Machine learning for all areas of automation

Beckhoff offers a machine learning (ML) solution that is seamlessly integrated into TwinCAT 3. Building on established standards, it brings the advantages of system openness familiar from PC-based control to ML applications. In addition, the TwinCAT solution supports execution of the machine learning models in real time, making it ideal to handle all of your automation solution’s tasks.

AI applications in industrial automation

Check out the accompanying video to find examples of applications that have been successfully implemented on the basis of Beckhoff technology. The applications below have been singled out, not least by the McKinsey market research institute, as the top fields of application for AI in the industry:

• collaborative and context-aware robotics
• reducing waste quantities
• machine optimization
• machine-integrated quality checking
• predictive maintenance

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, rather to enable the desired algorithms to be learned from model data instead. Beckhoff offers a closed and flexible workflow for the entire cycle from data collection through model training to deployment of the trained model.

Data collection

Each application and also each IT infrastructure places different demands on the method of collecting machine data: SQL or noSQL, file-based, local or remote, cloud-based data lake. For all of these scenarios there are a large number of established TwinCAT products available, such as the TwinCAT 3 Database Server TF6420, the TwinCAT 3 Scope Server TF3300, TwinCAT 3 Analytics Logger TF3500, or the TwinCAT IoT Data Agent TF6720.

The task of data collection generally falls within the realm of automation specialists. They know the control architecture, the general conditions on the shop floor, and are optimally equipped with the products referenced above to carry out their work efficiently and in line with the needs of the situation.

Model training

Models are trained based on the supplied data in frameworks such as PyTorch, TensorFlow, SciKit-Learn ^®, etc. These frameworks, which are established in the data science community, are generally open source and can therefore be used free of charge. Maximum flexibility is therefore assured and no limits are set in the case of an interdisciplinary project between automation engineers and data scientists – neither within the company nor across company boundaries. Each team member can work in their familiar environment, for example with TwinCAT for the automation specialist and TensorFlow for the data scientist.

Are you in need of a team member for such interdisciplinary projects? We can help. Simply get in touch with your local Beckhoff sales staff or use the contact form. Beckhoff offers support free of charge as normal and can help you to develop your ML solution. Should you be interested in the services of a data scientist, we can search for suitable companies for you in our network.

Deployment

The trained ML model can simply be exported from the ML framework in the standardized Open Neural Network Exchange format (ONNX) and handed over to the TwinCAT programmer.

Deployment takes place via TwinCAT Engineering directly into the TwinCAT XAR, so that the trained model (inference) is executed directly in hard real-time on the machine controller and is thus synchronous with all other controller objects.

ML models have the property of improving through training on larger sets of data. Likewise, general conditions can change gradually or spontaneously when the machine is being operated. To take account of this, you can update your trained ML models during the life of the machine: so without stopping the machine, without recompilation, and at the same time completely remotely via the standard IT infrastructure.