process optimization model were $5.841and 9.097 min. Thus, the multi-objective machining process optimization model performed better than each of the single-objective model for the two criteria of minimum production cost and minimum production time respectively. The results also show that minimum production time model performs better than the minimum production cost model. For the example considered, the multi-objective model gave a lower production time of 30.0% than the corresponding production time obtained from the minimum production cost model, while it gave a lower production cost of 16.46% than the corresponding cost obtained by the minimum production time model.
Rocznik
Strony
97--108
Opis fizyczny
Bibliogr. 19 poz., tab., wykr.
Twórcy
  • Department of Production, Faculty of Engineering, University of Benin, P.M.B 1154, Benin City, Nigeria
  • Department of Production, Faculty of Engineering, University of Benin, P.M.B 1154, Benin City, Nigeria
Bibliografia
  • 1. Ahmad, R. S., 2004. Determination of Optimal Machining Parameters Using Goal Programming Technique. Conference proceedings, Society of Manufacturing Engineers, IMTS 2004. 1-6.
  • 2. Cus, F., Zuperl, U. and Gecevsku, V., 2007. High speed end-milling optimization using Particle Swamp Intelligence. Journal of Achievements in Materials and Manufacturing Engineering. 22(2): 75-78.
  • 3. Borissova, D. and Mustakerov I., 2008. Multi-criteria Choice of Night Vision Devices Considering the Impact of Their Performance Parameters. AMO – Advanced Modeling and Optimization. 10(1): 81-93.
  • 4. Marler, R. T. and Arora J. S., 2004. Survey of multi-objective optimization methods for engineering. Structural Multidisciplinary Optimization. 126(6): 369-395.
  • 5. Lee, B. Y. and Tang, Y. S., 2000. Cutting parameter selection for Maximizing production rate or minimizing production cost multi-pass turning operations. Journal of Materials processing Technology. 105(1-2): 61-66.
  • 6. Cus, F. and Balic, J., 2003. Optimization of cutting process by GA approach, Robotics and Computer Integrated Manufacturing. 19: 113-121.
  • 7. Van Valdhuizen, D.A., Lamont, G.B., 2000. Multi-objective Evolutionary Algorithms. Analyzing the State-of-the-Art. Evolutionary Computation. 8(2), 125-147.
  • 8. Quiza-Sardinas, R., Santana, M. R. and Brindis, E. A., 2006. Genetic Algorithms-Based Multi-objective Optimization of cutting Parameters in Turning Processes. Engineering Applications of Artificial Intelligence. 19: 127-133.
  • 9. Bouzakis, K. D., Paraskevopoulou, R. and Giannopoulos, G., 2008. Multi-objective optimization of cutting conditions in milling using genetic algorithms. Proceedings of the 3rd International Conference on Manufacturing Engineering (ICMEN). 763-773.
  • 10. Kim, I. Y. and DeWeck, O. L., 2006. Adaptive weighted sum method for multi-objective optimization: a new method for Pareto front generation. Structural Multidiscipline Optimization. 31: 105–116.
  • 11. Shimizu, Y., 1999. Multi-objective optimization for site location problems through hybrid genetic algorithms with neural networks. Journal of Chemical Engineering of Japan. 32(1): 51-58.
  • 12. Deb, K., 1999. Multi-objective Genetic Algorithms: Problem, Difficulties and Construction of Test Problems. Evolutionary Computation. 7(3): 205-230.
  • 13. Sankararao, B. and Gupta, S. K., 2007. Multi-objective optimization of an industrial fluidized-bed catalytic cracking unit (FCCU) using two jumping gene adaptations of simulated annealing. Computers and Chemical Engineering. 31: 1496-1515.
  • 14. Goldberg, D. E., 1989. Genetic Algorithms in Search, Optimization and Machine Learning, Reading, MA: Addison-Wesley.
  • 15. Amiolemhen, P. E. and Ibhadode, A.O.A., 2004. Application of Genetic Algorithms - Determination of the Optimal Machining Parameters in the Conversion of a Cylindrical bar stock into a continuous finished profile. International Journal of Machine tools and Manufacture. 140 (1-3): 340-345.
  • 16. Onwubolu, G. C. and Kumalo, T., 2002. Multi-pass turning optimization based on Genetic Algorithms. International Journal of Production Research. 39(16): 3727-3745.
  • 17. Amiolemhen P.E., Eseigbe J.A. 2019. Genetic algorithms solution to the single-objective machining process optimization time model. Journal of Mechanical and Energy Engineering, Vol. 3(43), No. 1, pp. 13-24.
  • 18. Amiolemhen, P.E. and Ibhadode, A. O. A. (2019). Development of Genetic Algorithms based weighted method for multi-objective optimization. Journal of the Nigerian Association of Mathematical Physics. Vol. 35, pp. 149-154.
  • 19. Chen, M. C. and Tseng H. Y., 1998. Machining parameters selection for stock removal turning in process planning using a float encoding genetic algorithm. Journal of the Chinese Institute of Engineers. 16(4): 493-506.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7a3461bc-3cf6-4bb3-8b6b-7d6776a2190b
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