• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

评估数字图像相关技术在确定钢/复合材料混合结构表面应变中的有效性和质量

Assessment of Digital Image Correlation Effectiveness and Quality in Determination of Surface Strains of Hybrid Steel/Composite Structures.

作者信息

Romanowicz Paweł J, Szybiński Bogdan, Wygoda Mateusz

机构信息

Department of Machine Design and Composite Structures, Faculty of Mechanical Engineering, Cracow University of Technology, ul. Warszawska 24, 31-155 Cracow, Poland.

Department of Product Technology and Ecology, College of Management and Quality Sciences, Cracow University of Economics, 31-510 Cracow, Poland.

出版信息

Materials (Basel). 2024 Jul 18;17(14):3561. doi: 10.3390/ma17143561.

DOI:10.3390/ma17143561
PMID:39063853
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11278566/
Abstract

The application of the digital image correlation (DIC) contactless method has extended the possibilities of reliable assessment of structure strain fields and deformations throughout the last years. However, certain weak points in the analyses using the DIC method still exist. The fluctuations of the results caused by different factors as well as certain deficiencies in the evaluation of DIC accuracy in applications for hybrid steel/composite structures with adhesive joints are one of them. In the proposed paper, the assessment of DIC accuracy based on the range of strain fluctuation is proposed. This relies on the use of a polynomial approximation imposed on the results obtained from the DIC method. Such a proposal has been used for a certain correction of the DIC solution and has been verified by the introduction of different error measures. The evaluation of DIC possibilities and accuracy are presented on the examples of the static tensile tests of adhesively bonded steel/composite joints with three different adhesives applied. The obtained results clearly show that in a non-disturbed area, very good agreement between approximated DIC and FEM results is achieved. The relative average errors in an area, determined by comparison of DIC and FEM strains, are below 15%. It is also observed that the use of approximated strains by polynomial function leads to a more accurate solution with respect to FEM results. It is concluded that DIC can be successfully applied for the analyses of hybrid steel/adhesive/composite samples, such as determination of strain fields, non-contact visual detection of faults of manufacturing and their development and influence on the whole structure behavior during the strength tests, including the elastic response of materials.

摘要

在过去几年中,数字图像相关(DIC)非接触式方法的应用扩展了可靠评估结构应变场和变形的可能性。然而,使用DIC方法进行分析时仍存在某些弱点。不同因素导致的结果波动以及在钢/复合材料混合结构粘接接头应用中DIC精度评估的某些不足就是其中之一。在本文中,提出了基于应变波动范围的DIC精度评估方法。这依赖于对从DIC方法获得的结果施加多项式近似。这样的提议已用于对DIC解进行一定的修正,并通过引入不同的误差度量进行了验证。以使用三种不同粘合剂的粘接钢/复合材料接头的静态拉伸试验为例,介绍了DIC的可能性和精度评估。获得的结果清楚地表明,在无干扰区域,近似DIC结果与有限元法(FEM)结果之间取得了很好的一致性。通过比较DIC和FEM应变确定的区域内相对平均误差低于15%。还观察到,使用多项式函数的近似应变相对于FEM结果会得到更准确的解。得出的结论是,DIC可以成功应用于钢/粘合剂/复合材料混合样品的分析,例如应变场的确定、制造缺陷的非接触视觉检测及其发展以及强度试验期间对整个结构行为的影响,包括材料的弹性响应。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/1977a25725cd/materials-17-03561-g025.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/45bee94e380f/materials-17-03561-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/ca5f7356d55b/materials-17-03561-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/f9728756c090/materials-17-03561-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/096dac2cdec8/materials-17-03561-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/321996d32017/materials-17-03561-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/669de649ae40/materials-17-03561-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/a5e491121638/materials-17-03561-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/01744acf9ca6/materials-17-03561-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/771187467d5a/materials-17-03561-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/3512beb21e47/materials-17-03561-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/4669691a8956/materials-17-03561-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/bd8a75a42139/materials-17-03561-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/c27ca3c260c4/materials-17-03561-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/eede414bfd3c/materials-17-03561-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/eb94938467e7/materials-17-03561-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/76b7616dca32/materials-17-03561-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/74d8f2fcdfa6/materials-17-03561-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/0c26ea4a25c5/materials-17-03561-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/e8f71d74bb5f/materials-17-03561-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/c321c013d9d6/materials-17-03561-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/7e1bb8e025a1/materials-17-03561-g021.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/77939b337ad5/materials-17-03561-g022.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/0ee5969a59c5/materials-17-03561-g023.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/a54f9cb23f44/materials-17-03561-g024.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/1977a25725cd/materials-17-03561-g025.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/45bee94e380f/materials-17-03561-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/ca5f7356d55b/materials-17-03561-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/f9728756c090/materials-17-03561-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/096dac2cdec8/materials-17-03561-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/321996d32017/materials-17-03561-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/669de649ae40/materials-17-03561-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/a5e491121638/materials-17-03561-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/01744acf9ca6/materials-17-03561-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/771187467d5a/materials-17-03561-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/3512beb21e47/materials-17-03561-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/4669691a8956/materials-17-03561-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/bd8a75a42139/materials-17-03561-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/c27ca3c260c4/materials-17-03561-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/eede414bfd3c/materials-17-03561-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/eb94938467e7/materials-17-03561-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/76b7616dca32/materials-17-03561-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/74d8f2fcdfa6/materials-17-03561-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/0c26ea4a25c5/materials-17-03561-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/e8f71d74bb5f/materials-17-03561-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/c321c013d9d6/materials-17-03561-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/7e1bb8e025a1/materials-17-03561-g021.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/77939b337ad5/materials-17-03561-g022.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/0ee5969a59c5/materials-17-03561-g023.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/a54f9cb23f44/materials-17-03561-g024.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9467/11278566/1977a25725cd/materials-17-03561-g025.jpg

相似文献

1
Assessment of Digital Image Correlation Effectiveness and Quality in Determination of Surface Strains of Hybrid Steel/Composite Structures.评估数字图像相关技术在确定钢/复合材料混合结构表面应变中的有效性和质量
Materials (Basel). 2024 Jul 18;17(14):3561. doi: 10.3390/ma17143561.
2
Application of DIC Method in the Analysis of Stress Concentration and Plastic Zone Development Problems.数字图像相关方法在应力集中和塑性区发展问题分析中的应用
Materials (Basel). 2020 Aug 5;13(16):3460. doi: 10.3390/ma13163460.
3
Investigation of Compressive and Tensile Behavior of Stainless Steel/Dissolvable Aluminum Bimetallic Composites by Finite Element Modeling and Digital Image Correlation.基于有限元建模与数字图像相关技术的不锈钢/可溶解铝双金属复合材料压缩与拉伸行为研究
Materials (Basel). 2021 Jun 30;14(13):3654. doi: 10.3390/ma14133654.
4
The Tensile Behavior of Hybrid Bonded Bolted Composite Joints: 3D-Digital Image Correlation versus Finite Element Analysis.混合粘结螺栓连接复合材料接头的拉伸行为:三维数字图像相关法与有限元分析
Materials (Basel). 2024 Apr 5;17(7):1675. doi: 10.3390/ma17071675.
5
Impact Fatigue Life of Adhesively Bonded Composite-Steel Joints Enhanced with the Bi-Adhesive Technique.采用双胶粘剂技术提高胶粘复合钢接头的冲击疲劳寿命。
Materials (Basel). 2023 Jan 2;16(1):419. doi: 10.3390/ma16010419.
6
Analysis of Strain Field Heterogeneity at the Microstructure Level and Inverse Identification of Composite Constituents by Means of Digital Image Correlation.基于数字图像相关技术的微观结构层面应变场非均匀性分析及复合材料组分反演识别
Materials (Basel). 2020 Jan 8;13(2):287. doi: 10.3390/ma13020287.
7
Application of Digital Image Correlation for Strain Mapping of Structural Elements and Materials.数字图像相关技术在结构元件和材料应变映射中的应用。
Materials (Basel). 2024 May 27;17(11):2577. doi: 10.3390/ma17112577.
8
Static and Fatigue Behaviour of Double-Lap Adhesive Joints and Notched Metal Samples Reinforced by Composite Overlays.
Materials (Basel). 2022 Apr 29;15(9):3233. doi: 10.3390/ma15093233.
9
Digital image correlation in dental materials and related research: A review.数字图像相关技术在牙科材料及相关研究中的应用:综述。
Dent Mater. 2021 May;37(5):758-771. doi: 10.1016/j.dental.2021.02.024. Epub 2021 Mar 11.
10
Digital Image Correlation and Ultrasonic Lamb Waves for the Detection and Prediction of Crack-Type Damage in Fiber-Reinforced Polymer Composite Laminates.用于检测和预测纤维增强聚合物复合材料层压板中裂纹型损伤的数字图像相关和超声兰姆波技术
Polymers (Basel). 2024 Jul 11;16(14):1980. doi: 10.3390/polym16141980.

引用本文的文献

1
The Influence of Rolling Direction and Dynamic Strengthening on the Properties of Steel.轧制方向和动态强化对钢性能的影响
Materials (Basel). 2025 Aug 13;18(16):3808. doi: 10.3390/ma18163808.
2
Study on Relationship Between Mechanical Properties and Water Absorption Characteristics of Mortars by Using Digital Image Correlation Method (DICM).基于数字图像相关法(DICM)的砂浆力学性能与吸水特性关系研究
Materials (Basel). 2025 Mar 6;18(5):1182. doi: 10.3390/ma18051182.

本文引用的文献

1
Static and Fatigue Behaviour of Double-Lap Adhesive Joints and Notched Metal Samples Reinforced by Composite Overlays.
Materials (Basel). 2022 Apr 29;15(9):3233. doi: 10.3390/ma15093233.
2
Determination of Optimal Flat-End Head Geometries for Pressure Vessels Based on Numerical and Experimental Approaches.基于数值和实验方法确定压力容器的最佳平封头几何形状
Materials (Basel). 2021 May 12;14(10):2520. doi: 10.3390/ma14102520.
3
Application of DIC Method in the Analysis of Stress Concentration and Plastic Zone Development Problems.数字图像相关方法在应力集中和塑性区发展问题分析中的应用
Materials (Basel). 2020 Aug 5;13(16):3460. doi: 10.3390/ma13163460.
4
Estimation of Notched Composite Plates Fatigue Life Using Residual Strength Model Calibrated by Step-Wise Tests.基于逐步试验校准的残余强度模型估算带缺口复合材料板的疲劳寿命
Materials (Basel). 2018 Nov 3;11(11):2180. doi: 10.3390/ma11112180.
5
Optical Fiber Sensors for Aircraft Structural Health Monitoring.用于飞机结构健康监测的光纤传感器
Sensors (Basel). 2015 Jun 30;15(7):15494-519. doi: 10.3390/s150715494.
6
Fiber optic sensors for structural health monitoring of air platforms.光纤传感器在航空平台结构健康监测中的应用。
Sensors (Basel). 2011;11(4):3687-705. doi: 10.3390/s110403687. Epub 2011 Mar 25.