摘要：Carbon fiber reinforced plastics (CFRPs) have evolved into the primary material for several lightweight structures. However, despite their extensive use and the quality amelioration, CFRPs remain susceptible to a variety of manufacturing defects, the most common of which are the pores. Predictive tools capable of correlating the mechanical properties of CFRP parts with the characteristics of defects as derived from non-destructive testing (NDT) techniques or even further with the manufacturing parameters could serve as an effective tool for the quality control of CFRP structural parts. In the present paper, the characteristics of pores as evaluated by X-ray Computed Tomography (CT) have been correlated with the matrixdominated mechanical properties of unidirectional porous CFRP specimens using Artificial Neural Networks (ANN). Thirty (30) porosity scenarios have been created and given as input to the numerical model. That multi-scale numerical model, which had been validated experimentally, has been used for training the ANN model. The predictions of the ANN agree very well with results from mechanical tests. Moving one step forward, a second ANN has been developed to correlate the autoclave pressure directly with the mechanical properties of the CFRP specimens. The validity of the latter ANN depends on the accuracy of the relation between the autoclave pressure and the characteristics of the pores. The present work represents a step towards the development of effective quality control tools for composite materials.
