A TRUNCATED GAUSSIAN RANDOM FIELD METHOD FOR MODELING THE POROSITY DEFECT IN COMPOSITE STRUCTURES

Abstract

Reducing the cost of manufacturing of primary structures can be achieved by better control of the manufacturing tolerances and acceptable levels of variability. Predicting the effect of defects can also help clear quality concerns that affect the amount of parts that require repairs or need to be scrapped. In this paper, a numerical method is presented for the assessment of the effect of a variable distribution of porosity defects in composite structures at the macroscale. A numerical method is first implemented to simulate random porous zones over the 3-D surface of the composite part. Two or more material configurations can be considered: the sane one and the porous ones. In the numerical model, each element of the part mesh is assigned one of the materials. The random choice between the materials for each element is based on the simulation of a truncated Gaussian random field over the 3-D surface. A Monte Carlo simulation method is used to study the effect of the random spatial distribution of porosity on the mechanical performance of the structure. To illustrate this approach, the case of a curved stiffened panel with a compression load case is considered, and the mechanical performance is evaluated. By taking all the samples of random porosity distributions that have high values for displacement and stress, the critical zones of the structure are identified. Based on these predictive methods, a methodology could further be defined to select more critical areas for inspection and potentially reduce time required for quality controls.