Method of an Automated Tool Design of Punch-Bending Processes Using the Asset Administration Shell

<jats:p>A common challenge of punch-bending is the control and compensation of process deviations which are induced, e.g., by different material batches. The resulting deviations are currently countered iteratively using expert knowledge in the design phase as well as during the production process. However, this is not always successful due to the complex interactions between semi-finished product properties and forming tools. To automate the optimization of tool geometries and process adjustments, an accurate prediction model of correlations between process/tool parameters and product properties is necessary. In that regard, an application programming interface (API) aligned with the Asset Administration Shell (AAS) specification is developed utilizing a hybrid data-driven approach. It enables the transformation of various data formats e.g., from sensor and simulation data into a machine-readable structure. The API is linked to a digital twin and a database, to automatically gather requested information and provide the new structured data to a machine learning (ML) model. To validate the developed method, hybrid punch-bending data is automatically transferred to an ML model which then returns tool geometry suggestions for a defined product geometry.</jats:p>