利用人工神经网络预测复合板元件的屈曲行为

K. Falkowicz, Monika Kulisz
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引用次数: 0

摘要

本文介绍了如何利用人工神经网络(ANN)来分析带切口的复合板元素,这种元素可以作为弹簧元素使用。分析以数值方法的结果为基础。利用获得的数值数据开发了 ANNs 模型,以预测复合板在不同切口和角度配置下的自然屈曲扭转形式。使用大量数据集对 ANNs 模型进行了训练和测试,并使用各种统计方法对其准确性进行了评估。所开发的 ANNs 模型在预测具有不同切口和纤维天使配置的薄壁板在压缩条件下的临界力和屈曲形式方面表现出了很高的准确性。数值分析与 ANNs 模型的结合为评估带有切口的复合材料板的稳定性提供了一种实用高效的解决方案,可用于工程应用中的设计优化和结构监测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Buckling Behaviour of Composite Plate Element Using Artificial Neural Networks
This article presents the use of artificial neural networks (ANNs) to analysis of the composite plate elements with cut-outs which can work as a spring element. The analysis were based on results from numerical approach. ANNs models have been developed utilizing the obtained numerical data to predict the composite plate’s flexural-torsional form of buckling as natural form for different cut-outs and angels configurations. The ANNs models were trained and tested using a large dataset, and their accuracy is evaluated using various statistical measures. The developed ANNs models demonstrated high accuracy in predicting the critical force and buckling form of thin-walled plates with different cut-out and fiber angels configurations under compression. The combination of numerical analyses with ANNs models provides a practical and efficient solution for evaluating the stability be - haviour of composite plates with cut-outs, which can be useful for design optimization and structural monitoring in engineering applications.
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