Digital Twin of Non-Ferrous Metal Casting Robot

Abstract

The robot system for non-ferrous metal casting process can replace a large number of manual operations while ensuring product quality, and complete the casting process independently. The automatic casting process requires a robot to pour molten metal quickly and accurately into a mold that moves in real time. To achieve this, traditional robots adjust the path through manual teaching. The steps of manual teaching take an immense amount of time and effort. In addition, temperature is the main factor affecting the service life of the robot. The service life of various precision parts of the robot will be significantly reduced under the high temperature environment of metal casting. This paper presents a digital twin system for optimizing traditional non-ferrous metal casting robots. By establishing a digital twin platform, twin robots, virtual scenes and the physical world mapping, the problem of long manual teaching time is solved, an algorithm for estimating the service life of the rolling functional parts of the joint bearing is proposed, and the casting robot is obtained. The end joint bearing functional parts work continuously for one day in the actual casting scene, and their service life is reduced by about 3.9%. Estimated service life of robot functional parts can prevent some potential failures of robot equipment and improve the overall service life of the robot.