用神经网络预测注塑成型部件的纤维取向及其几何影响

IF 2.3 3区 材料科学 Q3 MATERIALS SCIENCE, COMPOSITES
Till Hermann, Dariusz Niedziela, Diyora Salimova, Timo Schweiger
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引用次数: 0

摘要

短纤维增强塑料(SFRP)的注塑成型模拟非常耗时。然而,迄今为止,它对于预测局部纤维取向、优化成型工艺和预测材料的机械性能是非常必要的。本研究介绍了人工神经网络(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。以模拟注塑成型板为基础,比较了三种 NN 架构,目的是预测板的几何形状对局部纤维取向的影响。结果表明,NN 优于对齐纤维的基线假设,并展示了准确预测 FOT 的巨大潜力。NN 的计算效率,尤其是在预测阶段,与传统模拟方法相比,减少了 104 倍的处理时间。这项研究为进一步探索 NN 在注塑成型工艺实际应用中部分取代耗时模拟的可行性奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting the fiber orientation of injection molded components and the geometry influence with neural networks
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.
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来源期刊
Journal of Composite Materials
Journal of Composite Materials 工程技术-材料科学:复合
CiteScore
5.40
自引率
6.90%
发文量
274
审稿时长
6.8 months
期刊介绍: Consistently ranked in the top 10 of the Thomson Scientific JCR, the Journal of Composite Materials publishes peer reviewed, original research papers from internationally renowned composite materials specialists from industry, universities and research organizations, featuring new advances in materials, processing, design, analysis, testing, performance and applications. This journal is a member of the Committee on Publication Ethics (COPE).
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