Automated creation of AI models for image processing and time-based process signals

In addition to the TwinCAT 3 MLC Computer Vision extension package (TE3851), TwinCAT 3 MLC (TE3850) also offers the TwinCAT 3 MLC Signals and Time Series module (TE3852). This extends the range of functions to include the analysis of time-based process signals – often crucial for industrial applications, as current, temperature, and vibration curves provide valuable information about the state of processes, components, and tools. The models created with TwinCAT 3 MLC Signals and Time Series detect patterns and deviations in real time based on the relevant data, enabling predictive maintenance, process optimization, and anomaly detection directly in the control environment.

Typical applications in the field of anomaly detection include the detection of motor malfunctions (bearing damage, imbalance, mechanical problems) based on current, vibration, or acoustic signals as well as wear detection on milling and drilling tools based on spindle currents. Process-integrated quality monitoring can be implemented, for example, in welding processes using current and voltage curves or in cutting and packaging processes using servo motor currents. Examples of process optimization include the dynamic adjustment of adaptive process parameters (e.g., feed rate, pressing force), the reduction of energy consumption based on load profiles and forecasts, or the predictive control of complex systems.

TwinCAT 3 Machine Learning Creator is a pure web application. As the engineering takes place entirely in the browser, no local computing power is required. The process of creating AI models is therefore very simple and accessible for the user.