Hostname: page-component-848d4c4894-2xdlg Total loading time: 0 Render date: 2024-06-26T08:00:47.511Z Has data issue: false hasContentIssue false
  • English
  • Français

Structure Quantification and Gestalt of Continuous Fiber Reinforced Composite Microstructures for ICME

Published online by Cambridge University Press:  19 August 2014

Stephen Bricker ,
J.P. Simmons ,
Craig Przybyla  and
Russell Hardie
Stephen Bricker
Affiliation:
Air Force Research Lab, Wright Patterson Air Force Base, OH 45433, U.S.A. University of Dayton Research Institute, 1700 S. Patterson Blvd., Dayton, OH 45469, U.S.A.
J.P. Simmons
Affiliation:
Air Force Research Lab, Wright Patterson Air Force Base, OH 45433, U.S.A.
Craig Przybyla*
Affiliation:
Air Force Research Lab, Wright Patterson Air Force Base, OH 45433, U.S.A.
Russell Hardie
Affiliation:
University of Dayton Research Institute, 1700 S. Patterson Blvd., Dayton, OH 45469, U.S.A.
*
*Corresponding Author: craig.przybyla@us.af.mil
Get access

Abstract

Continuous fiber reinforced composites (RFC) are hierarchal and complex at multiple scales. In this work, tools are developed to automate the 3D characterization and quantification of the overall microstructure. Structure quantification enables accurate representation for material simulation and property prediction for the integrated computation materials engineering (ICME) of RFC based components. Relationships are developed to describe the key attributes of the microstructure at multiple scales including the individual fibers, tows, weave, porosity, and secondary matrix phases, which are treated as 'gestalts' of the structure. Here gestalt refers to the essence of shape or complete form of key features of the microstructure such as those of the tow architecture of the textile. Visualization tools are developed based on an artificial color scheme that allow the visual recognition of whole tows instead of just the collection of simple lines and curves representative of the fibers, which provides means whereby the gestalt of the microstructure can be visualized at the tow scale. These tools are demonstrated using a 3D dataset of the SiNC/SiC S200 ceramic matrix composite material (CMC) obtained via automated serial sectioning. Methods are then demonstrated to generate microstructure models representative of the characterized material for finite element analyses (FEA).

Keywords

compositedefectsoptical metallography
Type
Articles
Information
MRS Online Proceedings Library (OPL) , Volume 1709: Symposium WW – Materials by Design—Merging Advanced In-Situ Characterization with Predictive Simulation , 2014 , mrss14-1709-ww09-07
Copyright
Copyright © Materials Research Society 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Comer, M. L. and Delp, E. J., “EM/MPM algorithm for segmentation of textured images: Analysis and further experimental results, “IEEE Transactions on Image Processing, vol. 9, pp. 1731–1744, 2000.CrossRefGoogle ScholarPubMed
Simmons, J. P., Chuang, P., Comer, M., Spowart, J. E., Uchic, M. D., and De Graef, M., “Application and further development of advanced image processing algorithms for automated analysis of serial section image data,” Modelling and Simulation in Materials Science and Engineering, vol. 17, pp. 1–22, 2009.CrossRefGoogle Scholar
Jackson, M., “EM/MPM,” Dayton, OH: BlueQuartz Software (http://www.bluequartz.net/projects/EIM_Segmentation/), 2014 Google Scholar
Przybyla, C. P., Godar, T., Bricker, S., Simmons, J. P., Jackson, M., Zwada, L., and Pearce, J., “Statistical characterization of SiC/SiC ceramic matrix composites at the filament scale with Bayesian segmentation, hough transform feature extraction, and pair correlation statistics,” International SAMPE Technical Conference, p. 859–878, 2013 Google Scholar
Spowart, J. E., Mullens, H. M., and Puchala, B. T., “Collecting and analyzing microstructures in three dimensions: a fully automated approach, “Journal of Materials, vol. 55, pp. 35–37, 2003 Google Scholar
Spowart, J. E., “Automated serial sectioning for 3D analysis of microstructures,” Scripta Materialia, vol. 55, pp. 5–10, 2006.CrossRefGoogle Scholar
Carlo Alberto, Nannetti, et al. . “Manufacturing Sic-Fiber-Reinforced Sic Matrix Composites By Improved CVI/Slurry Infiltration/Polymer Impregnation And Pyrolysis.” Journal Of The American Ceramic Society 87.7 (2004): 1205–1209.Google Scholar
Coon, D. N., “Monte Carlo simulation of creep failure in a 0°/90° plain weave SiC/SiC composite lamina,” Journal of Materials Science 38 (2003): 3121–3129 CrossRefGoogle Scholar
Pina, de Omena, Rafaela, Samanta, Pardini, Luiz Claudio, and Pagotto Yoshida, Inez Valéria. “Carbon Fiber/Ceramic Matrix Composites: Processing, Oxidation And Mechanical Properties.” Journal Of Materials Science 42.12 (2007): 4245–4253.CrossRefGoogle Scholar
Baudry, Pierre, Dourges, Marie-Anne, and Pailler, René. “Influence Of Carbon Fibre Treatment And Filler Addition On Porosity And Structure Change Of Carbon Fibres Reinforced Phenolic Matrix Composites During Carbonization.” Journal Of Materials Science 44.14 (2009): 3643–3651 CrossRefGoogle Scholar
Szeliski, R., “Image Alignment and Stitching,” in The Handbook of Mathematical Models in Computer Vision, Paragios, N., Chen, Y., and Faugeras, O., Eds. New York: Springer, 2005.Google Scholar
Demptster, A.P., Laird, N.M., and Rubin, D.B., “Maximum Likelihood from Incomplete Data via the EM Algorithm,” Journal of the Royal Statistical Society, B, vol. 39(1), pp. 1–38, (1977).Google Scholar
Haimes, R., Kenwright, D., “On the Velocity Gradient Tensor and Fluid Feature Extraction,” AIAA paper, 3288 (1999).Google Scholar