Inline closed-loop control of bending angles with machine learning supported springback compensation

Abstract Closed-loop control of product properties is becoming increasingly important in forming technology research and enables users to counteract unavoidable uncertainties in semi-finished product properties and process environments. Therefore, closed-loop controlled forming processes are considered to have the potential to reduce tolerances on desired product properties, resulting in consistent qualities. The achievement of associated increases in robustness and reliability is linked to enormous requirements, which in particular include the inline recording of the product properties to be controlled and the subsequent adaptation of the process control through the targeted derivation of manipulated variables. The present paper uses the example of an air bending process to show how the bending angle can be controlled camera-based and how springback can be compensated within a stroke by recording force signals and subsequently predicting the loaded bending angle using machine learning algorithms. The results show that the combined application of camera-based control and machine learning assisted springback compensation leads to highly accurate bending angles, whereby the results strongly depend on the machine learning algorithms and associated data transformation processes used.