Operation Sequencing Using Genetic Algorithm
| Periodical | Applied Mechanics and Materials (Volume 163) |
|---|---|
| Main Theme | History of Mechanical Technology and Mechanical Design 2012 |
| Edited by | Hong-Sen Yan, Jianbin Zhang, Guanglin Wang, Kuei-Yuan Chan, Yidu Zhang, Chunjie Wang and Hai Zhang |
| Pages | 57-61 |
| DOI | 10.4028/www.scientific.net/AMM.163.57 |
| Citation | Lian Liu et al., 2012, Applied Mechanics and Materials, 163, 57 |
| Online since | April, 2012 |
| Authors | Lian Liu, Li Hong Qiao |
| Keywords | Constraint Matrix, Genetic Algorithm (GA), Manufacturing Feature, Operation Sequencing |
| Price | US$ 28,- |
Operation sequencing is one of the most important tasks in process planning. The sequencing procedures associate manufacturing features from 3D CAD models and machining methods together to satisfy certain manufacturing process constraints. In order to simplify process constraint aggregations, two types of constraint matrixes, feature constraint matrix and the operation constraint matrix, are proposed in this paper, which take into account of the compulsive constraints, such as geometric topology constraints, manufacturing process knowledge criteria, custom compulsive constraints and so forth. Accordingly, an iterative genetic algorithm is proposed, which is naturally used in the manufacturing feature level and operation level. In the manufacturing feature level, feasible feature sequences are generated based on the analysis of feature constraint matrix. In the operation level, the information that is contained in the machining operation such as machine tools, set-ups and cutting tools is considered to optimize the operation sequences based on the results acquired in the feature level. Compared with the traditional simple genetic algorithm, the iterative genetic algorithm is proved to be superior in shortening the operation sequencing time.
