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In this paper Knowledge Discovery System (KDS) is proposed and implementedfor the extraction of knowledge-mean stiffness of a polymer composite materialin which when fibers are placed at different orientations. Cosine amplitudemethod is implemented for retrieving compatible polymer matrix andreinforcement fiber which is coming under predicted fiber class, from thepolymer and reinforcement database respectively, based on the designrequirements. Fuzzy classification rules to classify fibers into short, mediumand long fiber classes are derived based on the fiber length and the computedor derive critical length of fiber. Longitudinal and Transverse module ofPolymer Matrix Composite consisting of seven layers with different fiber volumefractions and different fibers orientations at 0,15,30,45,60,75 and 90 degreesare analyzed through Rule-of Mixture material design model. The analysisresults are represented in different graphical steps and have been measuredwith statistical parameters. This data mining application implemented here hasfocused the mechanical problems of material design and analysis. Therefore,this system is an expert decision support system for optimizing the materialsperformance for designing light-weight and strong, and cost effective polymercomposite materials.

Knowledge Discovery System For Fiber Reinforced Polymer Matrix Composite Laminate