Resistance Curves in the Tensile and Compressive Longitudinal Failure of Composites

This paper presents a new methodology to measure the crack resistance curves associated with fiber-dominated failure modes in polymer-matrix composites. These crack resistance curves not only characterize the fracture toughness of the material, but are also the basis for the identification of the parameters of the softening laws used in the analytical and numerical simulation of fracture in composite materials. The method proposed is based on the identification of the crack tip location by the use of Digital Image Correlation and the calculation of the J-integral directly from the test data using a simple expression derived for cross-ply composite laminates. It is shown that the results obtained using the proposed methodology yield crack resistance curves similar to those obtained using FEM-based methods in compact tension carbon-epoxy specimens. However, it is also shown that the Digital Image Correlation based technique can be used to extract crack resistance curves in compact compression tests for which FEM-based techniques are inadequate

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

Micromechanical finite element modeling of long fiber reinforced thermoplastics

Long fiber reinforced thermoplastics are promising candidates for the mass production of lightweight components. In order to predict their microstructure-dependent properties, a novel procedure for the generation of a representative volume element is developed. The approach mimics the pressing process during the fabrication of the material by compression molding. The model is experimentally validated with respect to different mechanical properties, including elasticity, creep and damage

Synchrotron-based visualization and segmentation of elastic lamellae in the mouse carotid artery during quasi-static pressure inflation

This dataset contains images that were obtained during quasi-static pressure inflation of mouse carotid arteries. Images were taken with phase propagation imaging at the X02DA TOMCAT beamline of the Swiss Light Source synchrotron at the Paul Scherrer Institute in Villigen, Switzerland. Scans of n=12 left carotid arteries (n-6 Apoe-deficient mice, n=6 wild-type mice, all on a C57Bl6J background) were taken at pressure levels of 0, 10, 20, 30, 40, 50, 70, 90 and 120 mmHg. For analysis we selected 75 images from the center of each stack (starting at the center of the stack, and skipping 2 of every three images in both cranial and caudal axial directions) for each sample and for each pressure level, resulting in a total of 75 x 12 x 9 = 8100 analyzed images from 108 different scans. Segmentation, 3D visualization and geometric analysis is presented in the corresponding manuscript. Files are uploaded in 16bit .tif format and are named: mouseid_pressurelevel_stacknumber, with mouseid consisting of either Apoe (Apoe-deficient) or Bl (wild-type) and the mouse number, pressurelevel varies from P0 to P120 and stacknumber indicates which image from the stack has been uploaded

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

Development of a Data Fusion-Based Multi-Sensor System for Hybrid Sheet Molding Compound

In den letzten Jahren ist die Produktion von faserverstärkten Kunststoffen stetig gestiegen. Ein Teil davon ist das glasfaser-verstärkte Sheet Molding Compound (SMC), welches sich durch seine günstigen Herstellkosten und einfache Verarbeitung auszeichnet. Allerdings weist dieses Material schlechte mechanische Eigenschaften auf, welche eine Anwendung für Strukturbauteile verhindert. Um diesem Nachteil entgegen zu wirken, wird das diskontinuierliche Glasfaser-SMC lokal mit kontinuierlichem Carbonfaser-SMC verstärkt. Dadurch können die Vorteile des günstigen und leicht zu verarbeitenden Glasfaser-SMC mit den sehr guten mechanischen Eigenschaften von Carbonfaser-SMC in Faserrichtung kombiniert werden. Die Kombination dieser beiden Werkstoffe kann bereits in einem frühen Produktionsschritt zu einer Vielzahl an möglichen Defekten wie beispielsweise Delamination, Falten oder Winkelabweichungen führen. Um keine weiteren wertschöpfenden Maßnahmen an defekten Bauteilen durchzuführen, muss die Qualitätssicherung bereits in einem frühen Prozessstadium durchgeführt werden. Die zu entdeckenden Fehler werden in außen- und innenliegende Defekte unterteilt. Da kein System verfügbar ist, um alle relevanten Defekte zu detektieren, wird pro Defektklasse ein Messsystem benötigt. Zudem erstreckt sich der Anwendungsbereich neben dem Halbzeug auch auf das ausgehärtete Bauteil. Das Laserlichtschnittsystem und die aktive Thermografie, in Form der Puls-Phasen-Thermografie, haben sich als geeignet erwiesen. Beide Systeme werden zunächst einzeln untersucht und für den vorliegenden Anwendungsfall angepasst. Dabei ist es möglich die Puls-Phasen-Thermografie methodisch zu einer Tiefenauswertung weiterzuentwickeln. Des Weiteren werden Fehler nicht nur detektiert, sondern auch definiert. Anschließen werden die beiden Systeme in einem Multisensorik-System zusammengeführt. Mit Hilfe der Datenfusion sind eine Auswertung von außen- und innenliegenden Defekten, sowie die Ermittlung von geometrischen Zusammenhängen zwischen einzelnen Defekten möglich. Durch den Aufbau eines Schichtmodells wird zusätzlich eine benutzerfreundliche Auswertung ermöglicht, welche dem Anwender schnell einzelne Schichten aufzeigen kann. Mit der Ermittlung der Messunsicherheit des Multisensorik-Systems wird die Güte aufgezeigt

Monitoring steel fibre orientation in self-compacting cementitious composite slabs during pouring with dynamic X-ray radiography

This paper presents a new technique based on dynamic X-ray radiography that can be used to assess fibre orientation during pouring of steel fibre reinforced cementitious composites. Synthetic examples were used to assess the suitability and robustness of the technique, which was shown to provide reliable measurements of fibre orientation even when the signal-to-noise ratio is relatively high. A study was then carried out on the effect of formwork aspect ratio, time/duration of pouring, and rebar placement on the fibre orientation while pouring self-compacting cementitious composite slabs. Results demonstrated the ability of the technique in monitoring the movements of fibres while pouring, and the strong effect of the flow in inducing preferential fibre alignment within the slabs. Fibre orientation was found to vary progressively over time and could take about half of the duration of pouring to fully stabilise.University of Sydney - Sydney Research Accelerator (SOAR) programm

GPU-accelerated ray-casting for 3D fiber orientation analysis

Orientation analysis of fibers is widely applied in the fields of medical, material and life sciences. The orientation information allows predicting properties and behavior of materials to validate and guide a fabrication process of materials with controlled fiber orientation. Meanwhile, development of detector systems for high-resolution non-invasive 3D imaging techniques led to a significant increase in the amount of generated data per a sample up to dozens of gigabytes. Though plenty of 3D orientation estimation algorithms were developed in recent years, neither of them can process large datasets in a reasonable amount of time. This fact complicates the further analysis and makes impossible fast feedback to adjust fabrication parameters. In this work, we present a new method for quantifying the 3D orientation of fibers. The GPU implementation of the proposed method surpasses another popular method for 3D orientation analysis regarding accuracy and speed. The validation of both methods was performed on a synthetic dataset with varying parameters of fibers. Moreover, the proposed method was applied to perform orientation analysis of scaffolds with different fibrous micro-architecture studied with the synchrotron μCT imaging setup. Each acquired dataset of size 600x600x450 voxels was analyzed in less 2 minutes using standard PC equipped with a single GPU