An investigation of the static damage mechanisms of pultruded glass fiber reinforced polymers with artificial neural networks.

Pultrusion is a promising technique for manufacturing composite materials in the form of constant-section profiles, which allows to obtain these products in a highly automated way and with an overall good fiber alignment and cohesion. This material is used in civil and structural applications, but its development is being slowed down due to the fact that different types of damage can suddenly develop during the loading of the structure, leading to unexpected failure. The objective of the study is to identify the damage modes evolving in pultruded glass-fiber reinforced polymers during static tensile tests. The experimental campaign consists of 34static tensile specimens with two different layups. During each test, Acoustic Emission data is recorded to assess the different characteristics of the signals and their location. A Self Organizing Map, clustered with the k-means algorithm, was used for retrieving classes of similar signals in the dataset. The evolution of damage and energy content of each class was followed during the test; this allowed identifying and separating different damage modes. Moreover, the possibility to apply unsupervised neural network clustering techniques to the AE data is investigated; this is used to filter out the signals which aren’t linkable to a material degradation