Xin Tong, Jianfeng Yu, Shengqiang Cui, Dong Xue, Jie Zhang, Yuan Li
{"title":"Assembly damage assessment of composite plates using uncertainty quantification and statistical analysis","authors":"Xin Tong, Jianfeng Yu, Shengqiang Cui, Dong Xue, Jie Zhang, Yuan Li","doi":"10.1007/s00419-024-02753-9","DOIUrl":null,"url":null,"abstract":"<div><p>During the assembly process, deformation occurs in composite thin-walled parts, leading to damage at the hole connections of their single-longitudinal splice (SLS) joints. To predict assembly damage in the design process, it is usually necessary to combine uncertainty analysis with finite element analysis (FEA) methods. However, this field has limited progress due to the large size of the analyzed objects relative to their span and the large number of constraints that increase computational costs and complexity. In this study, we employed a linear elastic method to analyze the assembly process of thin-walled components. We achieved the uncertainty propagation (UP) and uncertainty quantification (UQ) of profile deviation random fields by using sub-modeling techniques, and obtained the stress distribution around the connection holes. The validity of the method was confirmed through a comparison with a full-process FEA of a three-hole SLS model. By integrating our method with the Hashin damage criterion, we proposed a statistical analysis approach for predicting assembly failure in SLS joints. The results demonstrated that considering only the profile deviation of the composite panel wall had a significant impact on material damage.</p></div>","PeriodicalId":477,"journal":{"name":"Archive of Applied Mechanics","volume":"95 2","pages":""},"PeriodicalIF":2.2000,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Archive of Applied Mechanics","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1007/s00419-024-02753-9","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MECHANICS","Score":null,"Total":0}
引用次数: 0
Abstract
During the assembly process, deformation occurs in composite thin-walled parts, leading to damage at the hole connections of their single-longitudinal splice (SLS) joints. To predict assembly damage in the design process, it is usually necessary to combine uncertainty analysis with finite element analysis (FEA) methods. However, this field has limited progress due to the large size of the analyzed objects relative to their span and the large number of constraints that increase computational costs and complexity. In this study, we employed a linear elastic method to analyze the assembly process of thin-walled components. We achieved the uncertainty propagation (UP) and uncertainty quantification (UQ) of profile deviation random fields by using sub-modeling techniques, and obtained the stress distribution around the connection holes. The validity of the method was confirmed through a comparison with a full-process FEA of a three-hole SLS model. By integrating our method with the Hashin damage criterion, we proposed a statistical analysis approach for predicting assembly failure in SLS joints. The results demonstrated that considering only the profile deviation of the composite panel wall had a significant impact on material damage.
期刊介绍:
Archive of Applied Mechanics serves as a platform to communicate original research of scholarly value in all branches of theoretical and applied mechanics, i.e., in solid and fluid mechanics, dynamics and vibrations. It focuses on continuum mechanics in general, structural mechanics, biomechanics, micro- and nano-mechanics as well as hydrodynamics. In particular, the following topics are emphasised: thermodynamics of materials, material modeling, multi-physics, mechanical properties of materials, homogenisation, phase transitions, fracture and damage mechanics, vibration, wave propagation experimental mechanics as well as machine learning techniques in the context of applied mechanics.