Coatema Coating Machinery GmbH has demonstrated exciting developments, showing a pathway ultimately towards autonomous self-optimising coating and printing machines within the next decade or so.
As can be seen below, Coatema develops multi-station printing and coating systems, inline integrating R2R slot die coating, inkjet printing, drying, laser processing, intense light sintering, winding/unwinding, etc. The example below is a machine installed at the OET - Organic Electronic Technologies P.C. in Greece.
Of course, printing and coating are complex technologies with a large multi-parameter pace. Just some of the parameters are shown below. Therefore, product development and transition from lab-to- fab can be time consuming and challenging since finding as well as maintaining optimal printing, coating, drying, and sintering conditions across such as complex multi-step system can be a significant challenge, particularly for printing multi-layer devices or structures and for lab-to-fab transition.
Coatema now integrates multiple measurement points inline within its machinery (see below). The result is millions of data points per minute as output, giving insights at every stage of the process.
To make sense of all these data points, Coatema, together with partners Panda, is developing AI algorithms, which, for example, enable automatic identification of the location of the anomalies on the coated or printed surfaces. This automatic AI-based anomaly detection can be done in the time series as well, allowing one to identify the location as well as the time stamp of the anomalous coating or printing step. To identify such anomalies, as seen below, the algorithm is constantly analysing the data coming out of the multi-station fully-integrated printing and coating machines.
These developments by Coatema demonstrate the future evolution of printing and coating machinery. This level of insight will enable accelerated product development , optimization and lab-to-fab transition, as well as excellent uniform quality maintenance over large production print runs.
From the long-term perspective, it begins to lay the groundwork for autonomous self-optimising printing machines which find and maintain optimal print conditions with little human intervention.