Engineering topics
Publications and source records attributed to Tate, Lanetra C..
A new composite system formulated using commodity grade and engineered grade polymers. The composites can be fabricated into fibers, molded, or otherwise processed into useable articles. Use of this technology reduces the thermal conductivity and peak heat releases rates of the base polymer between 20%-50% while maintaining or enhancing the mechanical properties..
An in-situ system for detecting damage in an electrically conductive wire. The system includes a substrate at least partially covered by a layer of electrically conductive material forming a continuous or non-continuous electrically conductive layer connected to an electrical signal generator adapted to delivering electrical signals to the electrically conductive layer. Data is received and processed to identify damage to the substrate or electrically conductive layer. The electrically conductive material may include metalized carbon fibers, a thin metal coating, a conductive polymer, carbon nanotubes, metal nanoparticles or a combination thereof.
Accomplishing the best-performing composite matrix (resin) requires that not only the processing method but also the cure cycle generate low-void-content structures. If voids are present, the performance of the composite matrix will be significantly reduced. This is usually noticed by significant reductions in matrix-dominated properties, such as compression and shear strength. Voids in composite materials are areas that are absent of the composite components: matrix and fibers. The characteristics of the voids and their accurate estimation are critical to determine for high performance composite structures. One widely used method of performing void analysis on a composite structure sample is acquiring optical micrographs or Scanning Electron Microscope (SEM) images of lateral sides of the sample and retrieving the void areas within the micrographs/images using an image analysis technique. Segmentation for the retrieval and subsequent computation of void areas within the micrographs/images is challenging as the gray-scaled values of the void areas are close to the gray-scaled values of the matrix leading to the need of manually performing the segmentation based on the histogram of the micrographs/images to retrieve the void areas. The use of an algorithm developed by NASA and based on Fuzzy Reasoning (FR) proved to overcome the difficulty of suitably differentiate void and matrix image areas with similar gray-scaled values leading not only to a more accurate estimation of void areas on composite matrix micrographs but also to a faster void analysis process as the algorithm is fully autonomous.