Beckhoff Automation tf7810

TwinCAT 3 Vision Neural Network provides an integrated machine learning (ML) solution for vision-specific use cases. The implementation of the machine learning models takes place in real time. With the help of these models, complex data analyses can be learned automatically. This means that complex, manually created program constructs can be replaced. The TF7810 TwinCAT 3 Function is a high-performance execution module (inference engine) for trained neural networks. The neuronal networks are trained in established frameworks such as PyTorch, TensorFlow, or MATLAB®. The information from the learned network is loaded to the inference engine as a description file. The Open Neural Network Exchange (ONNX) standardized exchange format is supported, thus seamlessly merging the worlds of automation and data science. One example of a neural network is the Convolutional Neural Network (CNN). In contrast to classic ML models, image data is transferred directly and feature extraction takes place in the models. Various tasks can be solved with the help of these models. Application examples include object detection, segmentation, classification and anomaly detection for quality control, and process monitoring.