The influence of toughening-particles in CFRPs on low velocity impact damage resistance performance

The role of particle-toughening for increasing impact damage resistance in carbon fibre reinforced polymer (CFRP) composites was investigated. Five carbon fibre reinforced systems consisting of four particle-toughened matrices and one system containing no toughening particles were subjected to low velocity impacts ranging from 25 J to 50 J to establish the impact damage resistance of each material system. Synchrotron radiation computed tomography (SRCT) enabled a novel approach for damage assessment and quantification. Toughening mechanisms were detected in the particle-toughened systems consisting of particle–resin debonding, crack-deflection and crack-bridging. Quantification of the bridging behaviour, increase in crack path length and roughness was undertaken. Out of the three toughening mechanisms measured, particle systems exhibited a larger extent of bridging suggesting a significant contribution of this toughening mechanism compared to the system with no particle

Three-Dimensional Imaging and Numerical Reconstruction of Graphite/Epoxy Composite Microstructure Based on Ultra-High Resolution X-Ray Computed Tomography

A combined experimental and computational study aimed at high-resolution 3D imaging, visualization, and numerical reconstruction of fiber-reinforced polymer microstructures at the fiber length scale is presented. To this end, a sample of graphite/epoxy composite was imaged at sub-micron resolution using a 3D X-ray computed tomography microscope. Next, a novel segmentation algorithm was developed, based on concepts adopted from computer vision and multi-target tracking, to detect and estimate, with high accuracy, the position of individual fibers in a volume of the imaged composite. In the current implementation, the segmentation algorithm was based on Global Nearest Neighbor data-association architecture, a Kalman filter estimator, and several novel algorithms for virtualfiber stitching, smoothing, and overlap removal. The segmentation algorithm was used on a sub-volume of the imaged composite, detecting 508 individual fibers. The segmentation data were qualitatively compared to the tomographic data, demonstrating high accuracy of the numerical reconstruction. Moreover, the data were used to quantify a) the relative distribution of individual-fiber cross sections within the imaged sub-volume, and b) the local fiber misorientation relative to the global fiber axis. Finally, the segmentation data were converted using commercially available finite element (FE) software to generate a detailed FE mesh of the composite volume. The methodology described herein demonstrates the feasibility of realizing an FE-based, virtual-testing framework for graphite/fiber composites at the constituent level

Thermographic non-destructive evaluation for natural fiber-reinforced composite laminates

Natural fibers, including mineral and plant fibers, are increasingly used for polymer composite materials due to their low environmental impact. In this paper, thermographic non-destructive inspection techniques were used to evaluate and characterize basalt, jute/hemp and bagasse fibers composite panels. Different defects were analyzed in terms of impact damage, delaminations and resin abnormalities. Of particular interest, homogeneous particleboards of sugarcane bagasse, a new plant fiber material, were studied. Pulsed phase thermography and principal component thermography were used as the post-processing methods. In addition, ultrasonic C-scan and continuous wave terahertz imaging were also carried out on the mineral fiber laminates for comparative purposes. Finally, an analytical comparison of different methods was give

Non-Destructive Evaluation for Composite Material

The Nondestructive Evaluation Sciences Branch (NESB) at the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) has conducted impact damage experiments over the past few years with the goal of understanding structural defects in composite materials. The Data Science Team within the NASA LaRC Office of the Chief Information Officer (OCIO) has been working with the Non-Destructive Evaluation (NDE) subject matter experts (SMEs), Dr. Cheryl Rose, from the Structural Mechanics & Concepts Branch and Dr. William Winfree, from the Research Directorate, to develop computer vision solutions using digital image processing and machine learning techniques that can help identify the structural defects in composite materials. The research focused on developing an autonomous Non-Destructive Evaluation system which detects, identifies, and characterizes crack and delamination in composite materials from computed tomography (CT scans) images. The identification and visualization of cracking and delamination will allow researchers to use volumetric models to better understand the propagation of damage in materials, leading to design optimizations that will prevent catastrophic failure

Segmentation of X-ray CT and Ultrasonic Scans of Impacted Composite Structures for Damage State Interpretation and Model Generation

Composites are frequently used in aerospace structural applications due to their high strength to weight performance, but due to their layered structure they are vulnerable to transverse impacts. Impact damage in composite laminates often consists of highly interactive damage modes composed of delamination, matrix cracking, and fiber breakage. In order to ensure the safety of composite structures, a variety of non-destructive evaluation (NDE) techniques are used to characterize impact damage. However, procedures for utilizing NDE to create and validate models of residual strength after impact are not yet established due to either limitations in the characterization of impact damage, as in the case of Ultrasonic pulse-echo scanning (UT), or due to the complexity of interpretation of the NDE technique, as in the case of X-ray computed tomography (CT). Improved quantification of damage from CT and UT characterization may lead to improved predictive capabilities for the prediction of structural performance after an impact event.This work presents a novel automatic damage segmentation procedure for CT scans of impacted composites that converts the complex 3D dataset into simplified damage visualizations and 2D damage maps for each composite layer. The results of this procedure were utilized to create and validate a modeling procedure to improve UT characterization of impact damage, and to validate and generate finite element models of impact damage and residual strength performance. The generated residual strength models were created with varying levels of damage modeling fidelity and it was found that the level of damage modeling needed for accurate failure prediction depends greatly on the structural geometry and the presence of major damage features. This NDE and modeling effort was supported by a series of impact and residual strength experiments for flat and stringer stiffened composite panels. The developed techniques proved capable of characterizing impact damage in a variety of structural configurations and establishing models that incorporate this damage at different levels of complexity