Learning for all areas
Machine learning for all areas
Beckhoff offers a solution for machine learning (ML) that is seamlessly integrated into Twincat 3. The advantages of system openness familiar from PC-based control are also available for ML applications thanks to the use of established standards.
ML is also implemented in real time, making the Twincat solution suitable for the demanding motion sector, for example. This provides machine manufacturers with a basis for increasing machine performance, for example through prescriptive maintenance, self-optimization of process sequences or autonomous detection of process anomalies. The basic idea of ML is to no longer develop solutions for certain tasks through traditional engineering and transfer them into an algorithm. Instead, the desired algorithm should be learned using exemplary process data. In this way, powerful models can be trained and more efficient solutions achieved. Seamless integration into the control technology means that Twincat's multicore support is also available for ML. This means that the respective Twincat3 Inference Engine can be accessed from different task contexts without this having a mutually limiting effect. Full access to all fieldbus interfaces and data in Twincat is also provided. This allows the ML solution to use a large amount of data, for example for complex sensor data fusion (data linking). On the other hand, real-time-capable interfaces to actuators are available for Optimal Control, among other things. pb
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