工艺与制造英文2024被引 10
热塑性复合材料原位自动铺纤工艺热学建模的理论引导机器学习方法
Theory-guided machine learning for thermal modeling of in-situ automated fiber placement of thermoplastic composites
A. Fontes, Navid Zobeiry, Farjad Shadmehri · AS Composite (Canada)
摘要整理
原位自动铺纤（AFP）热塑性复合材料制造相比传统工艺具有显著优势，主要体现在消除二次热处理工序。在无二次加热的条件下，原位热历史成为控制界面结合发展、结晶动力学和残余应力演变的关键工艺参数。本研究通过理论引导机器学习（TGML）方法改进了原位AFP制造工艺的热学建模。提出了一种新型理论引导神经网络（TgNN），采用基于理论的预层变换来模拟原位AFP制造过程中的三维温度分布。该网络在不同热气枪温度（工艺参数）和热源移动速度组合条件下的实验测量温度数据上进行训练。特征工程通过对输入特征（时间、热电偶坐标、热气枪温度、热源速度）应用理论基础的预层变换来实现。与理论无关的神经网络相比，采用理论基础预层变换的TgNN具有更强的预测能力，且在相同性能下所需训练数据量更少。训练后的模型计算效率高，可用于在线工艺控制。
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