Neural networks modeling of strain energy density and Tsai-Wu index in laminated composites

IF 2.3 3区 材料科学 Q3 MATERIALS SCIENCE, COMPOSITES
Elías Ledesma-Orozco, Julio C. Galvis-Chacón, Alejandro E. Rodríguez-Sánchez
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引用次数: 0

Abstract

In laminated composite materials design, optimization mainly targets the stacking sequence configuration, which is defined by the lamina thickness and fiber orientations within each layer. Recent studies emphasize the increasing role of Machine Learning in promoting innovative composite designs by facilitating the accurate modeling of essential properties such as strength and stiffness. This study introduces two metamodels that utilize feed-forward artificial neural networks, taking laminate thickness and fiber steering angles as input parameters. The output variables, including strain energy density and the Tsai-Wu failure index, enable the prediction of stacking sequence configurations for laminated materials, a capability confirmed in a case study. The results showcase neural network models with the ability to predict these variables, achieving coefficients of determination above 0.90 for testing data. Consequently, this modeling approach has the potential to be a tool for designers, aiding in decision-making processes for the subsequent optimization of stiffness and strength in structural components made of laminated composite materials.
层状复合材料应变能密度和蔡武指数的神经网络建模
在层压复合材料设计中,优化主要针对堆叠顺序配置,而堆叠顺序配置是由每层内的薄片厚度和纤维取向决定的。最近的研究强调,机器学习通过促进强度和刚度等基本属性的精确建模,在促进创新复合材料设计方面发挥着越来越重要的作用。本研究引入了两个元模型,利用前馈式人工神经网络,将层板厚度和纤维转向角作为输入参数。输出变量包括应变能密度和蔡-吴失效指数,能够预测层压材料的堆叠顺序配置,这一能力在案例研究中得到了证实。研究结果表明,神经网络模型具有预测这些变量的能力,测试数据的决定系数超过 0.90。因此,这种建模方法有可能成为设计人员的工具,有助于决策过程,进而优化层压复合材料结构组件的刚度和强度。
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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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