Development of an intelligent metal forming robot and application to multi-stage cold forging

Abstract Metal forming processes often undergo different instability phases due to different factors like tool and part temperature variations, tool vibrations or frictional interactions between workpiece and tool. In the absence of an experienced process operator, these instabilities can induce a very considerable production loss. This study addresses this issue and proposes a method to develop a data-based virtual process operator equipped with the appropriate hardware and physical components that allow it to constantly monitor and if necessary regulate the process. The resulting system is introduced as the intelligent metal forming robot. The objective of this self-learning system is first to stabilize the process and ensure a certain part quality despite the noises, dynamical disturbances and user-defined changes of the part quality requirements, then, to control the process even in states that have not yet been experienced and at last to improve the control precision based on the updated process experience. This intelligent metal forming robot has been implemented and applied on a two-stage cold forging process, where the target quality feature was the head height of a screw-like part. The results showed that, based on a qualitative process experience and effective actuators, an intelligent self-learning system can significantly increase the robustness of a metal forming process.