What are extrusion processes?

Extrusion processes are used in a variety of industries. Extruders are used to form plastic products, such as sealing systems for car bodies. In addition to molding, extruders are also used for chemical processing and modification of materials. The food industry uses this principle to produce artificial meat substitutes, for example. In the plastics industry, this principle is used to produce rubber compounds such as thermoplastic elastomers. In both industries, single or twin screws are often used to feed a material through different zones in the elongated extruder. In the feed section, the materials are heated, compressed, degassed or mixed with other materials.

Challenges in extrusion processes

• Process stability and control: Maintaining stable process conditions is necessary to achieve consistent product quality. Challenges can arise from variations in extrusion parameters such as temperature, pressure, screw speed and material feed rate. Inadequate control can result in product defects such as surface imperfections, inconsistent dimensions, or poor mechanical properties.
• Cleaning and Maintenance: Extrusion equipment requires regular cleaning and maintenance to ensure optimal performance. Residual material buildup, die clogging, or wear and tear can impact product quality, increase downtime, and reduce overall efficiency.
• Energy Consumption: Extrusion processes can be energy-intensive due to the heating and melting requirements. Reducing energy consumption without compromising product quality poses a challenge for manufacturers.
• Sustainability and Recycling: Addressing environmental concerns is an emerging challenge in extrusion processes. Finding ways to optimize material usage, reduce waste generation, and incorporate recycled materials into the extrusion process are important for sustainable manufacturing practices.

Overcoming these challenges requires a combination of process optimization, advanced control systems, and continuous monitoring to ensure consistent and high-quality extruded products.

Our solution with ExtruAI 

With ExtruAI, we will resolve these challenges. With the help of a scalable assistant, we will give the employee on the extrusion line recommendations on how to readjust the process. This will support the employee's existing knowledge and minimise the number of necessary iterations. The plant operator benefits from less waste and increased machine capacity. Even less experienced employees can regulate the process independently with ExtruAI, which simplifies personnel allocation.
On a technical side, ExtruAI uses a prescriptive Artificial Intelligence, which consists of three components:

• Standard scheme for the processing of extrusion data: To enable a scalability a standard scheme to provide data out of the extrusion process will be created.
• Self-learning model for modelling the process state: A standard model approach that learns the process behaviour delivered by the defined standard scheme.
• Automated feedback to the operator with the possibility to recommend pre-defined actions for an anomaly.

Access to extruder data

Extruders are controlled by PLCs. A PLC can provide data (e.g. sensors, setpoints) via the industry standard for machine communication, OPC UA. The OPC UA standard for data exchange is defined to better align communication between different vendors. EUROMAP standards are defined for extrusion applications. The extrusion standard EUROMAP 83 defines several standard parameters such as troughput, specific output, temperature in different zones, product speed and energy consumption. With the PLC as the OPC UA server and our ExtruAI component as the OPC UA client, the data can be easily collected and automatically analyzed.

The KITT4SME project has developed a managed platform for Kubernetes hosting. We can host our component within this platform.

KITT4SME Support

We want to say thank you to the whole KITT4SME team - here you find the official website.
Here you can find the KITT4SME platform for application hosting and managing.
This work was supported by the H 2020 project "Platform enable KITs of Artificial Intelligence for an Easy Uptake of SMEs (KITT 4 SME)" under GA 952119.
Furthermore, we want to thank HEXPOL Compounding AB for supporting us with data from their manufacturing processes during the project.