Learning-based model predictive control for two-point incremental sheet forming

Abstract

Two-point incremental sheet forming (TPIF) is known to provide higher geometric accuracy compared to single point incremental sheet forming (SPIF). However, it may not always ensure that the maximum allowable absolute geometric error for complex industrial parts is below 0.75 mm. To overcome this challenge, we propose an on-line learning-based model predictive control (MPC) approach to enhance accuracy in TPIF. In this proposed design, the controller parameters can be updated adaptively and effectively on-line, based on historical data, current state, and system inputs, even with unknown system dynamics in future steps. The developed controller is experimentally validated using three benchmark shapes, namely a dog-bone shape, a hemispherical shape, and a water-drop shape, each with different geometric features, in TPIF. The experimental findings demonstrated that the percentage of errors below 0.75 mm can be significantly increased from less than 42 % without control to over 92 % with control for all the tested shapes, while maintaining the safety of the manufacturing process. This highlights the effectiveness and robustness of the developed MPC controller in achieving higher precision for complex parts. This will unlock TPIF capability to form custom high-accuracy parts, thereby enabling ISF as a commercially viable process with increased robustness and accuracy in the future.