Train a Support Vector Machine to improve the quality of your production processes
The convergence of machine learning and industrial automation gives access to unprecedented capabilities. All inputs, outputs and internal data made available by the automation controller can be processed by a SVM algorithm in TwinCAT 3. This can improve the quality of the produced goods but also allows to make a classification of the machine’s state of operations. The automation controller can process the classification in real time to enable immediate counter measures implemented in the automation program. The SVM is trained with historical data which has previously been collected. The result is a machine learning model that is executed on the PC-based controller. This TwinCAT 3 extension contributes to the convergence of machine learning and industrial automation.