OptiCoil: KI-gestützte Prozessoptimierung für nachhaltige Stahlfederproduktion

Motivation:

German companies are facing major challenges in the face of global crises and increasing demands for sustainability. The steel industry, one of the largest energy consumers in Germany, plays a central role in this. A sustainable industrial location requires the efficient and responsible use of steel as a resource.
Brand KG manufactures steel springs and bent wire parts for the automotive industry, among others, which must meet the highest quality standards. The quality of the preliminary products (wire bundles, coils) and precise process parameterisation are decisive for production quality. Optimising the production process depending on the coil properties has great potential for reducing steel scrap, which has a significant impact on the company's energy balance.

Aim: 

The OptiCoil project aims to analyse and optimise existing systems in steel spring production using machine intelligence in order to minimise steel scrap. The plan is to retrofit coiling machines used in spring production. By using AI methods, a deeper understanding of the coiling process and its interaction with the coil properties is to be achieved and the production process optimised.

Previous work: 

In the preliminary project (it'sowl-TP-KRISTINA), a winch machine has already been analysed using machine learning methods. Piezoelectric sensors were used to record vibration signals, which allowed conclusions to be drawn about the machine behaviour, predicted impending wire breakages and enabled machine downtimes to be avoided. The OptiCoil project is building on these results and researching further optimisation potential.