High Temperature Shear Strength of T650-35/HFPE-II-52 Polyimide Matrix Unidirectional Composite
The shear strength of a 4-ply unidirectional composite laminate consisting of carbon fibers (T650-35) and a polyimide matrix (HFPE-II-52) was measured over a temperature range of 25 to 315°C. The tests were performed using a Iosipescu test sample, modified to provide a more uniform shear stress distribution across the gauge section and loaded with an Arcan type test fixture. The test specimen design is based on the results of an extensive finite element study. Shear strength tests were performed on dry, 50% RH and 100% RH moisture saturated samples. Results of the experiment show that shear strength decreases from approximately 120 MPa at 25°C to 60 MPa at 315°C and that moisture saturated samples have a 5–10 MPa lower shear strength than dry samples.
Linear viscoelastic properties of HFPE-II-52 polyimide
The polyimide HFPE-II-52 was developed at NASA Glenn Research Center for use as a matrix in high temperature composite materials. The unique properties of such composites stem largely from the performance of the matrix at high temperature. Thus, as part of a larger effort to study high temperature composite materials, the linear viscoelastic properties of HFPE are measured and a mathematical model of the properties is developed. In particular, storage, loss, and stress relaxation moduli were obtained from cyclic and transient loading experiments. A Prony series was fit to the relaxation modulus data. As a cross check, the fit to the relaxation modulus was converted to storage and loss moduli and compared with those measured directly. Effects of postcuring and of moisture on the properties are investigated as well. These results provide researchers with a constitutive model for HFPE-II-52 and provide some insight into the performance of HFPE matrix composites at high temperatures. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 3255–3263, 2006
Multivariate Image Texture by Multivariate Variogram for Multispectral Image Classification
Traditional image texture measure usually allows a texture description of a single band of the spectrum, characterizing the spatial variability of gray-level values within the singleband image. A problem with the approach while applied to multispectral images is that it only uses the texture information from selected bands. In this paper, we propose a new multivariate texture measure based on the multivariate variogram. The multivariate texture is computed from all bands of a multispectral image, which characterizes the multivariate spatial autocorrelation among those bands. In order to evaluate the performance of the proposed texture measure, the derived multivariate texture image is combined with the spectral data in image classification. The result is compared to classifications using spectral data alone and plus traditional texture images. A machine learning classifier based on Support Vector Machines (SVMs) is used for image classification. The experimental results demonstrate that the inclusion of multivariate texture information in multispectral image classification significantly improves the overall accuracy, with 5 to 13.5 percent of improvement, compared to the classification with spectral information alone. The results also show that when incorporated in image classification as an additional band, the multivariate texture results in high overall accuracy, which is comparable with or higher than the best results from the existing single-band and two-band texture measures, such as the variogram, cross variogram and Gray-Level Co-occurrence Matrix (GLCM) based texture. Overall, the multivariate texture provides the useful spatial information for land-cover classification, which is different from the traditional single band texture. Moreover, it avoids the band selection procedure which is prerequisite to traditional texture computation and would help to achieve high accuracy in the most classification tasks.