The Intelligent Optimization And Control Of Co-injection Molding

Polymer process belongs to the three-dimensional, transient, non-isothermal multi-phase multi-layer flow-forming process. Because of the coupling interaction of stresses on the interfaces between adjacent melts in multiphase-multilayer flow molding, the quality of a co-injection molding products is a multi-variable, highly nonlinear complex function which coupled by process parameters, polymer melt rheological parameters and mold configuration. Up to the present, the simulation software about polymer molding process has no function to optimize the process of co-injection molding and the reports about the theory of intelligent optimization of co-injection molding process are also extremely rare at home and abroad, therefore, the theoretical study of intelligent optimization of co-injection molding process has important theoretical research value and engineering application value. Based on the theory of rheology, quasi three-dimensional, non-isothermal theoretical model of polymer co-injection molding and the corresponding stable numerical algorithm with fast convergence was established. By means of artificial intelligence technology, the simulated annealing and multi-island genetic algorithms based intelligence optimization algorithm and technology on the quality and energy-saving were put forward for a co-injection molding process. Through numerical simulation and process optimization, the optimization process conditions of the quality and energy-saving in a co-injection molding process were studied, the following main achievements are yielded:(1) Based on technique characteristics of polymer co-injection molding process and theories of polymer rheology, hydrokinetics and thermodynamics etc., the quasi three-dimensional, non-isothermal theoretical model describing co-injection molding process and the corresponding stable numerical algorithm with fast convergence of polymer were established by means of reasonable assumption.(2) The research results show the optimized objective function of co-injection molding process is a multi-variable, highly nonlinear complex function and also the optimization process belongs to multi-objective optimization process, therefore, the study on intelligent optimization theory and technology of co-injection molding process is the only correct way to achieve multi-objective and multi-parameter highly nonlinear process optimization in co-injection molding process.(3) The simulated annealing and multi-island genetic algorithms based intelligence optimization algorithm and technology on the quality and energy-saving were put forward for a co-injection molding process, and research results show the multi-island genetic algorithms is the ideal intelligence optimization and control technology by which product dimensional precision,product quality and energy-saving of a co-injection molding process are optimized.(4) The research shows that simulated annealing and multi-island genetic algorithms search variable in the overall scope, this has a good effect on avoiding the convergence situation of partial solution. The result is a suitable solution can be found in overall scope. And comparing with the Taguchi method, breakthrough phenomenon of core melt can be avoided in co-injection molding process because the constraints can be imposed.(5) Based on the design of Taguchi orthogonal experimentation in co-injection molding process and by means of S / N ratio analysis, the influence of the various parameters of the process conditions on the largest penetration depth of core melt of co-injection molding products was studied. Through the analysis of variance, the influence degree of the process parameters on the penetration depth of core melt was sorted as follows: shell melt temperature, melt filling rate, core melt temperature and packing pressure.