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|Title:||Multi-objective optimization of high-speed milling with parallel genetic simulated annealing||Authors:||Wang, Z.G.
|Issue Date:||Nov-2006||Citation:||Wang, Z.G., Wong, Y.S., Rahman, M., Sun, J. (2006-11). Multi-objective optimization of high-speed milling with parallel genetic simulated annealing. International Journal of Advanced Manufacturing Technology 31 (3-4) : 209-218. ScholarBank@NUS Repository. https://doi.org/10.1007/s00170-005-0191-x||Abstract:||In this paper, the optimization of multi-pass milling has been investigated in terms of two objectives: machining time and production cost. An advanced search algorithm-parallel genetic simulated annealing (PGSA)-was used to obtain the optimal cutting parameters. In the implementation of PGSA, the fitness assignment is based on the concept of a non-dominated sorting genetic algorithm (NSGA). An application example is given using PGSA, which has been used to find the optimal solutions under four different axial depths of cut on a 37 SUN workstation network simultaneously. In a single run, PGSA can find a Pareto-optimal front which is composed of many Pareto-optimal solutions. A weighted average strategy is then used to find the optimal cutting parameters along the Pareto-optimal front. Finally, based on the concept of dynamic programming, the optimal cutting strategy has been obtained. © Springer-Verlag London Limited 2006.||Source Title:||International Journal of Advanced Manufacturing Technology||URI:||http://scholarbank.nus.edu.sg/handle/10635/60844||ISSN:||02683768||DOI:||10.1007/s00170-005-0191-x|
|Appears in Collections:||Staff Publications|
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