  In this paper Knowledge Discovery System (KDS) is proposed and implemented
for the extraction of knowledge-mean stiffness of a polymer composite material
in which when fibers are placed at different orientations. Cosine amplitude
method is implemented for retrieving compatible polymer matrix and
reinforcement fiber which is coming under predicted fiber class, from the
polymer and reinforcement database respectively, based on the design
requirements. Fuzzy classification rules to classify fibers into short, medium
and long fiber classes are derived based on the fiber length and the computed
or derive critical length of fiber. Longitudinal and Transverse module of
Polymer Matrix Composite consisting of seven layers with different fiber volume
fractions and different fibers orientations at 0,15,30,45,60,75 and 90 degrees
are analyzed through Rule-of Mixture material design model. The analysis
results are represented in different graphical steps and have been measured
with statistical parameters. This data mining application implemented here has
focused the mechanical problems of material design and analysis. Therefore,
this system is an expert decision support system for optimizing the materials
performance for designing light-weight and strong, and cost effective polymer
composite materials.
