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Machine learning for all areas of automation

Beckhoff now offers a machine learning (ML) solution that is seamlessly integrated into TwinCAT 3. Building on established standards, it brings to ML applications the advantages of system openness familiar from PC-based control. In addition, the TwinCAT solution supports machine learning in real time, allowing it to handle demanding tasks like complex motion control. Its capabilities provide machine builders with an optimum foundation for enhancing machine performance.

The fundamental idea with machine learning is to no longer follow the classic engineering route of designing solutions for specific tasks and then turning these solutions into algorithms, but to enable the desired algorithms to be learned from model process data instead. For data collection, various proven TwinCAT products are available such as e.g. TC3 Database Server TF6420 or TC3 Scope Server TF3300. Training is performed in established frameworks such as MATLAB®, TensorFlow, PyTorch, SciKit-learn, a.o. A trained model can be easily imported into the TwinCAT runtime in a standardised format (ONNX). In automation technology, this opens up new possibilities as well as optimisation potential in such areas as predictive maintenance and process control, anomaly detection, collaborative robotics, automated quality control, and machine optimisation.


TF3800 | TwinCAT Machine Learning Inference Engine

TF3800 | TwinCAT Machine Learning Inference Engine: execution module of trained classical machine learning algorithms

TF3810 | TwinCAT Neural Network Inference Engine

TF3810 | TwinCAT Neural Network Inference Engine: execution module of trained neural networks

Further information