Single system for online monitoring and inspection of automated fiber placement with object segmentation by artificial neural networks

The reduction of material defects in the automated fiber placement process is one of the significant factors for manufacturing large and complex components more efficiently in the future. However, the monitoring of complex manufacturing processes usually requires complex sensor and computer systems that are often quite sensitive to disturbances and errors. New techniques such as image segmentation with neural networks provide a new approach to this problem and have the potential to solve complex processes faster and more robustly. In this study, a system is presented that performs monitoring, inspection and measurement tasks simultaneously in automated fiber placement processes. The system is based on the SiamMask network which is used for the automatic image processing. The artificial neural network is trained to recognize individual carbon fiber tapes and segment them for additional analysis. For the creation of the testing- and training data, an analytical approach is presented. The post-processing of the object segmentation, which is the primary output of the SiamMask network and the identification of individual tapes, provides accurate measurements which are demonstrated by an example. We show that image segmentation with modern approaches like SiamMask offers great potential to handle highly complex engineering tasks in a faster and more intelligent manner in comparison to conventional methods.