To, Xuan Dung (UTTOP), Blanco, Jefferson Roman (ENSAM), CHEVRET-ZIMMER, Sandra (Arts et Métiers Institute of Technology), Ouaidat, Ginwa (ENSAM), Raharijaona, Thibaut (University of Lorraine, LCFC), Noureddine, Farid (LGP-ENIT), Rakotondrabe, Micky (University of Toulouse alliance)

Robotized Incremental Sheet Forming Trajectory Control Using Deep Neural Network for Force/Torque Compensator and Task-Space Error Tracking Controller

Scheduled for presentation during the Invited Session "Mechatronic approaches in advanced manufacturing for Industry-of-Future" (WeBT1), Wednesday, July 16, 2025, 14:40−15:00, Room 105

Joint 10th IFAC Symposium on Mechatronic Systems and 14th Symposium on Robotics, July 15-18, 2025, Paris, France

This information is tentative and subject to change. Compiled on July 16, 2025

Abstract

In Robotized Incremental Sheet Forming (ISF), achieving precise geometrical accuracy is a challenging task due to trajectory tool center point (TCP) position errors at the forming tool attached to the robot’s end-effector. These errors primarily arise from external disturbance forces and torques generated during the interaction between the forming tool and the elastic metal sheet. While jointtorque space controllers can mitigate reaction forces and torques through dynamic modeling, jointspace control has inherent limitations, particularly for industrial high-load robots like the ABB IRB 8700. To overcome these challenges, this work implements an external force/torque (F/T) compensator in task-space using a deep neural network. The network predicts trajectory errors induced by reaction forces and torques measured via a 6-axis F/T sensor. Additionally, the forming tool’s trajectory is precisely monitored using a laser tracker, which serves as a feedback mechanism in a closed-loop task-space error-tracking controller. This controller detects and corrects trajectory deviations in real time. By integrating the F/T compensator and the task-space error-tracking controller, the proposed approach effectively compensates for reaction forces and torques while addressing additional errors introduced by other process-related factors. This integration results in significantly enhanced accuracy in robotic incremental forming processes.