Process monitoring during clinching based on acoustic emission analysis

• Applicant: Dr.-Ing. Sven Hübner
• Subject Area: Primary Shaping and Reshaping Technology, Additive Manufacturing
• Term: since 2026
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 569426345

Currently, a variety of different materials and sheet thicknesses are used in lightweight construction concepts in order to take advantage of the specific material properties according to the load or requirements of the structure. The joining of components by clinching represents an extraordinary challenge. Due to unavoidable, sporadic process disruptions and errors, automated process monitoring is of great benefit and extremely advantageous. In non-destructive monitoring during a joining process, force and displacement signals are usually recorded and conclusions are drawn about the connection quality. The recorded force and displacement signals are evaluated using special windows, limit values or tolerance bands. However, not all damage and process errors can be detected. For example, cracks that occur due to the stress reduction in the clinch point after the joining process / return stroke are not even recorded. To date, there is no automated process monitoring based on acoustic emission (AE) signals during clinching. The aim is to clarify the basic scientific questions on the physics of acoustic emission in plastic clinching processes. The microscopic mechanisms - from dislocation movements and grain deformation to microcrack formation - are to be investigated with regard to the characteristics of the resulting AE signals. The aim is to improve the joining point quality during clinching by detecting cracks using AE emissions that cannot be detected using conventional technology. A routine for the targeted evaluation of the measurement data for this previously unexplored application is to be developed. This requires a numerical correlation variable to describe the AE signals as a function of progressive elongation of the grains and strain hardening (dislocation movements), which provides an OK/NOK statement from a threshold value and correlates with crack formation, which may also cause further detectable AE activities. To this end, a sound scientific understanding of the relationship between acoustic emission signals and material-related effects at the microstructure level as well as process damage in the clinching process is to be developed and, based on this, innovative methods for suitability for real-time monitoring and defect detection are to be developed. The theoretical development and experimental validation of clinch-specific damage criteria is intended to optimise the detection of process defects such as cracks or insufficient connections. In addition, the project aims to integrate advanced machine learning models in order to analyse complex AE data from the clinching process and thus be able to describe the relationship between AE signal and deformation at microstructure level faster and better.

• DFG Programme: Research Grants