The analysis of FiberGuard tension data is complex and time‑consuming, often requiring deep expert knowledge. This slows down reactions to quality deviations and makes internal knowledge transfer challenging.
In addition, limited real‑time feedback on quality issues delays corrective actions and increases the risk of bobbin quality downgrades.
Artificial Intelligence in manufacturing for DTY (AIM4DTY) is an AI‑based solution that continuously monitors yarn tension behavior and detects deviations from expected quality limits.
FiberGuard sensors capture tension characteristics and generate fault graphs when deviations occur. An AI model analyzes these fault graph patterns and assigns the most probable root cause for bobbin quality downgrades with high accuracy.
The application visualizes patterns, deviations, and root‑cause insights in a clear and intuitive way, enabling faster understanding and decision‑making directly on the shopfloor.