Application of DIC Technology in Quality Monitoring of High‑Performance Composite Structural Components: From Non‑Contact Full‑Field Measurement to Intelligent Damage Early Warning
Release time:
2026-02-25 19:22
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High‑performance composite materials are rapidly replacing traditional metal materials on a large scale in the aerospace, wind power, and automotive industries, thanks to their high specific strength, high specific stiffness, and outstanding corrosion resistance. According to Grand View Research, the global non‑destructive testing (NDT) market had reached… in 2024. US$21.28 billion It is expected to increase to US$45.97 billion by 2033. [1] Meanwhile, the global composite materials testing market reached US$1.8 billion in 2023 and continues to expand at a CAGR of 6.3%. [2]
However, the inherent anisotropy and complex damage mechanisms of composite materials pose significant challenges to traditional single-point inspection methods. In a 58‑page review published in MDPI’s NDT journal, Oliveira et al. (2025) systematically summarized the typical defects encountered in aerospace composites—delamination, matrix cracking, fiber breakage, and fiber pullout—while emphasizing the urgent need for innovation in nondestructive testing technologies. [3] Chen (2025) further pointed out at the 45th Risoe International Conference on Materials Science that wind turbine blade lengths have now exceeded… 150 meters The cost of physical certification testing has soared to the millions of Danish kroner. [4] Against this backdrop, digital image correlation (DIC) technology, with its unique advantages of being non‑contact, enabling full‑field measurements, and achieving subpixel accuracy, is emerging as the core technological approach for quality monitoring of high‑performance composite structural components.
DIC technology achieves full-field, precise measurement of in-plane and out-of-plane displacement and strain by analyzing the grayscale changes in speckle images of a material’s surface before and after deformation. After subpixel interpolation optimization, it can achieve… Subpixel level Measurement accuracy. [3] Compared with traditional strain gauges, DIC does not require mechanical connections and is particularly well suited for full-field deformation monitoring of thin-walled composite structures and large components.
In their study published in the European Journal of Wood and Wood Products in 2025, Melinda et al. used the open-source DIC software Ncorr to conduct bending performance tests on CFRP‑reinforced laminated single‑ply beams, with an elastic stiffness comparison ratio of… 0.97–1.05 , the displacement comparison ratio is 0.93–1.07 , fully validating the high reliability of DIC as a non‑contact measurement method. [5]
In engineering practice, EikoTwin DIC Optical Strain Measurement System By directly embedding DIC measurements onto the finite element mesh and leveraging geometric prior knowledge to perform camera calibration, seamless integration between experimental data and simulation models is achieved. This model-driven approach not only eliminates the limitations associated with traditional calibration targets but also extends the measurement range to the edges of the specimen, providing an efficient solution for full-field quality assessment of composite structural components.
3.1 Verification of the Mechanical Properties of Composite Materials
Nugraha et al. (2025) reported in Scientific Reports that the combined use of Digital Image Correlation (DIC) and Finite Element Analysis (FEA) for characterizing light‑cured 3D‑printed multilayer glass fiber–reinforced composites resulted in a ultimate tensile strength (UTS) that, compared to the unmodified material,… From 20.1 MPa to 59.3 MPa after four layers of glass fiber reinforcement. , increasing by nearly three times, with DIC validation results showing a high degree of agreement with experimental data. [6] Zhong et al. (2024) used DIC technology to investigate the fatigue damage behavior of off-axis CFRP composites and determined… 7.8% It identifies the critical failure threshold for compressive strain and reveals the characteristic pattern of tensile cracks propagating from the edge toward the center, along with compressive damage exhibiting an S‑shaped fiber buckling distribution. [7]
3.2 Early Crack Warning and Damage Identification
In their study published in Scientific Reports in 2025, Wu et al. used DIC analysis to investigate the structural behavior of wire‑mesh–reinforced cement composite panels and found that increasing the number of reinforcement layers can enhance the ultimate load-carrying capacity. 18%–76% 。 [8]
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DIC analysis shows that transverse strain is the most sensitive parameter for early crack detection—this finding provides a quantifiable early warning basis for online quality monitoring of composite structural components, marking an important leap in DIC technology from passive inspection to proactive early warning.
— Wu Z et al. Scientific Reports, 2025
3.3 Monitoring of Large Structural Component Manufacturing Processes
At the 2026 EikoSim Echo Day Symposium, Paul’s team from the École des Mines de Saint-Étienne in France presented an innovative application: utilizing… EikoTwin DIC Optical Strain Measurement System Real‑time monitoring of thickness variations during the composite material infusion molding process with an accuracy of ±20 μm successfully revealed the diametrically opposite wetting behaviors of flax fibers (swelling) and carbon fibers (compaction), while cleverly leveraging DIC residuals to track the hidden flow front. [11] During the qualification testing of the Ariane 6 rocket’s satellite dispenser, ArianeGroup employed EikoTwin DIC for full-field measurements with 8 cameras, saving… 4 days On-site preparation time, potentially achievable approximately 25% Cost reduction. [12]
Pathan et al. (2025) published a review in the International Journal of Applied Mechanics and Engineering that highlights the tremendous potential of integrating Digital Image Correlation (DIC) with artificial intelligence: By combining DIC with Lamb wave analysis and incorporating intelligent methods such as Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANNs), and Genetic Algorithms, automatic identification of composite material damage can be achieved without requiring prior knowledge of material properties. [9] Sadeghian et al. (2026) further summarized in the MDPI journal Materials that the coupling of DIC with machine learning has significantly enhanced the accuracy, speed, and reliability of damage identification in engineering materials. [10]
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Statistics from Tijani et al. show that among 128 DIC-related studies, 30 were published before 2015 (23.4%), 48 were published between 2015 and 2020 (37.5%), and 50 were published between 2020 and 2025 (39.1%), indicating a significant acceleration in growth. The study proposes a future roadmap for the deep integration of DIC with the Internet of Things (IoT), drone imaging, machine learning, and digital twins. [18]
— Tijani I A et al. Archives of Computational Methods in Engineering, 2025
Romanowicz et al. (2024) further validated the effectiveness of DIC in hybrid steel/composite structures in their study published in Materials: the average relative error between the coating and finite element calculations was less than… 15% , the steel core is less than 14%. [13] These studies indicate that the synergy between DIC and AI is driving a paradigm shift in composite material quality monitoring, moving it from “manual interpretation” to “intelligent diagnosis.” Notably, EikoTwin DIC Optical Strain Measurement System The brand‑new Python API and batch processing mode introduced in version 2026.1 provide a convenient interface for the automated integration of DIC data with machine learning algorithms. [11]
According to WiseGuy Reports, the global DIC system market was approximately… in 2024. US$436.7 million It is projected to approach US$1 billion by 2035, with a compound annual growth rate of approximately 7.8%. [14] The carbon fiber composite materials market is growing in tandem, reaching a size of US$24.37 billion in 2024, with the aerospace and defense sectors accounting for 26.6% of the total. [15]
Looking ahead, with DIC technology at its core, and by integrating various non‑destructive testing methods such as ultrasound, thermal imaging, and acoustic emission, while leveraging artificial intelligence to achieve automated damage detection and early warning, the development of an intelligent quality monitoring system that covers the entire lifecycle of composite structural components will become a crucial technological foundation for ensuring structural safety in the aerospace, wind power, and high‑end manufacturing sectors. As technical standards like ASTM E2208 continue to be refined… [16] The release of the 2nd edition of the iDICs “DIC Good Practice Guidelines” [17] The standardized application of DIC technology is being accelerated. With… EikoTwin DIC Optical Strain Measurement System As a professional platform for representatives, it is leveraging model-driven measurement, intelligent data processing, and digital twin integration to usher in a new era of precise and intelligent quality monitoring for high‑performance composite structural components.
References
[1] Grand View Research. Non-Destructive Testing Equipment & Services Market Size Report, 2024. Market Size: USD 21.28 billion (2024), CAGR of 9.2%.
[2] Allied Market Research. Composites Testing Market Report, 2024. Market Size: USD 1.8 billion (2023), CAGR of 6.3%.
[3] Oliveira T L L, Hadded M, Mimouni S, et al. The Role of Non-Destructive Testing of Composite Materials for Aerospace Applications. NDT , 2025, 3, 3. (MDPI)
[4] Chen X. Virtual testing for failure prediction of large-scale composite wind turbine blades. IOP Conference Series: Materials Science and Engineering , 2025, 1338: 012002.
[5] Melinda A P, et al. Open-Source DIC Evaluation of Bending Performance of CFRP‑Strengthened Laminated Veneer Lumber Timber Beams. European Journal of Wood and Wood Products , 2025, 83: 104.
[6] Nugraha A D, Setiawan F, Khotami M Z, et al. Experimental, Numerical, and DIC Analysis of High-Performance VPP Composites with Multilayer Glass Fiber Reinforcement. Scientific Reports , 2025, 15: 41081.
[7] Zhong Z, Wang F, Kong F, et al. Study of Fatigue Damage Behavior in Off-Axis CFRP Composites Using Digital Image Correlation Technology. Heliyon , 2024, 10(3): e25577. doi: 10.1016/j.heliyon.2024.e25577
[8] Wu Z, Madadi A, Yu T. Structural Analysis of Ferrocement Composite Panels with Expanded Perlite–Based Mortar. Scientific Reports , 2025.
[9] Pathan F U, Patil M M, Baviskar P R, et al. An Exploration of Vibration-Based Damage Detection Techniques for Composite Materials. Int. J. of Applied Mechanics and Engineering , 2025, 30(3): 97–113.
[10] Sadeghian M, Palevicius A, Sablinskas J, et al. From Pixels to Predictions: Integrating Machine Learning and Digital Image Correlation for Damage Identification in Engineering Materials. Materials (MDPI), 2026, 19(1): 77. doi: 10.3390/ma19010077
[11] EikoSim. EikoTwin DIC 2026.1 Release & Echo Day 2026 Workshop. Grouting Monitoring Case Study from the École des Mines de Saint-Étienne. eikosim.com
[12] EikoSim. ArianeGroup Case Study — MUTATION (DGA RAPID Project), Qualification of the Ariane 6 Galileo Dispenser. eikosim.com
[13] Romanowicz P J, Szybinski B, Wygoda M. Assessment of the Effectiveness and Quality of Digital Image Correlation in Determining Surface Strains of Hybrid Steel/Composite Structures. Materials , 2024, 17(14): 3561. doi: 10.3390/ma17143561
[14] WiseGuy Reports. Digital Image Correlation (DIC) System Market Report. Market Size: USD 436.7 million (2024), CAGR 7.8%.
[15] Grand View Research. Carbon Fiber Composites Market Report, 2024. Market Size: USD 24.37 billion (2024), with the aerospace sector accounting for 26.6%.
[16] ASTM E2208-02(2018)e1. Standard Guide for Evaluating Non-Contacting Optical Strain Measurement Systems. ASTM International.
[17] International Digital Image Correlation Society (iDICs). A Good Practices Guide for Digital Image Correlation, 2nd Edition. idics.org
[18] Tijani I A, Wakjira T G, Alam M S, et al. Digital Image Correlation (DIC) for Structural Health Monitoring of Bridge Systems: A State-of-the-Art Review with Future Research Directions. Archives of Computational Methods in Engineering , 2025. doi: 10.1007/s11831-025-10459-6
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