Integrated Identification of Collaborative Robots for Robot Assisted 3D Printing Processes

2026-04-02

AI Summary

This study addresses the challenges of low accuracy, difficult control, and unreliable error prediction in collaborative robots used for 3D printing, which stem from dynamic complexity. The authors propose a full-process integrated parameter identification framework tailored for collaborative robots, comprising five sequential steps: geometric and inertial analysis, friction modeling, controller parameter identification, and system residual parameter estimation. This approach yields a physically consistent dynamic model and, for the first time, enables unified identification of robot body, actuator, and controller parameters—making it suitable for real-world scenarios with limited sensing and programming capabilities. Experimental validation on a six-degree-of-freedom collaborative robot performing thermoplastic extrusion demonstrates excellent agreement between the identified model and empirical data, significantly enhancing printing accuracy, control performance, and error prediction fidelity.

Abstract

In recent years, the integration of additive manufacturing (AM) and industrial robotics has opened new perspectives for the production of complex components, particularly in the automotive sector. Robot-assisted additive manufacturing processes overcome the dimensional and kinematic limitations of traditional Cartesian systems, enabling non-planar deposition and greater geometric flexibility. However, the increasing dynamic complexity of robotic manipulators introduces challenges related to precision, control, and error prediction. This work proposes a model-based approach equipped with an integrated identification procedure of the system's parameters, including the robot, the actuators and the controllers. We show that the integrated modeling procedure allows to obtain a reliable dynamic model even in the presence of sensory and programming limitations typical of collaborative robots. The manipulator's dynamic model is identified through an integrated five step methodology: starting with geometric and inertial analysis, followed by friction and controller parameters identification, all the way to the remaining parameters identification. The proposed procedure intrinsically ensures the physical consistency of the identified parameters. The identification approach is validated on a real world case study involving a 6-Degrees-Of-Freedom (DoFs) collaborative robot used in a thermoplastic extrusion process. The very good matching between the experimental results given by actual robot and those given by the identified model shows the potential enhancement of precision, control, and error prediction in Robot Assisted 3D Printing Processes.

Problem

• collaborative robots
• robot-assisted additive manufacturing
• dynamic modeling
• precision
• error prediction

Innovation

• integrated identification
• collaborative robot
• dynamic modeling
• robot-assisted additive manufacturing
• parameter identification