The quality of punched hole surfaces significantly influences the formability in subsequent processes such as collar forming, especially in the occurrence of edge cracks. Traditional evaluation based on metallographic cross-sections is unsuitable for inline inspection. This has prompted the development of novel approaches for the inline characterization of the functional cutting surfaces. Such methods also enable the systematic acquisition of production data required for data-driven models that enhance robustness against stochastic variations in multi-stage forming processes. This study proposes a non-destructive, automated method for characterizing punched hole cutting surfaces using a laser line profiler. An experimental setup with adjustable tilt angle and a rotation stage for automated sample rotation was constructed. Punched samples of S500MC steel and EN AW-1050A aluminum with different sheet thicknesses were investigated. A python algorithm was developed to automatically identify characteristic features of the cutting surface according to VDI 2906–2. Validation using optical microscopy of metallographic cross-sections confirmed the accuracy of the method. A mean absolute percentage error (MAPE) below 5% was achieved for burnish and fracture zone height. The results demonstrate the potential of laser profiling for non-destructive characterization of punched hole cutting surfaces, establishing the method’s feasibility and laying the groundwork for transfer to near-industrial conditions. Initially validated under laboratory settings, the proposed method will be applied in a progressive forming tool to assess the impact of real-world disturbances such as dust and vibration. While the laboratory results are promising, further optimization of the measurement technique and data evaluation is required for full industrial implementation.
Hahn et al. (Tue,) studied this question.