Oez, Zeyneddin
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Publication Access status: Open Access , TubeBEND: A Real-World Dataset for Geometry Prediction in Rotary Draw BendingThis paper presents TubeBEND, a real-world dataset comprising 318 rotary tube bending processes, which were collected and sorted by experts from various fields to evaluate machine learning and signal analysis methods. The dataset addresses the industrial challenge of predicting the geometry of a first-stage bend, which can be beneficial for designing machine clamping molds for the second-stage bend in two-stage rotary draw bending. Some geometry criteria, such as the tube's final bent angle (or springback) and its cross-sectional deformation, are being recorded in this dataset. This dataset enables the development of machine learning models for geometry prediction and improved machine setup, supporting operators in reducing springback and deformation. Moreover, by recording some process parameters, such as tool movements and forces or torques applied to them, we deliver detailed information about their impacts on the final tube geometry. The focus of our work is to discover solutions that can replace traditional methods, such as trial-and-error or simulation-based predictions, by including experimental process variables in ML algorithms. Our dataset is publicly available as a benchmark to improve data-driven methods in this field at: https://github.com/zeyneddinoz/tubebend and https://zenodo.org/records/18458823.Source Type:InProceedings2 7
