Use Case: Introducing Reicofil

Reifenhäuser Reicofil is the globally leading provider of complete nonwoven, meltblown and composite lines. With its technologies, innovative spirit and know-how along the entire value chain, it enables its customers worldwide to produce nonwoven fabrics economically, reliably and sustainably.

Reifenhäuser Reicofil is a member of the Reifenhäuser Group - the biggest network in plastics extrusion technology worldwide. No other company bundles more competencies in the engineering of machines and components for the production of extrusion products.

It took twelve years of development until the first line for the production of nonwovens could be brought to market. Installed in 1985, the line is still in operation. Since that time, more than 250 lines have been installed. Today Reifenhäuser Reicofil is among the hidden champions in Germany.

Reicofil takes responsibility for following generations. As a supplier of production lines and machinery, they have the opportunity to develop technologies that enable resource and energy conserving production of nonwovens. The goal of Reicofil: better performance and greater energy efficiency with every line generation.

In order to guarantee best quality to its customers, Reicofil uses various methods to observe and improve production such as condition monitoring. It determines the wear and tear of machines in order to plan maintenance work and thus avoid production stoppages and thus high costs. However currently, condition monitoring is mostly carried out by experienced employees. This is one of the challenges, that has been faced in IMPROVE.

Reicofil line

    The innovative tool developed in IMPROVE uses machine learning to reduce unplanned downtime and helps producers to schedule their maintenance actions. By data acquisition systems and algorithms the tool learns the behaviour of the system and can thereby detect and localise anomalies. In contrast to previous methods for condition monitoring, which were related to human experience, the new tool only focuses on machine data and is able to learn by itself. It calculates wear of specific machine parts in relation to the good condition.

    Reicofil composite technology: fulfilling quality standards in the hygiene and medical industries

    It is often a challenge to estimate when it is the best time to replace machine components.

    Replacing some machine components like the conveyor belt requires machine downtime and therefore estimation of the best time to change is difficult. For the most efficient production the conveyor belt should be changed in time: not too early (to use the belt as long as possible) and not too late (impact on product quality).

    With the help of IMPROVE, Reicofil’s customers can save costs caused by unnecessary or too early changes of machine parts (e.g. conveyor belt) on the one hand and – even more crucial - can reduce waste caused by wear of certain machine parts.

    Prediction of the condition of conveyor belt helps producers to plan maintenance and have a good overview over the machinery components status.


    The condition of the conveyor belt will be displayed on machine HMI in real-time and will also predict how the wear will affect the product quality in the future. It prevents production of scrap and gives the possibility to anticipate maintenance work, to automate reordering of machine parts and to make production processes more efficient. In the long term, systems like this will be able to continue learning independently and optimize themselves.

    With the newly developed IMPROVE tool, wear processes can be made tangible and foreseen. This way, producers can plan maintenance and replacement actions optimally.

Further information on Reicofil and its role in IMPROVE can be found here.