In recent years, advancements in automation technology have spurred a series of interesting and promising innovative technologies. Machine learning is one of the most distinctive and promising technologies among them, and Beckhoff has now seamlessly integrated this technology into its control systems through TwinCAT 3 , achieving a perfect fusion of data science and equipment manufacturing, creating a significant synergistic effect and unlocking new potential for mechanical optimization.
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The fundamental concept of machine learning is to move away from the traditional engineering approach of designing solutions for specific tasks and then translating those solutions into algorithms. Instead, it learns the desired algorithms from boilerplate process data. This alternative approach trains powerful machine learning models to provide more advanced or higher-performing solutions. In automation technology, this opens up new possibilities and optimization potential in many areas, including predictive maintenance and process control, anomaly detection, collaborative robots, fully automated quality control, and machine optimization.
Integrating machine learning will bring significant benefits to robotics, algorithm optimization, anomaly detection, model predictive control, and other applications. Currently, many manufacturing systems have already collected vast amounts of data that can be used for machine learning, and Beckhoff TwinCAT 3 Measurement and TwinCAT 3 Connectivity can play a crucial role in data acquisition and analysis.
Furthermore , by seamlessly integrating these systems through TwinCAT 3 Machine Learning, machine learning frameworks including TensorFlow , PyTorch , and MATLAB® can be directly applied .
Beckhoff has recently launched TwinCAT 3 Machine Learning Inference Engine for traditional machine learning algorithms and TwinCAT 3 Neural Network Inference Engine for deep learning and neural networks . These two software products provide automation experts and equipment manufacturers with an industry solution that integrates the execution of trained machine learning models into their systems.
Through seamless integration with control technologies, TwinCAT 3's support for multi-core systems can also be used in machine learning applications. This means that different task scenarios can access a specific TwinCAT 3 Inference Engine without interfering with each other. It also provides full access to all fieldbus interfaces and data available in TwinCAT . This allows machine learning solutions to utilize large amounts of data, for example, for complex sensor data fusion (data merging), and also means that optimal control can be achieved using the real-time interface of automata.
