Extrusion International 2-2025

10 Extrusion International 2/2025 INDUSTRY NEWS Guiding Topic of K 2025 – “Embracing Digitalisation” K 2025 has set out to tackle the central challenges of our time and present concrete solutions from 8 to 15 October in Düsseldorf. This aim is also re ected by its guiding top - ics. One of them reads “Embracing Digitalisation”. The plastics industry faces major economic and regulatory disruptions worldwide. Rising competitive pres- sure, stricter environmental regula- tions and higher demands made on circularity increase the pressure to innovate. On-going digitalisation offers new opportunities for produc- ing more ef ciently and sustainably here. Automated processes, data- based control systems and smart connectivity already ease adapta- tion to stricter requirements in many companies today. The Federal Minis- try for Economic Affairs and Climate Protection’s (BMWK) Digitalisation Index 2024 provides an indication of the increasing level of digitalisa- tion and has found that the German economy has become around 14% more digital in the last ve years. This has increased especially fast in the category “Processes” which de- scribes both the digital maturity of intra-company work ows and the connection with external partners. Arti cial Intelligence (AI) is con - sidered a key milestone in this. Ac- cording to a Bitkom study, 78% of the industrial companies polled view AI as decisive for their com- petitiveness while more than half are waiting to see how others get on rst. At the same time, 48% lack the necessary AI skills and 91% de- mand fewer regulatory obstacles so as not to hamper AI innovations. These gures underline that there is broad consensus on the relevance of digitalisation but many rms are hesitant to implement it in practice. Digital key technologies: connectivity and IoT The digital control and connectiv- ity of machines forms the basis for new technologies. “In plastics ma- chinery construction automation has already been ongoing for over 40 years. Now nearly all go one step further and bank on digitalisation,” says Ulrich Reifenhäuser, Chair- man of the Advisory Board at K in Düsseldorf. Cyber-physical systems (CPS) and the Internet of Things (IoT) make it possible to capture and evaluate production data seam- lessly in real time. Sensors monitor temperature, ow rate or in-mould pressures, for example, and transmit the values to Cloud applications. An important communication standard for this is OPC UA, which makes for safe and cross-manufacturer data exchange. Rising data volumes lead to ques- tions of data use. According to in- dustrial associations, the so-called “EU Data Act” has created clar- ity on this now. The new Data Act obliges machinery manufacturers to provide machine users with the data generated during operation in a simple and understandable, machine-readable way. At the same time, predictive maintenance moves into focus because real-time analy- ses can detect deviations early on and reduce unplanned downtimes. Arti cial Intelligence and automation AI adds new dynamism to digi- tal processes as self-learning algo- rithms analyse large data volumes and optimise processes exibly. Machine learning accelerates de- velopment cycles and improves pro- cess control. Digital twins go even one step further: they depict real production lines virtually and deliver structured data on the complete ma- chinery utilisation. Furthermore, they offer the possibility to save machine data and information in a structured and machine-readable format over the complete lifecycle. Digital twins are said to also comply with the re- quirements of the Digital Product Passport (DPP),whichwas introduced with the EU’s Ecodesign for Sustain- able Products Regulation (ESPR) en- tering into force in July 2024. These virtual twins of real manufacturing plants accelerate development cycles and ease maintenance strategies. Optical quality control & AI-assisted sorting In the eld of quality assurance camera systems and AI-based im- age processing support manufac- turing processes. They detect shape deviations, surface defects or ma- terial impurities during production and ensure consistent quality levels. These technologies allow early de- fect detection thereby reducing re- jects and ensuring a more ef cient use of resources. In the wake of stricter environ- mental regulations and rising cus- tomer expectations the tness of plastics for circularity is also mov- ing centrestage. AI-assisted sorting systems with near-IR sensors (NIR) identify different plastic types, sep- arate high-quality recyclates from impurities and improve the recy- cling quality. This increases reuse