Each application places different demands on the method of archiving machine data: SQL or noSQL, file-based, cloud-based data lake, etc. For all these scenarios there are a large number of established TwinCAT products available, such as the TF6420 TC3 Database Server, the TF3300 TC3 Scope Server or the TF6720 TC3 IoT Data Agent.
Training is performed in established frameworks such as PyTorch, TensorFlow, SciKit-Learn, MATLAB®, etc. This ensures maximum flexibility. No limits are set in the case of an interdisciplinary project between automation engineers and data scientists – neither internally in the company nor beyond the bounds of the company. The learned model can simply be exported in a standardized format (ONNX) and handed over to the TwinCAT programmer.
Deployment takes place via TwinCAT Engineering directly into the TwinCAT XAR, so that the learned model (inference) is executed directly in hard real-time on the machine controller and is thus synchronous with all other controller objects.
In automation technology, this opens up new possibilities as well as optimization potential in such areas as predictive maintenance and process control, anomaly detection, collaborative robotics, automated quality control, and machine optimization.