Multi-Fidelity Information Fusion to Model the Position-Dependent Modal Properties of Milling Robots

Maximilian Busch,Michael F. Zaeh

doi:10.3390/robotics11010017

Abstract

Robotic machining is a promising technology for post-processing large additively manufactured parts. However, the applicability and efficiency of robot-based machining processes are restricted by dynamic instabilities (e.g., due to external excitation or regenerative chatter). To prevent such instabilities, the pose-dependent structural dynamics of the robot must be accurately modeled. To do so, a novel data-driven information fusion approach is proposed: the spatial behavior of the robot’s modal parameters is modeled in a horizontal plane using probabilistic machine learning techniques. A probabilistic formulation allows an estimation of the model uncertainties as well, which increases the model reliability and robustness. To increase the predictive performance, an information fusion scheme is leveraged: information from a rigid body model of the fundamental behavior of the robot’s structural dynamics is fused with a limited number of estimated modal properties from experimental modal analysis. The results indicate that such an approach enables a user-friendly and efficient modeling method and provides reliable predictions of the directional robot dynamics within a large modeling domain.

Highlights

Modal Properties of Milling Robots.To achieve the climate targets set by the European Commission, formulated in theThe European Green Deal in 2019 [1], modern production processes must continuously become more energy and resource-efficient
The validation results show that the proposed modeling approach makes it possible to model the spatial behavior of the robot dynamics in the form of its modal properties
This paper presented a methodology for modeling the position-dependent dynamics of industrial robots

Summary

Introduction

Modal Properties of Milling Robots.To achieve the climate targets set by the European Commission, formulated in theThe European Green Deal in 2019 [1], modern production processes must continuously become more energy and resource-efficient. Post-processing of the workpieces, for example by milling, is still required to achieve high geometrical accuracy [3] In such scenarios, machining robots are the ideal platforms to follow the additive manufacturing processes in the process chain, because they offer a large workspace and at the same time low investment costs compared to conventional machine tools [4,5]. Their low dynamic stiffness continues to limit their industrial application [6,7,8]. Unstable machining processes result in poor surface quality of the workpiece

Methods

Results

Discussion

Conclusion