The Application of Artificial Intelligence Methods in RTM Simulation
JIANG Jinchi1, LI Wenxiao1,*, FANG Keyan2
1 School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai 200082, China 2 East China Electric Power Design Institute Co., Ltd., of China Power Engineering Consulting Group, Shanghai 200063, China
摘要 树脂传递模塑成型(Resin transfer molding,RTM)工艺仿真对于提高成型质量,降低RTM工艺成本至关重要。将人工智能方法引入RTM工艺仿真中,可以不必求解复杂的多尺度渗流模型就能够获得对RTM模具设计的指导性意见。本文综述了以遗传算法和机器学习方法为主的人工智能方法在RTM工艺仿真中的研究现状,并讨论了该领域存在的问题及发展方向。遗传算法主要被应用于注胶口及流道配置优化方面,但在复杂问题中收敛性较差,与其他局部搜索算法结合的方法展现出解决复杂问题的潜力;机器学习方法的应用研究处于起步阶段,目前主要被应用于注射压力、浸渍质量、渗透率预测等方面,只对简单二维充模问题进行了研究;其他人工智能方法通常计算成本低,但难以验证最优性。人工智能方法的问题集中在迭代/训练所需的数据集的获取成本方面。其在三维复杂几何结构及非均匀渗透率制件方面的应用是未来的重要发展方向。
Abstract: The simulation of resin transfer molding (RTM) is crucial for reducing the cost of RTM process. Introducing artificial intelligence methods into the RTM process simulation can provide guiding opinions for the design of RTM molds without solving complex models of multi-scale flow. This paper reviews the research status of artificial intelligence algorithms, mainly including genetic algorithms and machine learning methods in RTM simulation, and discusses the existing problems and development directions of artificial intelligence methods in the field of RTM simulation. Genetic algorithms are mainly applied in the optimization of injection gates and flow channel configurations, but have poor convergence in complex problems. The method, combined with other local search algorithms, shows potential for solving complex problems. The application research of machine learning methods is in its infancy. At present, it is mainly applied in the prediction of injection pressure, impregnation quality, permeability, etc., and only simple two-dimensional problems have been studied. Other artificial intelligence methods usually have low computational cost, but it is difficult to verify optimality. The main problems of artificial intelligence methods are concentrated in the acquisition cost of the data set required for iteration/training. Their application in three-dimensional complex geometric composites and composites with non-uniform permeability is an important development direction in the future.
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2026, Vol. 40 
