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Open AccessArticle

Learning-Based Prediction of Pose-Dependent Dynamics

1
Virtual Machining–Chair for Software Engineering, Department of Computer Science, TU Dortmund University, Otto-Hahn-Str. 12, 44227 Dortmund, Germany
2
Institute of Machining Technology, Faculty of Mechanical Engineering, TU Dortmund University, Baroper Str. 303, 44227 Dortmund, Germany
*
Author to whom correspondence should be addressed.
J. Manuf. Mater. Process. 2020, 4(3), 85; https://doi.org/10.3390/jmmp4030085
Received: 30 June 2020 / Revised: 26 August 2020 / Accepted: 27 August 2020 / Published: 31 August 2020
(This article belongs to the Special Issue Machine Tool Dynamics)
The constantly increasing demand for both, higher production output and more complex product geometries, which can only be achieved using five-axis milling processes, requires elaborated analysis approaches to optimize the regarded process. This is especially necessary when the used tool is susceptible to vibrations, which can deteriorate the quality of the machined workpiece surface. The prediction of tool vibrations based on the used NC path and process configuration can be achieved by, e.g., applying geometric physically-based process simulation systems prior to the machining process. However, recent research showed that the dynamic behavior of the system, consisting of the machine tool, the spindle, and the milling tool, can change significantly when using different inclination angles to realize certain machined workpiece shapes. Intermediate dynamic properties have to be interpolated based on measurements due to the impracticality of measuring the frequency response functions for each position and inclination angle that are used along the NC path. This paper presents a learning-based approach to predict the frequency response function for a given pose of the tool center point. View Full-Text
Keywords: machine learning; milling; dynamics machine learning; milling; dynamics
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Finkeldey, F.; Wirtz, A.; Merhofe, T.; Wiederkehr, P. Learning-Based Prediction of Pose-Dependent Dynamics. J. Manuf. Mater. Process. 2020, 4, 85.

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