Image Processing Based Method of Predicting Stiffness Characteristics of Short Fiber Reinforced Injection Molded Parts

Abstract

An automated method is presented for predicting the effective stiffness properties of injection molded parts reinforced with non-random distribution of short fibers. The methodology first employs image processing techniques to determine the probability distribution of fiber orientation (PDFO) and probability distribution of fiber length (PDFL), both of which considerably influence the anisotropy of the mechanical behavior of fibrous composites. Simple classical semi-empirical models are then utilized in conjunction with the PDFO and PDFL measurements to predict the elastic stiffness of the molded part. This automated method for predicting the stiffness of fibrous composites is applied to a glass (fiber)/resin (matrix) short fiber injection molded composite specimen.

Keywords

short fiber composites image processing stiffness prediction injection molded composites.

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