PROBABILISTIC MICROMECHANICS MACROMECHANICS FOR CERAMIC-MATRIX COMPOSITES/

Citation
Pln. Murthy et al., PROBABILISTIC MICROMECHANICS MACROMECHANICS FOR CERAMIC-MATRIX COMPOSITES/, Journal of composite materials, 32(7), 1998, pp. 679-699
Citations number
9
Categorie Soggetti
Materials Sciences, Composites
ISSN journal
00219983
Volume
32
Issue
7
Year of publication
1998
Pages
679 - 699
Database
ISI
SICI code
0021-9983(1998)32:7<679:PMMFCC>2.0.ZU;2-9
Abstract
Ceramic matrix: composites (CMCs) are known to display a considerable amount of scatter in their properties due to variations involved in fi ber/matrix properties, interphase properties, interphase bonding, amou nt of matrix voids, and many geometric or fabrication process related parameters such as ply thickness and ply orientation. This paper summa rizes the preliminary studies related to the incorporation of formal p robabilistic descriptions of the material behavior and fabrication rel ated parameters into micromechanics and macromechanics for CMCs. This process involves a synergistic coupling of two existing methodologies: namely ceramic matrix composite micro-and macromechanics analysis, an d a fast probability integration (FPI) technique to obtain probabilist ic composite behavior/response. Preliminary results in the form of cum ulative probability distributions and information on the response prob ability sensitivities to primitive variables for a unidirectional SiC/ RBSN ceramic matrix composite are presented. The cumulative distributi on functions are computed for composite moduli, thermal expansion coef ficients, thermal conductivities and longitudinal tensile strength at room temperature. Variations in the constituent properties that direct ly affect the above mentioned composite properties are accounted for v ia assumed probabilistic distributions. Collectively the results show that the present technique provides valuable information on the compos ite properties and sensitivity factors which are useful to the design/ test engineers. Furthermore, the present methodology is computationall y more efficient than a standard Monte-Carlo simulation technique and the agreement between the two is excellent as shown via select example s.