Real-Time Machine Vision System for an Automated Quality Monitoring in Mass Production of Multiaxial Non-Crimp Fabrics

Abstract

Fiber-reinforced plastics (FRPs) are used in an increasing number of applications due to their advanced light-weight properties. Beside classical deployments in high-value industries like aerospace and medical engineering, FRP materials are pushed towards mass production by the automotive industry. To mass-produce FRP products, textile structures are commonly used as semifinished products, such as multiaxial non-crimp fabrics (NCFs). However, poor repeatability and missing textile defect detection in the automated manufacturing of FRP components are major cost factors and challenge economically the series production. Reduction of these cost factors is not yet possible due to the lack of closed-loop control systems. There is currently no real-time quality monitoring system capable of ensuring quality in NCF production. The purpose of this study is to develop tools and concepts for real-time quality control of non-crimp fabrics. Therefore, a real-time machine-vision system has been developed with the purpose of detecting relevant quality features in a textile sample in deterministic time conditions. The embedded system ensures the execution of all process steps, i.e. image acquisition, processing, and evaluation, under real-time conditions. The main focus of this work is laid on the real-time algorithms for an accurate and robust detection of the fiber orientation under industrial conditions. The developed real-time system has been tested on a textile sample and an assessment of the measurement uncertainty has been performed. Results show that the proposed system can successfully assess common textile quality features.