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Erschienen in: The International Journal of Advanced Manufacturing Technology 11-12/2020

05.02.2020 | ORIGINAL ARTICLE

Application of intelligent water drop algorithm in process planning optimization

verfasst von: Bo Gao, Xiaoyu Hu, Zhenrui Peng, Yubo Song

Erschienen in: The International Journal of Advanced Manufacturing Technology | Ausgabe 11-12/2020

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Abstract

Computer-aided process planning is an important part of computer integrated manufacturing, which is associated with computer-aided manufacturing and computer-aided design. This paper presents a process planning method based on the Intelligent Water Drop (IWD) algorithm. First, the operation units were defined by analyzing the machining features and analyzing the constraint relationship according to the processing characteristics of the operation unit as a constraint for process optimization. Then, the IWD algorithm was combined with the process planning problem. According to the iterative characteristics of the algorithm, the nodes in the flow path of the water droplets were associated with the operation units, and the amount of soil in the water droplets was correlated with the production cost. The mathematical model of process planning was constructed on the premise of analyzing the optimization objectives and machining constraints. By searching the feasible solution space limited by the sequential constraint matrix, the process planning scheme with the least production cost was obtained. Finally, an experiment of two prismatic parts was used to illustrate the application process of the IWD algorithm in process planning, and the calculation results were compared with other methods. The comparison results showed that the proposed method is feasible and effective in process planning.

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Metadaten
Titel
Application of intelligent water drop algorithm in process planning optimization
verfasst von
Bo Gao
Xiaoyu Hu
Zhenrui Peng
Yubo Song
Publikationsdatum
05.02.2020
Verlag
Springer London
Erschienen in
The International Journal of Advanced Manufacturing Technology / Ausgabe 11-12/2020
Print ISSN: 0268-3768
Elektronische ISSN: 1433-3015
DOI
https://doi.org/10.1007/s00170-019-04850-4

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