Workflow with Beckhoff tools: from data to the AI model 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.
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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.
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. These frameworks, which are established in the data science community, are generally open source and can therefore be used free of charge.
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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.
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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. Real-time-capable ML model inference capabilities as a standard module in TwinCAT 3: no separate hardware is required and the functionality is implemented solely in software on the same platform as the remaining control application.
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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 om te vragen flört make 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.
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 the execution of the machine learning models in real time.
Its capabilities provide machine builders with an optimum foundation for enhancing machine performance.