Through AI-driven image analysis and data models, methods are developed to analyse material surfaces and automatically adjust process parameters. For example, it allows compensation for uneven colour absorption or variations in material structure, leading to more homogeneous and high-quality results.
A key contribution is to reduce the need for physical tests and iterations. Through predictive models, properties such as colour bleed, functionality or durability can be simulated in advance. This reduces both resource use and lead times in product development.
RA2 thus opens up for a more data-driven and flexible production where processes are continuously optimised based on real-time data. In the longer term, conditions are created for integrating recycled materials into advanced textile applications with high performance and aesthetic quality.
Contact
Junchun Yu, Senior Lecturer at the Department of Textile Technology, RA2 Leader
E-mail: junchun.yu@hb.se
Telephone: +46 33-435 4190
Sina Seipel, Senior Lecturer at the Department of Textile Technology
E-mail: sina.seipel@hb.se
Telephone: +46 33-435 4191
