A novel machine learning framework for impact force prediction of foam-filled multi-layer lattice composite structures

IF 5.7 1区工程技术 Q1 ENGINEERING, CIVIL

Thin-Walled Structures Pub Date : 2024-10-21 DOI:10.1016/j.tws.2024.112607

Jiye Chen , Yufeng Zhao , Hai Fang , Zhixiong Zhang , Zheheng Chen , Wangwang He

{"title":"A novel machine learning framework for impact force prediction of foam-filled multi-layer lattice composite structures","authors":"Jiye Chen , Yufeng Zhao , Hai Fang , Zhixiong Zhang , Zheheng Chen , Wangwang He","doi":"10.1016/j.tws.2024.112607","DOIUrl":null,"url":null,"abstract":"<div><div>Numerical simulations can provide valuable insights for the optimization of design and operational management; however, they are often impractical and computationally intensive. Machine learning methods are appealing to these problems due to their sufficient efficiency and accuracy. In this study, a novel framework for predicting the impact responses of foam-filled multi-layer lattice composite structures (FMLCSs) was proposed by combining the accurate finite element (FE) analyses, surrogate models, fast Fourier transform (FFT) method, and inverse FFT (IFFT) method. Firstly, reliable FM models were established to simulate the crashworthiness of the five FMLCSs under impact loading, including an analysis of energy transformation. Subsequently, surrogate models, namely radial basis function (RBF), polynomial response surface (PRS), Kriging (KRG), and back propagation neural network (BPNN), combined with methods of FFT and IFFT, were employed to predict the impact force-time series of the FMLCSs. More than 1000 frequency points were employed for each type of FMLCS, and all the R-square (<em>R</em><sup>2</sup>) values of the established surrogate models exceeded 0.95, indicating that the proposed framework accurately predicted the impact duration and impact responses in the frequency domain. In addition, parameter sensitivity analysis revealed that a high peak impact force was accompanied by a short impact duration. Moreover, increasing the lattice-web height resulted in a significant increase in the impact duration.</div></div>","PeriodicalId":49435,"journal":{"name":"Thin-Walled Structures","volume":"205 ","pages":"Article 112607"},"PeriodicalIF":5.7000,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Thin-Walled Structures","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0263823124010474","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}

引用次数: 0

Abstract

Numerical simulations can provide valuable insights for the optimization of design and operational management; however, they are often impractical and computationally intensive. Machine learning methods are appealing to these problems due to their sufficient efficiency and accuracy. In this study, a novel framework for predicting the impact responses of foam-filled multi-layer lattice composite structures (FMLCSs) was proposed by combining the accurate finite element (FE) analyses, surrogate models, fast Fourier transform (FFT) method, and inverse FFT (IFFT) method. Firstly, reliable FM models were established to simulate the crashworthiness of the five FMLCSs under impact loading, including an analysis of energy transformation. Subsequently, surrogate models, namely radial basis function (RBF), polynomial response surface (PRS), Kriging (KRG), and back propagation neural network (BPNN), combined with methods of FFT and IFFT, were employed to predict the impact force-time series of the FMLCSs. More than 1000 frequency points were employed for each type of FMLCS, and all the R-square (R²) values of the established surrogate models exceeded 0.95, indicating that the proposed framework accurately predicted the impact duration and impact responses in the frequency domain. In addition, parameter sensitivity analysis revealed that a high peak impact force was accompanied by a short impact duration. Moreover, increasing the lattice-web height resulted in a significant increase in the impact duration.

查看原文本刊更多论文

用于泡沫填充多层晶格复合结构冲击力预测的新型机器学习框架

数值模拟可以为优化设计和运营管理提供有价值的见解；然而，数值模拟往往不切实际，而且计算密集。机器学习方法具有足够的效率和准确性，因此对这些问题很有吸引力。本研究结合精确的有限元（FE）分析、代用模型、快速傅立叶变换（FFT）方法和反FFT（IFFT）方法，提出了预测泡沫填充多层晶格复合材料结构（FMLCS）冲击响应的新框架。首先，建立了可靠的 FM 模型来模拟五种 FMLCS 在冲击载荷下的耐撞性，包括能量转换分析。随后，采用径向基函数（RBF）、多项式响应面（PRS）、Kriging（KRG）和反向传播神经网络（BPNN）等代用模型，结合 FFT 和 IFFT 方法，预测了 FMLCS 的冲击力-时间序列。每种类型的 FMLCS 都采用了超过 1000 个频率点，所建立的代用模型的 R-square（R2）值都超过了 0.95，表明所提出的框架能准确预测频域内的冲击持续时间和冲击响应。此外，参数敏感性分析表明，峰值冲击力大，冲击持续时间短。此外，增加格网高度会显著增加冲击持续时间。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Thin-Walled Structures 工程技术-工程：土木

CiteScore

9.60

自引率

20.30%

发文量

801

审稿时长

66 days

期刊介绍： Thin-walled structures comprises an important and growing proportion of engineering construction with areas of application becoming increasingly diverse, ranging from aircraft, bridges, ships and oil rigs to storage vessels, industrial buildings and warehouses. Many factors, including cost and weight economy, new materials and processes and the growth of powerful methods of analysis have contributed to this growth, and led to the need for a journal which concentrates specifically on structures in which problems arise due to the thinness of the walls. This field includes cold– formed sections, plate and shell structures, reinforced plastics structures and aluminium structures, and is of importance in many branches of engineering. The primary criterion for consideration of papers in Thin–Walled Structures is that they must be concerned with thin–walled structures or the basic problems inherent in thin–walled structures. Provided this criterion is satisfied no restriction is placed on the type of construction, material or field of application. Papers on theory, experiment, design, etc., are published and it is expected that many papers will contain aspects of all three.