用于开孔拉伸复合材料失效预测的机器学习增强特征长度法

IF 5.3 Q2 MATERIALS SCIENCE, COMPOSITES
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引用次数: 0

摘要

特征长度法是一种预测开孔和闭孔复合材料失效的非局部方法。这种方法需要确定复合材料层压板在失效载荷下的线性弹性应力场。通常,这需要计算昂贵的渐进损伤和线性弹性建模,并通过有限元分析(FEA)进行模拟。在本研究中,我们展示了机器学习方法在高效、准确地预测开孔复合材料层压板特征长度方面的优势。我们发现,对载荷-位移曲线的预测有助于最终失效载荷的预测。我们还发现,使用长短期记忆神经网络而不是卷积解码器神经网络能更准确地预测线性弹性应力场。我们表明,在有足够训练数据的情况下,通过独立预测破坏载荷和线性弹性应力场来间接预测特征长度,比直接预测特征长度的结果更灵活、更可解释、更准确。与基于有限元分析的方法相比,我们的机器学习辅助特征长度方法可节省五个数量级以上的时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A machine learning enhanced characteristic length method for failure prediction of open hole tension composites

A machine learning enhanced characteristic length method for failure prediction of open hole tension composites
The characteristic length method is a non-local approach to predicting the failure of open and closed-hole composite features. This method requires the determination of the linear elastic stress field of the composite laminate at its failure load. Typically, this requires computationally expensive progressive damage and linear elastic modelling and simulation with finite element analysis (FEA). In this study, we demonstrate the benefit of machine learning methods to efficiently and accurately predict characteristic lengths of composite laminates with open holes. We find that the prediction of the load-displacement profile usefully informs ultimate failure load prediction. We also find that linear elastic stress fields are more accurately predicted using a long-short term memory neural network rather than a convolutional decoder neural network. We show indirect prediction of characteristic length, via prediction of failure loads and linear elastic stress fields independently, results in more flexible, interpretable and accurate results than direct prediction of characteristic length, given sufficient training data. Our machine learning-assisted characteristic length method shows over five orders of magnitude of time-saving benefit compared to FEA-based methods.
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来源期刊
Composites Part C Open Access
Composites Part C Open Access Engineering-Mechanical Engineering
CiteScore
8.60
自引率
2.40%
发文量
96
审稿时长
55 days
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