Semester

Spring

Date of Graduation

2003

Document Type

Dissertation

Degree Type

PhD

College

Statler College of Engineering and Mineral Resources

Department

Mechanical and Aerospace Engineering

Committee Chair

Ever J. Barbero.

Abstract

Fiber reinforced plastic composites are an attractive alternative to traditional materials because of, among other things, the ability to concurrently design the materials and ratios to fit a specific need. One method of fiber reinforcement is through the used of woven fabrics, which provide more balanced overall strengths and durability during fabrication. The weaving and interlacing of the fibers, however, adds a level of complexity when predicting material properties and strengths using micromechanical models. Traditional models have mostly been based on classical thin lamination theory, and this method is limited in its scope and applicability for woven fabric composites. This research sought to develop a novel procedure for predicting the overall material properties (a complete set) and internal stresses for a plain weave fabric composite. The new model is based on periodic microstructure, taking advantage of the sinusoidal weaving nature of the plain weave geometry. The new application of periodic microstructure combines the power and comprehensiveness of the finite element method with the ease of use and speed of a micromechanical model based in classical lamination theory. All of the relevant equations and relationships pertaining to the application of periodic microstructure to a plain weave fabric composite were developed. The analytical weave geometry of Ito and Chou and the experimentally determined geometry developed by the Construction Engineering Research Laboratory (Army Corps of Engineers), along with the derived equations, were inputs into a Mathcad program that calculates the effective stiffness matrix of the representative volume element (RVE) as well as the point wise stresses at any location within the RVE volume. Results were compared with existing experimental and finite element data, with excellent correlation in both cases.

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