Please use this identifier to cite or link to this item:
|Title:||Multi-objective optimization of high-speed milling with parallel genetic simulated annealing|
|Authors:||Wang, Z.G. |
|Source:||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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Mar 7, 2018
WEB OF SCIENCETM
checked on Jan 24, 2018
checked on Mar 11, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.