A preliminary step toward intelligent forming of fabric composites: Artificial intelligence-based fiber distortions monitoring

Abstract

This work presents a prototype of an intelligent quality inspection tool for application to fibers distortion monitoring in the fabric composites forming processes. To this end, a series of hemisphere draping tests on a typical commingled fiberglass/polypropylene twill weave were conducted at the dry form, and the defects (in-plane and out-of-plane) for each formed part were inspected via camera vision. By gathering data from around 30 samples and over 1200 images from different forming regions, different machine-learning algorithms were trained and validated. As an application scenario in a smart factory, the developed simple AI tool would be used by an operator, or a robot, to scan different areas of the formed parts and identify ‘defected’ (fail) versus ‘non-defected’ (pass) scenarios. It was found that the K-nearest neighbors and Support Vector Machine models detect the defects with an error rate of less than 5% in the present case study, regardless of the background noise in the images such as external objects, marks on samples, or blurriness.