TF38 seamlessly integrates machine learning for automation capabilities into industrial automation processes, setting a new standard for intelligent manufacturing.
Introducing TF38 and Its Integration with Machine Learning
The TF38 function offers a high-performance execution module, or inference engine, designed for the real-time application of trained machine learning models. By supporting established frameworks such as SciKit-Learn, libSVM, and XGBoost, TF38 enables the execution of a wide array of ML algorithms directly within the automation environment. This integration allows for enhanced data analysis, predictive maintenance, and optimized production processes.
Key Features of TF38 in Machine Learning for Automation
A key feature of TF38 is its support for the open neural network exchange (ONNX) format, an open standard that facilitates interoperability between various ML frameworks. This ensures that models trained in diverse environments can be seamlessly deployed in TwinCAT 3, providing flexibility and efficiency in developing and implementing ML solutions.
Enhancing Performance and Efficiency with TF38
"The introduction of TF38 is a significant milestone in the convergence of machine learning and industrial automation,” explained Beth Ragdale, product manager at Beckhoff UK. “Embedding advanced analytical functionalities directly into our control systems enables our customers to achieve exceptional levels of performance and efficiency."
Real-Time Applications of Machine Learning for Automation
The TF38 function is designed to process all inputs, outputs, and internal data from the automation controller using machine learning algorithms. This capability not only improves product quality but also supports real-time classification of machine operational states. By training models with historical data, the system can execute ML models on PC-based controllers, allowing immediate actions to be implemented within the automation system.
Seamless Integration of Machine Learning for Automation in TwinCAT 3
Beckhoff's innovation is further demonstrated by the integration of TF38 into the existing TwinCAT 3 environment. Users can leverage familiar tools and interfaces to develop, train, and deploy ML models, reducing the learning curve and accelerating the adoption of advanced analytics in industrial applications. By embedding machine learning for automation into the heart of control systems, Beckhoff is paving the way for smarter, more efficient industrial processes.
