The Assembly Sequence Planning Based On Subassembly Identification And Genetic Algorithms

Assembly Sequences Planning is essentially a problem of NP combination optimization, and the generation of feasible assembly sequences is the core issue in this problem. In this paper, the difficulty of Assembly Sequences Planning is studied by the method which is based on sub-assembly identification and genetic algorithms. In this paper, the primary job is as follows:Firstly, the established approach of assembly model is studied. First of all, the basic data of assembly model can be obtained from the CAD system, which should be processed according to the method of assembly sequence planning used in this paper, and then the assembly model is established by the pattern of automated distinguishment and human-computer interaction. Assembly model contains the information need by sub-assembly identification and genetic algorithms:connection information, global impeding information and support information. And the established methods of contact connection undirected graph, global impeding matrix and support matrix describing the product of the information are provided in detail. Also the decision methods of the geometry feasibility in the feasible assembly/disassembly sequence and the stability in the system when the sub-assemblies, components are in place are introduced. At last the decision methods are illustrated by an example.Secondly, according to the contact connection undirected graph in the assembly model, the recognition method of basic parts based on the connection relation and the foundation part feature is introduced; the recognition algorithm of typeâ… and typeâ…¡sub-assembly is proposed based on the contact connection undirected graph, global impeding and support matrix provided by an assembly model. The algorithm is inspected and verified by an example.Thirdly, the genetic algorithm is used for generation and evaluation of assembly sequence. By adopting the symbol encoding method and selecting the corresponding appropriate genetic operators, the fitness function is designed according to the general principles of the assembly. And then the assembly sequences of sub-assembly and overall assembly are respectively generated and evaluated.Finally, according to the analysis above, the effectiveness of the method in this paper has be verified, by the example of the assembly sequence planning of positive speed gearbox. The model and identifying method in this paper, verified by comparing to the methods of references, is more completely and accurately to identify the two kinds of sub-assemblies. And the genetic algorithm could obtain the optimization assembly sequence more effectively and rapidly.