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Multi-Objective Optimizations for Microinjection Molding Process Parameters of Biodegradable Polymer Stent

机译:可生物降解聚合物支架微注射成型工艺参数的多目标优化

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Microinjection molding technology for degradable polymer stents has good development potential. However, there is a very complicated relationship between molding quality and process parameters of microinjection, and it is hard to determine the best combination of process parameters to optimize the molding quality of polymer stent. In this study, an adaptive optimization method based on the kriging surrogate model is proposed to reduce the residual stress and warpage of stent during its injection molding. Integrating design of experiment (DOE) methods with the kriging surrogate model can approximate the functional relationship between design goals and design variables, replacing the expensive reanalysis of the stent residual stress and warpage during the optimization process. In this proposed optimization algorithm, expected improvement (EI) is used to balance local and global search. The finite element method (FEM) is used to simulate the micro-injection molding process of polymer stent. As an example, a typical polymer vascular stent ART18Z was studied, where four key process parameters are selected to be the design variables. Numerical results demonstrate that the proposed adaptive optimization method can effectively decrease the residual stress and warpage during the stent injection molding process.
机译:用于可降解聚合物支架的微注射成型技术具有良好的发展潜力。然而,成型质量与显微注射工艺参数之间存在非常复杂的关系,并且难以确定工艺参数的最佳组合以优化聚合物支架的成型质量。本文提出了一种基于克里格代理模型的自适应优化方法,以减少支架在注模过程中的残余应力和翘曲。将实验设计(DOE)方法与kriging替代模型集成在一起,可以近似设计目标和设计变量之间的功能关系,从而取代了优化过程中昂贵的支架残余应力和翘曲的重新分析。在此提出的优化算法中,预期改进(EI)用于平衡本地搜索和全局搜索。有限元方法(FEM)用于模拟聚合物支架的微注射成型过程。例如,研究了典型的聚合物血管支架ART18Z,其中选择了四个关键过程参数作为设计变量。数值结果表明,所提出的自适应优化方法可以有效降低支架注射成型过程中的残余应力和翘曲。

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