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

Alessandro Dimauro, Davide Tebaldi, Fabio Pini, Luigi Biagiotti, Francesco Leali

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.

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

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.

Paper Structure

This paper contains 16 sections, 16 equations, 6 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Methods and Tools
  3. Dynamic Modeling of Robotic Manipulators, Actuators and Controllers
  4. Integrated Parameter Identification Procedure
  5. Step 1: From CAD file to Geometric and first-attempt Inertial Parameters.
  6. Step 2: From Static and End Effector Measures to Identification Refinement of Inertial Parameters.
  7. Step 3: From Low Speed Measures to Identification of Friction Parameters.
  8. Step 4: From Static Measures with Disturbances to Identification of Controller Parameters.
  9. Step 5: Identification of Remaining Parameters.
  10. Results of Parameters Identification
  11. Model Verification through Design of Experiments
  12. Parameters Ranges and Tested Conditions
  13. Validation of the Identified Model
  14. Results on Printed Cubes
  15. Conclusions
  16. ...and 1 more sections

Figures (6)

  • Figure 1: Structure of the feedback system composed of the controller, the electric motor, the elastic transmission and the $i$-th link of the robot manipulator.
  • Figure 2: Workflow of the integrated parameters identification procedure.
  • Figure 3: Experimental setup involving an ABB GoFa CRB15000 collaborative robot for 3D printing process.
  • Figure 4: Characteristics of the friction torques $\tau_{f,i}(\dot{q}_i)$.
  • Figure 5: Joint space trajectories relative to the tenth data point in Table \ref{['points_table']}: comparison between the identified model (red) and experimental acquisitions (blue).
  • ...and 1 more figures