TwinCAT 3 Vision Machine Learning provides an integrated machine learning (ML) solution for vision-specific use cases. Both the training and the implementation of the machine learning models take place in real time, and they even help machines to learn sophisticated data analyses automatically. This can be used to replace complex, manually created program constructs.

A variety of different classic ML models are available, such as Support Vector Machine (SVM), Random Forest (RTrees), k-Means++, and Principal Component Analysis (PCA). These can be used for classification, regression, cluster analysis, and anomaly detection.

The ML functions can be seamlessly combined with other functions of the Vision library. What’s more, the package also offers feature extraction and standardization procedures.

Application examples include object recognition/detection, sorting, quality control, and process monitoring.