Computer graphics (2nd ed. in C): principles and practice
Computer graphics (2nd ed. in C): principles and practice
A geometric algorithm for selecting optimal set of cutters for multi-part milling
Proceedings of the sixth ACM symposium on Solid modeling and applications
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Optimizing of NC tool paths for five-axis milling using evolutionary algorithms on wavelets
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Design and Analysis of Experiments
Design and Analysis of Experiments
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
An EMO algorithm using the hypervolume measure as selection criterion
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Intelligent process planning methods for the manufacturing of moulds
International Journal of Computer Applications in Technology
Generation of reciprocating tool motion in 5-axis flank milling based on particle swarm optimization
Journal of Intelligent Manufacturing
A co-evolutionary multi-objective optimization algorithm based on direction vectors
Information Sciences: an International Journal
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Computer-Numerical-Control based five-axis milling offers new possibilities for improving the machining process. However, this procedure is still difficult to handle, particularly in case of machining complex free-formed surfaces. An optimization approach based on the multi-objective evolutionary algorithm SMS-EMOA (S-metric selection evolutionary multi-objective optimization algorithm) combined with a multi-population approach has been developed and used in order to utilize the potential of the five-axis milling process. After a general introduction to this machining process and the potential of path optimization, the designed multi-population multi-objective evolutionary approach, its integration into the simulation, and its adaptation to the practical example is described.