Integrity of Carbon-Fibre Epoxy Composites through a Nanomechanical Mapping Protocol towards Quality Assurance

Koumoulos, Elias P.; Charitidis, Costas A.

doi:10.3390/fib6040078

Cited by 6 publications

(4 citation statements)

References 26 publications

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“…Considering the half-space elastic deformation theory, H and E values can be calculated from the experimental data (load displacement curves) using the Oliver-Pharr (O&P) model [6,9]. The derived expressions for calculating E extracted from indentation measurements are based on Sneddon's elastic contact theory:…”

Section: The Dataset: Experimental Details and Methodologymentioning

confidence: 99%

“…Using the theory of contact mechanics and assuming an elastic behavior, the hardness (referring to the contact pressure) and the elastic modulus of the material can then be calculated from the initial part of the unloading curve [5]. Furthermore, information on elastic recovery, plastic deformation [6], fracture [7], sensitivity of strain-rate and strain-hardening, the effect of residual stresses [8], and size [9] in plasticity locally are is provided, making the nanoindentation technique a rich information source of the mechanics of a probed material with a growing range of applications. An important and recent field of application of the nanoindenter is the assessment of in-situ properties of fibrous composite constituents [10,11].…”

Section: Introductionmentioning

confidence: 99%

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Constituents Phase Reconstruction through Applied Machine Learning in Nanoindentation Mapping Data of Mortar Surface

Koumoulos

Paraskevoudis²,

Charitidis

2019

J. Compos. Sci.

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In the present study, data generated from nanoindentation were used in order to reconstruct the surface constituent phases of mortar grids through machine learning algorithms. Specifically, the K-Means algorithm (unsupervised learning) was applied to two 49 measurement (7 × 7) datasets with information about the modulus (E) and hardness (H) in order to discover the underlying structure of the data. The resulting clusters from K-Means were then evaluated and values range assigned so as to signify the various constituent phases of the mortar. Furthermore, another dataset from nanoindentation containing information about E, H, and the surface colour of the measured area (obtained from an optical microscope) was used as the training set in order to develop a random forests model (supervised learning), which predicts the surface colour from the E and H values. Colour predictions on the two 7 × 7 mortar grids were made and then possible correlations between the clusters, signifying constituent phases, and the predicted colours were examined. The groupings of data in the clusters (phases) corresponded to a unique surface colour. Finally, the constituent phases of the mortar grids were reconstructed in contour plots by assigning the corresponding cluster of the K-Means algorithm to each measurement (position in the grid).

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Section: The Dataset: Experimental Details and Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Constituents Phase Reconstruction through Applied Machine Learning in Nanoindentation Mapping Data of Mortar Surface

Koumoulos

Paraskevoudis²,

Charitidis

2019

J. Compos. Sci.

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“…Load ranged from 1 µN to 30 mN, while load and displacement resolution were 1 nN and 0.04 nm, respectively. A trapezoidal protocol was adopted [37], with displacement control set at 200 nm to comply with mapping requirements according to ISO 14577-1 standard [38] and ASTM E2546-15, and holding time of 3 s was applied to minimize creep contribution within the unloading curve. E r and H were measured by applying the Oliver-Pharr model within the region of 75-95% of applied load for the unload curve [9,31,39].…”

Section: Nanoindentation Testingmentioning

confidence: 99%

Carbon Fiber Reinforced Composites: Study of Modification Effect on Weathering-Induced Ageing via Nanoindentation and Deep Learning

Konstantopoulos

Semitekolos

Koumoulos³

et al. 2021

Nanomaterials

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The exposure of carbon-fiber-reinforced polymers (CFRPs) to open-field conditions was investigated. Establishment of structure–property relations with nanoindentation enabled the observation of modification effects on carbon-fiber interfaces, and impact resistance. Mapping of nanomechanical properties was performed using expectation-maximization optimization of Gaussian fitting for each CFRPs microstructure (matrix, interface, carbon fiber), while Weibull analysis connected the weathering effect to the statistically representative behavior of the produced composites. Plasma modification demonstrated reduced defect density and improved nanomechanical properties after weathering. Artificial intelligence for anomaly detection provided insights on condition monitoring of CFRPs. Deep-learning neural networks with three hidden layers were used to model the resistance to plastic deformation based on nanoindentation parameters. This study provides new assessment insights in composite engineering and quality assurance, especially during exposure under service conditions.

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“…In the recent past, statistical aspects of the strength analysis have been in the focus using a sensitivity analysis to reduce experimental uncertainties. Fracture tests have to be combined with experimental technologies to study the damage and failure phenomenology like micrograph analysis, crack density measurements, acoustic emission, ultrasonics, thermography, nanoindentation mapping, computed tomography, or even in situ computer tomography [55][56][57].…”

Section: Advanced Characterization Techniques Including Standardizatmentioning

confidence: 99%

Research and Development in Carbon Fibers and Advanced High-Performance Composites Supply Chain in Europe: A Roadmap for Challenges and the Industrial Uptake

et al. 2019

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Structural materials, typically based on metal, have been gradually substituted by high-performance composites based on carbon fibers, embedded in a polymer matrix, due to their potential to provide lighter, stronger, and more durable solutions. In the last decades, the composites industry has witnessed a sustained growth, especially due to diffusion of these materials in key markets, such as the construction, wind energy, aeronautics, and automobile sectors. Carbon fibers are, by far, the most widely used fiber in high-performance applications. This important technology has huge potential for the future and it is expected to have a significant impact in the manufacturing industry within Europe and, therefore, coordination and strategic roadmapping actions are required. To lead a further drive to develop the potential of composites into new sectors, it is important to establish strategic roadmapping actions, including the development of business and cost models, supply chains implementation, and development, suitability for high volume markets and addressing technology management. Europe already has a vibrant and competitive composites industry that is supported by several research centers, but for its positioning in a forefront position in this technology, further challenges are still required to be addressed.

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Integrity of Carbon-Fibre Epoxy Composites through a Nanomechanical Mapping Protocol towards Quality Assurance

Cited by 6 publications

References 26 publications

Constituents Phase Reconstruction through Applied Machine Learning in Nanoindentation Mapping Data of Mortar Surface

Constituents Phase Reconstruction through Applied Machine Learning in Nanoindentation Mapping Data of Mortar Surface

Carbon Fiber Reinforced Composites: Study of Modification Effect on Weathering-Induced Ageing via Nanoindentation and Deep Learning

Research and Development in Carbon Fibers and Advanced High-Performance Composites Supply Chain in Europe: A Roadmap for Challenges and the Industrial Uptake

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