"We have experienced an enormous surge in digitalization and can benefit from a steep learning curve." But that's all the positives Ulrich Leidecker can take away from the biggest influencing factor for 2020 - coronavirus.

Phoenix Contact will probably close the 2020 financial year with a moderate decline in sales of 4 to 5 %; the original business expectation of 2 to 3 % growth had to be dropped due to coronavirus.

In terms of technology, Leidecker is focusing primarily on the PLCnext open ecosystem. Phoenix Contact licensed its PLCnext runtime environment to Yaskawa back in September. Yaskawa plans to use the PLCnext runtime system in the Motion Controls and Robotics divisions, initially in Europe and the USA. New to the PLCnext Control family are two devices that have been specially developed with a focus on edge use cases. They are based on the EPC 1502 and EPC 1522 industrial box PCs.
The functionalities of the PLCnext Runtime System are supplemented by software applications specially prepared for edge computing, such as an additional web-based configuration interface for Docker containers, NodeRed and an integrated database.

In future, PLCnext applications that require functional safety can also be retrofitted with a corresponding extension module. Two expansion modules, AXC F XT SPLC 1000 and AXC F XT SPLC 1000, are now available to implement systems with up to 32/300 Profisafe devices in accordance with SIL3 or. Performance Level PLe.

Scalable AI solutions from the field level to the controller and edge level through to the cloud can also be implemented in future with the PLCnext ecosystem. The first step towards integrating artificial intelligence into the world of automation is the new AXC F XT ML 1000 extension module, which is expected to expand the ecosystem from mid-2021. The extension module makes it possible to apply machine learning algorithms directly to the PLC. Every AXC controller in the PLCnext family that is functionally extended in this way can then implement the classification of objects based on deep learning methods, for example.