Influence of the cure state on mechanical properties of an epoxy‐based adhesive: Experimental characterization and numerical simulation

固化(化学) 材料科学 复合材料 胶粘剂 环氧树脂 差示扫描量热法 极限抗拉强度 傅里叶变换红外光谱 环氧胶粘剂 热力学 物理 图层(电子) 量子力学
作者
Paulo Roberto Teixeira,A. Akhavan‐Safar,Ricardo J. C. Carbas,Lucas F. M. da Silva
出处
期刊:Polymers for Advanced Technologies [Wiley]
卷期号:33 (4): 1163-1170 被引量:8
标识
DOI:10.1002/pat.5589
摘要

Abstract Heat curing epoxy‐based adhesives are extensively used in primary bonded structures. The manufacturing process of joints with heat curing adhesives is commonly made with isothermal processes where a large curing time is set to guarantee the complete curing of the adhesive. The aim of the current study is to investigate the influence of the curing state on the tensile mechanical properties of a structural one‐component epoxy based adhesive. To achieve this, tensile tests on bulk adhesive dog‐bone coupons with incomplete curing were performed. The process cycle was adjusted to achieve the desired curing degree for testing. The curing process was simulated by using the Kamal kinetics model calibrated with differential scanning calorimetry (DSC) tests. The results show that the curing process is very sensitive to the curing temperature, requiring a low curing temperature to control process duration. A good correlation between the estimated curing degree and the final obtained curing measured with the Fourier‐transform infrared spectroscopy FTIR was found. Regarding the mechanical properties, the elastic modulus and the tensile strength are reduced at lower curing degrees making the material softer and more ductile. The loss in the mechanical properties shows to be consistent with the measurements performed with dynamical mechanical analyses (DMA) by measuring the evolution of the elastic modulus with the curing degree. In this way, the numerical simulation of the curing process seems to be a valuable tool to predict the final performance of adhesive, and design the curing cycle.

科研通智能强力驱动
Strongly Powered by AbleSci AI
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
1秒前
1秒前
远远发布了新的文献求助10
1秒前
2秒前
2秒前
Orange应助难过思菱采纳,获得10
3秒前
3秒前
4秒前
复杂黑夜完成签到,获得积分20
4秒前
搜集达人应助淡然的镜子采纳,获得10
4秒前
4秒前
4秒前
正直月饼发布了新的文献求助10
4秒前
阿游完成签到 ,获得积分10
4秒前
4秒前
量子星尘发布了新的文献求助10
6秒前
一一发布了新的文献求助10
6秒前
7秒前
五号完成签到,获得积分10
7秒前
7秒前
8秒前
xixi很困发布了新的文献求助10
8秒前
8秒前
vine发布了新的文献求助10
8秒前
一路朝阳发布了新的文献求助10
8秒前
8秒前
8秒前
8秒前
华仔应助韩han采纳,获得10
9秒前
9秒前
知识四面八方来完成签到 ,获得积分10
9秒前
所所应助lsw采纳,获得10
9秒前
11秒前
上上签完成签到,获得积分10
12秒前
充电宝应助Zz采纳,获得10
12秒前
wxl关注了科研通微信公众号
12秒前
乐乐应助李文娜采纳,获得10
13秒前
11发布了新的文献求助10
13秒前
张江泽完成签到,获得积分10
13秒前
辣小扬发布了新的文献求助10
13秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Basic And Clinical Science Course 2025-2026 3000
Encyclopedia of Agriculture and Food Systems Third Edition 2000
人脑智能与人工智能 1000
花の香りの秘密―遺伝子情報から機能性まで 800
Principles of Plasma Discharges and Materials Processing, 3rd Edition 400
Pharmacology for Chemists: Drug Discovery in Context 400
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5609955
求助须知:如何正确求助?哪些是违规求助? 4694535
关于积分的说明 14882709
捐赠科研通 4720767
什么是DOI,文献DOI怎么找? 2544982
邀请新用户注册赠送积分活动 1509819
关于科研通互助平台的介绍 1473013