The injection molding simulation of short fiber reinforced plastics (SFRP) is time consuming. However, until now it is necessary for predicting the local fiber orientation, to optimize the molding process and to predict the mechanical behavior of the material. This research presents the capabilities of artificial neural networks (NN) in predicting fiber orientation tensor (FOT) during injection molding processes, with a focus on enhancing computational efficiency compared to traditional simulation methods. Three NN architectures are compared based on simulated injection molded plates, with the goal of predicting the effect of the plate geometry on the local fiber orientation. Results indicate that NN outperform the baseline assumption of aligned fibers and demonstrate significant potential for accurate FOT prediction. The computational efficiency of NN, especially during the prediction phase, showcases a reduction in processing time by a factor of 104 compared to traditional simulation methods. This research lays a foundation for further exploration into the feasibility of NN in partly replacing time-consuming simulations for practical applications in injection molding processes.

中文翻译：

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使用神经网络预测注塑组件的纤维取向和几何影响

短纤维增强塑料 (SFRP) 的注塑成型模拟非常耗时。然而，到目前为止，有必要预测局部纤维取向、优化成型工艺并预测材料的机械行为。这项研究展示了人工神经网络 (NN) 在注塑成型过程中预测纤维取向张量 (FOT) 的能力，重点是与传统模拟方法相比提高计算效率。基于模拟注塑板比较了三种神经网络架构，目的是预测板几何形状对局部纤维取向的影响。结果表明，神经网络优于对齐纤维的基线假设，并展示了准确 FOT 预测的巨大潜力。神经网络的计算效率，尤其是在预测阶段，处理时间减少了 10 倍4与传统的模拟方法相比。该研究为进一步探索神经网络在注塑工艺实际应用中部分替代耗时模拟的可行性奠定了基础。