人工智能方法在RTM工艺仿真中的应用

doi:10.11896/cldb.24120233

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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.

Key words： composite resin transfer molding(RTM) simulation parameter optimization artificial intelligence

Published: 10 January 2026 Online: 2026-01-09

CLC number:

TB322

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	JIANG Jinchi

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JIANG Jinchi. The Application of Artificial Intelligence Methods in RTM Simulation[J]. Materials Reports, 2026, 40(1): 24120233-8.

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https://www.mater-rep.com/EN/10.11896/cldb.24120233 OR https://www.mater-rep.com/EN/Y2026/V40/I1/24120233

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