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Incremental sheet forming is a sheet forming process for small lot sizes due to its dieless principle. One of its process variants includes local heating of the sheet to counteract some of the process restrictions (formable materials, forming forces, achievable deformations). Although forming at elevated temperatures provides various advantages, the geometric accuracy of the formed part remains low due to shrinking effects caused by local heating and cooling. This publication presents a data-driven approach where process data is gathered and used in regression learning to predict the geometric accuracy resulting from the shrinking effects. To successfully apply regression learning, a big amount of process data is needed covering a wide range of possible process states. Therefore, a specific experimental series, consisting of 54 individual forming experiments, is designed and carried out. Based on the 3D digitization of the formed parts, a process database is built up comprising 408,296 records, each representing a toolpath point. This process database is used to train 19 different regression models. The performance of their ability to predict the geometric deviations is investigated. A compensation approach is presented that improves the geometric accuracy through a prediction-based modification of the toolpath. Validation experiments demonstrate the improvement of the geometric accuracy of the formed part and the generalizability of the approach.

In robot-based incremental sheet forming, the forming robot is displaced due to the forming forces and the comparable low stiffness. As the forming forces can not be predicted precisely, the stiffness needs to be compensated based on the measurement of a force torque sensor. While previous approaches used precalculated lookup tables, this publication presents a multi body system robot model that can calculate the displacement of the tool center point in real-time. In incremental sheet forming, the supporting robot is typically force controlled to bring superimposed stress into the forming zone. Unfortunately, this could lead to oscillations in the stiffness compensation. The presented force control approach takes the stiffness compensation into account to ensure a smooth movement of the forming robot. In a series of 30 forming experiments the effectiveness of the developed stiffness compensation and force control is validated.

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