基于多场耦合方法的厚截面复合材料固化过程的多目标优化

Multi-objective optimization for curing process of thick composite based on multi-physics coupling method

针对厚截面复合材料固化过程温度峰值过大所引起的材料力学性能降低及残余应力过大等问题,建立了基于多场耦合方法的复合材料固化过程多目标优化模型,用以降低固化温度峰值和缩短固化时间。首先建立包含热化学子模型、树脂黏度子模型和流动压实子模型的固化温度多场耦合模型,用以准确描述固化过程复合材料内部温度及构件厚度的演化规律。通过与文献中已有实验结果比较,证明所建立的多场耦合模型的有效性。在该多场耦合模型基础上,引入径向基(RBF)神经网络作为代理模型,利用多目标优化方法,对固化工艺参数进行最佳组合匹配。研究表明,温度峰值与保温平台温度变化呈明显非线性关联,这与复合材料固化过程的非线性特性有很大关系。在保温温度层面,为了降低温度峰值,需要提高第一阶段的保温温度,降低第二阶段的保温温度,同时在保温平台的时间上进行调整,以缩短固化时长。相比较于原有固化工艺制度,本文提出的优化方法可以显著降低厚截面复合材料层合板的固化时长和温度峰值。

In order to reduce the mechanical properties and the curing residual stress of the thick composite caused by the excessive temperature peak during the curing process, a multi-objective optimization model based on the multi-physics coupling characteristics was developed to reduce the maximum curing temperature peak and the curing time. Firstly, a three-dimensional model which incorporated three typical sub-models including thermo-chemical model, resin viscosity model and resin flow model was established to investigate the development of temperature and thickness of laminate during curing process. The results of numerical model were compared with experiment data in reference and good accordance was obtained. Then, a multi-objective optimization method was applied to optimize curing process parameters by using a radial basis function neural network model(RBF) as the surrogate model. It is shown that the curing temperature peak has a nonlinear relationship with the first and second dwell temperature, which is related to the nonlinear characteristics of the curing process. In order to reduce the temperature peak, it is necessary to increase the first dwell temperature and reduce the second dwell temperature.Meanwhile, the dwell time should also be adjusted to shorten the total curing time. Compared to standard cure profiles, the proposed optimization method can significantly reduce the curing time and temperature peak for thick composite laminates.