On Delamination Crack Detection in Carbon Fiber Reinforced Polymers Using Electrical Impedance Tomography and Supervised Learning

RÉSUMÉ : L’usage des Polymères Renforcés en Fibres de Carbone (PRFC) s’est répandu grâce notam- ment à leur important rapport résistance/poids, leur résistance à la corrosion et à la fatigue, et à la flexibilité qu’ils permettent lors de la conception, par rapport aux métaux. Ils sont com- posés de plaques de matrice polymère, renforcées par des fibres de carbone, qui peuvent être empilées et orientées arbitrairement de façon à atteindre les propriétés mécaniques désirées. En revanche, du fait de leur anisotropie mécanique élevée, les PRFC possèdent des modes de rupture qui leur sont propres. En particulier, la fatigue du matériau et un impact à basse énergie peuvent se traduire par le phénomène de délaminage, soit le décollement des plaques du matériau. Comme cette dégradation ne peut pas être détectée par inspection visuelle, la fiabilité des structures en PRFC s’en trouve réduite. Il est donc essentiel de développer une méthode automatique de détection du délaminage. Plusieurs techniques non-destructives existent déjà, parmi lesquelles figurent les ultrasons, les fibres optiques, les ondes de Lamb et les courants de Eddy. Cependant, la plupart de ces méthodes requièrent l’utilisation de capteurs coûteux et ne peuvent être appliquées lors de l’opération de l’appareil, ou nécessitent l’intervention sur place de personnel qualifié. La Tomographie d’Impédance Électrique (TIE) a été envisagée pour la détection du délami- nage en raison de son faible coût et de sa capacité à fournir des informations en temps réel sur la santé du matériau. Cette méthode consiste à reconstituer une carte de la conductivité d’un matériau en injectant des courants et en mesurant les différences de potentiel résultantes. Cependant, d’importantes incertitudes demeurent dans l’estimation de la position et de la taille du délaminage. Il est donc nécessaire de développer un outil qui permette, d’une part, de déterminer les mesures qui apportent le plus d’information vis-à-vis des paramètres du délaminage, et d’autre part, de tirer de ces mesures une estimation stable de ces paramètres. Dans ce document, nous étendons les méthodes d’apprentissage supervisé au traitement des données de TIE. L’objectif général est l’optimisation de la configuration des électrodes pour l’application de la TIE à la détection de délaminage dans les PRFC. Ce projet s’articule en deux étapes. Dans un premier temps, il faut comprendre et formuler le modèle mathématique associé au problème direct ; nous reprenons le modèle d’électrode proposé par Somersalo (1992). Cela implique aussi de caractériser et paramétrer le délaminage, ainsi que d’identifier les erreurs associées au modèle et aux mesures expérimentales. Cette étape mène à la génération de données synthétiques de mesures de potentiels à l’aide d’un logiciel d’éléments finis. Lors de la deuxième phase, le problème inverse est abordé du point de vue de la classification binaire, l’inférence portant sur la présence de délaminage. Un bruit multiplicatif Gaussien est ajouté aux tensions mesurées. Plusieurs algorithmes issus de l’apprentissage automatique sont utilisés : la méthode des forêts aléatoires, la méthode des plus proches voisins, et la méthode des machines à vecteurs de support. Nous utilisons la performance de ces algorithmes en fonction des paramètres expérimentaux pour comprendre les relations existantes entre ces derniers. Nos résultats indiquent qu’une forte anisotropie ne rend pas toujours les prédictions plus difficiles; cela peut même donner lieu à de meilleures prédictions lorsque l’espacement des électrodes est très supérieur à l’épaisseur du matériau. Ceci nous pousse à recommander des recherches plus approfondies au sujet de l’influence jointe des paramètres géométriques et électriques du matériau sur le positionnement optimal des électrodes.----------ABSTRACT : Materials made of Carbon Fiber Reinforced Polymer (CFRP) are increasingly used in various engineering domains due to their high strength-to-weight ratio. However, they are subject to delamination, a mode of failure which can cause layers to separate. Since this type of failure is not visually observable, detection with non destructive testing is essential. The aim of Electrical Impedance Tomography is to reconstruct the conductivity distribution of a medium by injecting current through electrodes and measuring resulting voltages. More precisely, in the context of damage detection, the aim is to detect voltages anomalies that betray the presence of delamination. Research has already been done about statistical inference on delamination size and location. However, the inverse problem was always tackled from a regression point of view, and its study failed to provide insights about the joint influence of measurement noise and samples properties, such as geometry and electrical conductivity anisotropy, on the prediction performance. In this document, we generate synthetic data using a finite element software and borrow algorithms from the supervised learning field for the solution of the inverse problem. We study the impact of anisotropy, electrode positioning, and measurement noise on the prediction performance in a classification setting. We also show that cavities are easier to detect than delamination. Our results indicate that high anisotropy might not necessarily make inferring the presence of delamination more difficult. This leads us to recommend further research on the joint influence of geometry and anisotropy on optimal electrode spacing

Damage Detection and Critical Failure Prevention of Composites

In this chapter, critical failure prevention mechanism for composite material systems is investigated. This chapter introduces both non-destructive failure detection methods and live structural tests and its applications. The investigation begins by presenting a brief review and analysis of current non-destructive failure detection methods. The work proceeds to investigate novel live structural tests, tomography and applications of the proposed techniques

Resistance of Carbon Fibre Reinforced Composites to Quasi-static and Ballistic Perforation

The failure mechanisms, as well as the indentation and penetration resistance, of carbon fibre reinforced plastic (CFRP) cross-ply laminates were investigated under quasi-static and ballistic loading. In this thesis, the two most prominent failure modes were indirect tension and shear plugging. To characterise the indirect tension mechanism, CFRP cross-ply coupons with various matrix shear strengths were subjected to uniaxial out-of-plane compression between lubricated platens, while CFRP cross-ply beams were subjected to quasi-static indentation between a flat bottom indentor and a lubricated back support. The out-of-plane compressive strength was accurately predicted by finite element simulations and analytical models. To characterise the shear plugging mechanism, quasi-static cropping tests were performed on CFRP cross-ply beams. A beam configuration was selected to allow for ease of identifying the failure mechanisms. The investigation was extended to consider the effect of matrix shear strength on the ballistic performance of simply supported CFRP cross-ply beams impacted by a flat projectile. Laminates with high matrix shear strength failed by shear plugging, and the penetration velocity increased with decreasing matrix shear strength. As the matrix shear strength decreased further, the failure mode switched to indirect tension and subsequently the penetration velocity remained elevated, independent of the matrix shear strength. Having established that shear plugging is associated with low impact resistance, a new type of bilayer CFRP composite (comprising one low and one high matrix shear strength layer) was developed with the intent of suppressing this shear plugging mode. The ballistic penetration resistance of the bilayer beams was compared to that of the above monolithic CFRP beams using the same ballistic set-up. It was observed that the shear plugging mode in the high strength layer was suppressed when the layer was placed at the distal face; failure switched to a back face tensile mode, and the impact resistance was improved. The investigation was extended to a more realistic impact environment: CFRP cross-ply laminates in a plate configuration were perforated by a steel ball. Specimens were tested under quasi-static and ballistic loading with either a back-supported condition (simulating a thick laminate) or an edge-clamped condition. The CFRP plates failed by indirect tension when back-supported but failed by shear plugging when edge-clamped. It was found that the addition of a protective aluminium alloy layer did not alter the failure mechanism of the CFRP, but did produce a load spreading effect that increased the penetration resistance.The tuition of this author was fully sponsored by the Croucher Foundation and the Cambridge Commonwealth, European & International Trust through the Cambridge Croucher International Scholarshi

Damage sensing in CFRP composites using electrical potential techniques

This Thesis investigates the damage sensing capabilities of the electrical potential measurement technique in carbon fibre reinforced polymer composites. Impact damage was introduced in multidirectional laminates and its effect on potential distribution studied. It was found that delaminations and fibre breakages within the laminate can be detected and located by measuring potential changes on the external composite surface. The extent and size of potential changes were significantly affected by the position of the current electrodes in relation to the potential measurement probes. A numerical model was developed investigating the effect of different size delaminations, located in various positions within the lamina, on electrical potential distributions on the external ply, and a quantitative analysis of the numerical results is presented. The numerical simulations demonstrated that the measured potential changes on the external ply were in proportion to the delamination size. The numerical and experimental results were compared and the optimum configuration of current electrodes and potential probes for damage detection selected. The response of electrical potential to mechanical strain, in unidirectional and multidirectional samples was also investigated. It was found that the conductive medium, used for introducing the current, defines the piezo-resistance performance of the composite. A finite element model was developed able to predict the effect of inhomogeneous current introduction in unidirectional specimens on electrical potential and piezo-resistance. The effects of temperature and water absorption on potential measurements were also presented

Conductivity-Based Nanocomposite Structural Health Monitoring via Electrical Impedance Tomography.

Nanocomposites have incredible potential when integrated as matrices in fiber-reinforced composites for transformative conductivity-based structural health monitoring (SHM). Key to this potential is the dependence of nanocomposite conductivity on well-connected nanofiller networks. Damage that severs the network or strain that affects the connectivity will manifest as a conductivity change. These damage or strain-induced conductivity changes can then be detected and spatially located by electrical impedance tomography (EIT). The nanofiller network therefore acts as an integrated sensor network giving unprecedented insight into the mechanical state of the structure. Despite the potential of combining nanocomposite matrices with EIT, important limitations exist. EIT, for example, requires large electrode arrays that are too unwieldy to be practically implemented on in-service structures. EIT also tends to be insensitive to small, highly localized conductivity losses as is expected from common modes fiber-reinforced composite damage such as matrix cracking and delamination. Furthermore, there are gaps in the fundamental understanding of nanocomposite conductivity. This thesis advances the state of the art by addressing the aforementioned limitations of EIT for conductivity-based SHM. This is done by insightfully leveraging the unique properties of nanocomposite conductivity to circumvent EIT's limitations. First, nanocomposite conductive properties are studied. This results in fundamental contributions to the understanding of nanocomposite piezoresistivity, the influence of nanofiller alignment on transverse percolation and conductivity, and conductivity evolution due to electrical loading. Next, the potential of EIT for conductivity-based health monitoring is studied and demonstrated for damage detection in carbon nanofiber (CNF)/epoxy and glass fiber/epoxy laminates manufactured with carbon black (CB) filler and for strain detection in CNF/polyurethane (PU). Lastly, the previously developed insights into nanocomposite conductive properties and damage detection via EIT are combined to greatly enhance EIT for SHM. This is done by first exploring how the sensitivity of EIT to delamination can be enhanced through nanofiller alignment and tailoring. A method of coupling the EIT image reconstruction process with known conductivity changes such as those induced by straining piezoresistive nanocomposites is developed and presented. This approach will tremendously bolster the image quality of EIT or, synonymously, significantly abate the number of electrodes required by EIT.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111613/1/ttallman_1.pd

Step heating thermography supported by machine learning and simulation for internal defect size measurement in additive manufacturing

A methodology based on step-heating thermography for predicting the length dimension of small defects in additive manufacturing from temperature data measured on thermal images is proposed. Regression learners were applied with different configurations to predict the length of the defects. These algorithms were trained using large datasets generated with Finite Element Method simulations. The different predictive methods obtained were optimized using Bayesian inference. Using predictive methods generated and based on intrinsic performance results, knowing the material characteristics, the defect length can be predicted from single temperature data in defect and non-defect zone. Thus, the developed algorithms were implemented in a laboratory set-up carried out on ad-hoc manufactured parts of Nylon and polylactic acid which include induced defects with different sizes and thicknesses. Using the trained algorithm, the deviation of the predicted results for the defect size varied between 13% and 37% for PLA and between 13% and 36% for Nylon.This research has been funded by Ministry of Science and Innovation (Government of Spain) through the research project titled Fusion of nondestructive technologies and numerical simulation methods for the inspection and monitoring of joints in new materials and additive manufacturing processes (FaTIMA) with code RTI2018-099850-B-I00

CFRP Delamination Density Propagation Analysis by Magnetostriction Theory

While Carbon Fiber Reinforced Polymers (CFRPs) have exceptional mechanical properties concerning their overall weight, their failure profile in demanding high-stress environments raises reliability concerns in structural applications. Two crucial limiting factors in CFRP reliability are low-strain material degradation and low fracture toughness. Due to CFRP’s low strain degradation characteristics, a wide variety of interlaminar damage can be sustained without any appreciable change to the physical structure itself. This damage suffered by the energy transfer from high- stress levels appears in the form of microporosity, crazes, microcracks, and delamination in the matrix material before any severe laminate damage is observed. This research presents a novel Non- Destructive Evaluation (NDE) technique for assessing subsurface interlaminar interphacial health. A new self-sensing smart composite material is born by embedding microscopic magnetically activated sensors between CFRP ply. Magnetostrictive Carbon Fiber Reinforced Polymer (MagCFRP) is a self-sensing structural health composite material that is magnetically activated by an external magnetic field. This research merges the governing magnetoelasticity and general magnetization mechanics with analytical, experimental, and numerical results. For mode I and mode II fiber- matrix debonding, cracking, and shear delamination, there was an observed localized magnetic flux density gradient of more than 3 mT (2%) with a reversible flux of only 25% for low driving magnetic flux density (≈ 0.2 T) using the indirect magnetization stimulation method