Inspect 4.0

For production companies, delivering consistently high-quality products has always been essential. With the Inspect 4.0 project, the University of Antwerp and Centexbel are joining forces to develop and test cutting-edge inspection technologies that enhance quality control while reducing costs.

By harnessing the power of machine vision and machine learning, the project is creating flexible and precise quality inspection systems tailored to modern manufacturing needs.

The technical textile sector is a key pillar of the global textile industry. In 2018, the Belgian textile sector generated €6.1 billion in revenue, with €2.2 billion coming from technical textiles alone—employing around 7,600 people. As pressure from low-wage competitors grows, Belgian producers and finishers must increasingly rely on consistent, high-quality output to stay competitive.

For bare, woven textiles, inspection has historically been performed by people, watching rolls of textile pass over a light table, and manually flagging imperfections. The thickness of coated textile was typically determined via radioactive sources. As both methods present problems, (inspection by humans is time consuming and imprecise, inspection by radioactive sources is being legally phased out) a transition to automated inspection is taking place.

This typically makes use of different cameras, coupled with libraries of textile defects, to alert the user when something has gone wrong. The project Inspect 4.0 seeks to help realize this transition, by illuminating and illustrating the benefits of such systems.

The aim of Inspect 4.0 is to demonstrate how machine learning and machine vision are combined to produce flexible and accurate quality inspection systems, which can be deployed in a range of textile manufacturing setups. In Inspect 4.0 machine vision is seen as a combination of camera systems that use the wavelength spectrum of electromagnetic waves between the UV and long wave infrared spectrum, (250 nm – 140 µm) complemented with extensive data analysis.

The combination of different camera systems can be used to replace and/or enhance existing quality inspection systems, easing the transition to more automated textile inspection. The data analysis will be used for a machine learning system allowing for the detection of errors. Once the illustrative system is set up, investigations will be made into the possibility of incorporating the machine into a broader predictive maintenance framework.