Correction of Incremental Sheet Forming using a Data-Driven Iterative Learning Controller

Single Point Incremental Forming (SPIF) is a flexible sheet manufacturing technology. While not viable for large scale production applications, SPIF excels in the small batch size and rapid prototyping environments, since geometry-specific tooling is not required. However, due to the lack of in-process support, SPIF typically results in parts with poor geometric tolerances when compared to more traditional part forming techniques such as stamping and deep drawing. For this reason, SPIF has yet to be widely adopted in the industry. In this work, a method of constructing a data-driven model for use with norm-optimal Iterative Learning Controller (ILC) is developed to improve the accuracy of a SPIF process. Using in-process measurements of the sheet along with knowledge of the input, a data-driven model is constructed to optimize the input by predicting the resulting geometry from a change in tool depth. This Iterative Learning Controller was tested on a truncated pyramid geometry, and the results showed that the controller was able to effectively reduce the process error from an MAE of 4.053 mm to 0.912 mm after five iterations.