Multiobjective optimization using dynamic neighborhood particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
AI for Modelling the Laser Milling of Copper Components
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
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This paper focuses on understanding the influence of laser milling process parameters on the final geometrical and surface quality of micro-channel features fabricated on AISI H13 steel. Optimal selection of process parameters is highly critical for successful material removal and high dimensional and surface quality for micro-sized die/mold applications. A set of designed experiments is carried out in a pulsed Nd:YAG laser milling system using AISI H13 hardened tool steel as work material. Arrays of micro-channels have been fabricated using a range of process parameters such as scanning speed (SS), pulse intensity (PI), and pulse frequency (PF). The relation between process parameters and quality characteristics has been studied with experimental modeling. Multi-criteria decision making for material and process parameter selection for desired surface quality and dimensional accuracy is investigated using an evolutionary computation method based on particle swarm optimization (PSO).