Thermoset polymer composite structures are heavily used in the aerospace, defense, transport, and energy sectors due to their lightweight and high-performance behavior. Thermoset polymer resins require external heat for manufacturing/curing. The behavior of these polymer composite materials is highly dependent on curing process as it affects evolution of material properties as well as residual stresses and deformation. Various cure process parameters, mainly related to cure thermal cycle, need to be optimized to get the desired properties of these structures. In this paper, the polymer cure process is explicitly modeled through finite element method. Its effects at the structural level are captured by modeling thermo-chemical-mechanical analysis through multiple length scales. The multi-scale analysis is carried out by surrogate models to reduce run time. In this study, non-dominated sorting genetic algorithm II is used for multi-objective cure process optimization. The objectives are to minimize the spring-in angle and minimize the process time with achieving degree of cure above given requirement. Insights from such optimization can be utilized by product designers as well as manufacturers to take timely decisions to improve the performance of these composite structures.

热固性聚合物复合结构因其轻质和高性能而广泛应用于航空航天、国防、运输和能源领域。热固性聚合物树脂需要外部热量来制造/固化。这些聚合物复合材料的行为高度依赖于固化过程，因为它影响材料性能的演变以及残余应力和变形。需要优化各种固化工艺参数（主要与固化热循环相关）以获得这些结构所需的性能。在本文中，聚合物固化过程通过有限元方法明确建模。通过多个长度尺度的热化学机械分析模型来捕获其在结构层面的影响。通过代理模型进行多尺度分析以减少运行时间。在本研究中，非支配排序遗传算法II用于多目标固化过程优化。目标是最小化弹入角并最小化加工时间，同时实现高于给定要求的固化程度。产品设计师和制造商可以利用此类优化的见解来及时做出决策，以提高这些复合材料结构的性能。