Digital Twin for a Collaborative Painting Robot.

Area of Science:

• Robotics
• Digital Twin Technology
• Manufacturing Automation

Background:

• Traditional painting processes face challenges in cost, waste, and time efficiency, particularly in single-item production.
• The need for adaptable and precise automation solutions in manufacturing is increasing.

Purpose of the Study:

• To develop and demonstrate a collaborative painting robot system using a digital twin framework.
• To enable virtual simulation and pre-execution estimation of painting processes.
• To reduce setup costs, material waste, and production time.

Main Methods:

• Development of a digital twin for an automatic painting robot.
• Real-time virtual environment construction of the physical object using camera-captured pose data.
• Bidirectional communication between physical and digital twins for task design and simulation.
• Generation of robot control parameters and end effector trajectories by the digital twin.

Main Results:

• The digital twin accurately simulates painting processes and estimates results before physical execution.
• Achieved a root mean square error (RMSE) of less than 1.5 mm and a standard deviation of RMSE less than 0.85 mm.
• Demonstrated reduction in setup costs, waste, and time, with an easy-to-use operating procedure.

Conclusions:

• The digital twin framework offers a viable alternative to manual labor in painting applications.
• The system enhances efficiency and precision, particularly for single-item production.
• Future benefits include fault detection, operational optimization, and data-driven planning.