Utilizing a combination of experimental and machine learning methods to predict and correlate between accelerated and natural aging of CFRP/AL adhesive joints under hygrothermal conditions

材料科学 复合材料 胶粘剂 结构工程 工程类 图层(电子)
作者
Sajjad Karimi,Jianyong Yu
出处
期刊:Polymer Composites [Wiley]
被引量:4
标识
DOI:10.1002/pc.29226
摘要

Abstract This study investigates how carbon fiber reinforced polymer (CFRP)‐to‐aluminum adhesive joints behave under accelerated aging conditions with hygrothermal exposure and compares these findings against naturally aged samples to evaluate material reliability in challenging environments. The CFRP‐to‐aluminum adhesive joints were manufactured and subjected to natural aging for durations ranging from 1 to 3 years with 6‐month intervals, as well as accelerated aging (hygrothermal) for periods ranging from 100 to 1200 h, with intervals of 50 h. Subsequently, the mechanical properties of the joints were evaluated using a three‐point bending test. To forecast natural aging times from accelerated aging data, five machine learning models were utilized: artificial neural network, support vector regression, linear regression, polynomial regression, and random forest regression. Hygrothermal aging significantly degraded the matrix, causing a shift in failure modes from cohesive to mixed types (cohesive, adhesive, and fiber tear failures), leading to a notable decline in bending strength. The study observed a 23.13% strength reduction in samples aged naturally for 3 years and a 24.33% decrease in those subjected to 1000 h of accelerated aging. The random forest regressor demonstrated superior accuracy in predicting natural aging times across different accelerated aging periods. Through the application of machine learning models, this study introduces a novel approach to forecast natural aging durations using data from accelerated aging experiments. This method shows potential for optimizing joints and composite structures, ultimately improving their durability and minimizing the likelihood of failures during operational use. Highlights Studied hygrothermal effects on accelerated aging of carbon fiber reinforced polymer/Aluminum (AL) adhesive joints. Noted strength reduction from hygrothermal aging. Used five machine learning models; random forest regression had the highest accuracy. Analyzed correlation between natural and accelerated aging of dissimilar adhesive joints.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
大观天下发布了新的文献求助10
1秒前
忽远忽近的她完成签到 ,获得积分10
1秒前
维生素发布了新的文献求助10
2秒前
butterfly发布了新的文献求助10
4秒前
豆豆完成签到 ,获得积分10
5秒前
范先生完成签到,获得积分10
8秒前
2222222完成签到,获得积分10
8秒前
Hello应助bulingbuling采纳,获得10
8秒前
蜡笔小新完成签到,获得积分10
11秒前
希望天下0贩的0应助小王采纳,获得10
11秒前
赘婿应助lh采纳,获得10
12秒前
12秒前
科研通AI2S应助butterfly采纳,获得10
13秒前
大模型应助butterfly采纳,获得10
13秒前
15秒前
做个梦给你完成签到,获得积分10
15秒前
学霸宇大王完成签到 ,获得积分10
15秒前
甜蜜的楷瑞完成签到,获得积分10
16秒前
魏煜佳完成签到,获得积分10
17秒前
Lc完成签到,获得积分10
17秒前
三伏天完成签到,获得积分10
17秒前
清图完成签到,获得积分10
17秒前
英姑应助简单采纳,获得10
17秒前
爱喝牛奶的大兔子完成签到 ,获得积分20
18秒前
19秒前
19秒前
潇湘雪月完成签到,获得积分10
20秒前
迎南完成签到,获得积分10
20秒前
懒癌晚期完成签到,获得积分10
21秒前
21秒前
初夏微凉发布了新的文献求助10
21秒前
21秒前
22秒前
23秒前
乐乐应助科研通管家采纳,获得10
24秒前
深情安青应助科研通管家采纳,获得10
24秒前
赘婿应助科研通管家采纳,获得10
24秒前
英姑应助科研通管家采纳,获得10
24秒前
田様应助科研通管家采纳,获得10
24秒前
所所应助科研通管家采纳,获得10
24秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
Research on Disturbance Rejection Control Algorithm for Aerial Operation Robots 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038388
求助须知:如何正确求助?哪些是违规求助? 3576106
关于积分的说明 11374447
捐赠科研通 3305798
什么是DOI,文献DOI怎么找? 1819322
邀请新用户注册赠送积分活动 892672
科研通“疑难数据库(出版商)”最低求助积分说明 815029