Research On Semantic Feature Modeling And Constraint Solving

In recent years, the structure of CAD software is changing from solid modeling centered systems"geometric CAD"to the feature and constraint based ones—"applied CAD". As an substantial part of CAD systems, technology concerning constraint modeling becomes more important than before, lots of beneficial counterplans have been proposed, and those gordian technique encourage the reformation and development of CAD systems. Meanwhile, it makes feature a modified flexibility by the new concept feature strategy, then the designing process becomes more efficiency than before. Therefore, the semantic feature is turning into a major developing direction of the new CAID systems.According to semantic feature model operation method, this paper proposes a direct operation and maintenance mechanism in modelling process. It firstly divided direct operation into translation transformation, proportion transformation, and rotation transformation those three operation methods. Secondly, during the process of direct operation, if there is not some changes about the split topological entities, then the result model will easily displayed from direct restrain solving. But if there is some changes about the split topological entities, from setting dynamic feature, correct prior standard, then according to the feature correction prior standard to come out a accurate result model. Finally, the paper proposes a effective checking and maintain method in the process of model modification.This paper also proposes an improved chaos search stragety. The combination of chaos optimization method and genetic algorithms make the creation of chaos genetic algorithm. The features of this new method is that the mechanism of the genetic algorithms is not changed but the search space and the coefficient of the adjustment of the optimization parameter are reduced continually, which leads to generation evolution to the next generation in order to pro-duce better optimization individuals so as to improve the performance of the genetic algorithms. But by analyzing it essence, Many repetitions are found, without considering the certain resemblance between them in the optimization process. and some improvements are made there. genetic algorithm searches solutions rapidly in early evolution period, which is used to search primary solutions. When the algorithm gets into the local extremum or finds preferable solutions, the improved chaos search strategy is used to activate the particles and to search the global best solutions accurately.