New languages

Automotive history is written in C++ today as BMW seeks young computer programmers. Automation experts are also gradually leaving IEC 61131 behind. "Users will have increasingly intuitive programming interfaces. We're developing these already in robotics. Developers will work in standard languages (C++, C#, Java and others), for example Kuka's latest robot controls are no longer programmed in KRL (Kuka Robot Language), but in Java," explains Professor Peter Hess of the Mechanical Engineering Faculty at Georg Simon Ohm Nuremberg Tech. Phoenix Contact has recognized this, and with its PLCnext Technology functions under IEC 61131-3 can be combined with routines from C/C++, C# or Matlab Simulink. Developers ensure that such simple integration of software from the open source community into Phoenix Contact's automation system is possible. Hartung is also hip to the importance of software. The MICA modular minicomputer stands as evidence. Their promise is for customers to be able to register, evaluate and process data directly in the environment where the machines and systems are running. The minicomputer can also be configured with individual hardware, open source software and appropriate interfaces – welcome to Python and its peers, software expertise is there, new application scenarios and markets can take shape.

Platforms as operating systems

They are called Adamos or Axoom – the Industry 4.0 platforms from different industrial companies in Germany and around the world. But what are the companies in this field working on now? On operating systems for industry, says Dr. Thomas Bauernhansl of Fraunhofer IPA in Stuttgart. These platforms might set the tone for the coming years, developing software interfaces together that can be purchased in a store. What does this operating system do? It manages PLCs from the cloud, for example – Edge PLC is soon to arrive. Bauernhansl calls this the "platform as operating system" approach (hard real time operating system). PLC providers have to sit up and take notice: How will controllers change in the next five years?

Machine learning

Artificial intelligence is all the rage among industrial pioneers, and the major software and cloud providers are already promising whole new worlds for industry. But the first step is machine learning – collecting information, recognizing patterns, drawing conclusions – perhaps like Roger Feist from the Achenbach Buschhütten mechanical engineering firm. In system operation, all data from the Bachmann M1 control is transmitted via OPC UA to a small single-board computer, which can take the information and store it in the cloud. Around three gigabytes of data can be gathered by each machine each day – primarily OPC, UA and SQL data. Because almost infinite storage is available in the cloud, this machine data never needs to be deleted for space reasons. Achenbach frequently relies on unsupervised machine learning, in which the system tries to recognize patterns in the machine data that deviate from unstructured noise. Ideally these patterns are typical of a given problem. Then the system can give the operator a solution recommendation – such as ordering a replacement part.