In progressive die deep drawing, small deviations in strip positioning and transport can accumulate across stages, leading to significant geometric variations and quality issues. Reliable datasets are therefore essential for data-driven modeling, yet process labels such as the nominal feed length are often affected by machine tolerances and thus introduce uncertainty. This work presents a camera-based methodology for automated feature extraction at the end of the deep drawing process. Using an inline profile projector, relevant geometric features such as circle distances, centroid positions, and local material widths are extracted from high-speed image data. The implemented algorithm combines edge detection, circle fitting, centroid analysis, and directional width measurements to generate consistent feature sets under industrial conditions. Challenges such as material reflections, asymmetric deformation, and non-ideal boundary conditions are addressed, and geometry-specific obstacles for feature detection are discussed. The extracted features are further analyzed with respect to their statistical variability across systematically varied feed length values. Results demonstrate that the variance of image-based features increases with higher feed length, indicating that nominal process labels do not always reflect the actual material position. The study highlights the importance of uncertainty quantification in dataset generation for machine learning in forming technology. By linking process variation to directly observable geometry features, the proposed approach provides both methodological guidance for feature extraction and conceptual insights into label quality in industrial datasets.
Schumann et al. (Mon,) studied this question.