首页|Studies from Chemnitz University of Technology in the Area of Robotics Published (Analysis of the Position-dependent Vibration behaviour of an Industrial 6-axis Robot and Combined FE-based Modelling of the Mechanical Frequency Responses)

Studies from Chemnitz University of Technology in the Area of Robotics Published (Analysis of the Position-dependent Vibration behaviour of an Industrial 6-axis Robot and Combined FE-based Modelling of the Mechanical Frequency Responses)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on robotics have bee n published. According to news reporting originating from Chemnitz University of Technology by NewsRx correspondents, research stated, "Industrial robots (IR) a re a cost-effective and highly flexible alternative to machining centres." The news reporters obtained a quote from the research from Chemnitz University o f Technology: "Nevertheless, their use for separating processes in production te chnology is limited because of their low structural rigidity, which results in c omparatively low accuracies. The vibration behaviour of the robots is a major ch allenge to increasing accuracy, as it can vary significantly depending on the po sition of the tool centre point (TCP) in the workspace. To apply vibration compe nsation, it is necessary to be able to describe the vibration behaviour of the r obot with sufficient accuracy. This article presents a new approach, utilising p arametric FEM simulation to generate a state space description of the robot. The simulation captures the pose-dependent vibration behaviour of each axis, which is then integrated into a downstream system simulation, resulting in a comprehen sive description of the robot's vibration behaviour. After being finely tuned, t he model demonstrates excellent alignment with the experimentally determined beh aviour."

Chemnitz University of TechnologyEmerg ing TechnologiesMachine LearningNano-robotRobotRobotics

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Jun.24)