Automatic detection of fibers orientation on composite laminates using convolutional neural networks

Abstract

Abstract The mechanical behaviour and failure of the composite materials are highly influenced by the fibre’s orientation. It is important to decide which type of layers and orientations to use for the layup sequence such that the composite laminate is as light and/or cheap as possible while being capable to carry the load for which it is designed. During the production process it is critical to cut and lay the composite woven according to the optimal layup sequence resulting from the optimization process. Therefore, it is important to develop an automatic system which is capable to accurately detect the fibres orientation from a material picture. In our previous work we proposed such a system based on image processing and computational geometry. The main disadvantage of this system is that it has a lot of parameters which need to be manually tuned for each material. In this paper we propose a simple system which doesn’t need any parameter tuning. It is based on convolutional neural networks and we demonstrate with a lot of examples that it is very accurate, stable and insensitive to image variations. The method was tested on diverse composite laminates and woven with different chromatic and morphological properties.