Li Hongxia, Liu Kui, Zhao Danyang, Wang Minjie, Li Qian, Hou Jianhua
School of Mechanical Engineering, Dalian University of Technology, Dalian 116023, China.
National Center for International Joint Research of Micro-Nano Molding Technology, Zhengzhou University, Zhengzhou 450000, China.
Materials (Basel). 2018 Nov 19;11(11):2322. doi: 10.3390/ma11112322.
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.
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,微注射成型质量与工艺参数之间存在非常复杂的关系,难以确定最佳工艺参数组合以优化聚合物支架的成型质量。在本研究中,提出了一种基于克里金代理模型的自适应优化方法,以减少支架注射成型过程中的残余应力和翘曲。将实验设计(DOE)方法与克里金代理模型相结合,可以近似设计目标与设计变量之间的函数关系,在优化过程中取代对支架残余应力和翘曲的昂贵重新分析。在该优化算法中,使用预期改进(EI)来平衡局部搜索和全局搜索。采用有限元方法(FEM)模拟聚合物支架的微注射成型过程。作为示例,研究了一种典型的聚合物血管支架ART18Z,选择四个关键工艺参数作为设计变量。数值结果表明,所提出的自适应优化方法能够有效降低支架注射成型过程中的残余应力和翘曲。