Strengths prediction of particulate reinforced metal matrix composites (PRMMCs) using direct method and artificial neural network

Predicting strengths and understanding how these values related to the underlying composite structure is essential for the design and application of particulate reinforced metal matrix composites (PRMMCs). In order to investigate how ultimate strength and endurance limit of an exemplary PRMMC material, WC-20 wt% Co, are related to other structural and mechanical characteristics, an integrated numerical approach consisting of direct methods (DM) and artificial neural network (ANN) is presented in this work. Using few features obtained from elastic and DM analyses as inputs, multiple regression and classification ANNs were established to predict global material strengths. With this approach, the study implied that the distribution pattern of the stress field, in particular the one pertained to the binder phase, has a nontrivial influence over global composite strengths.