Learning-Based Prediction of Pose-Dependent Dynamics

Felix Finkeldey,Petra Wiederkehr,Andreas Wirtz,Torben Merhofe

doi:10.3390/jmmp4030085

Felix Finkeldey, Petra Wiederkehr + Show 2 more

Open Access

https://doi.org/10.3390/jmmp4030085

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Abstract

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.

Highlights

Chatter vibrations are common challenges in milling processes, leading to an insufficient workpiece quality and reduced lifetime of the machine tool and cutting tools [1,2], especially if long and slender milling tools are necessary to machine the desired workpiece geometry [3,4,5,6]
The following section presents the results for the two considered learning tasks, i.e., the prediction of frequency response functions (FRFs) and the prediction of parameter values of compliance models, in order to reduce the measurement effort and to enable the possibility to retrieve information about the pose-dependent dynamic behavior of milling operations for poses that were not investigated technologically
An evolutionary-based optimization procedure was used to identify modal parameter values for compliance models that consisted of a set of uncoupled, damped harmonic oscillators and represented the dynamic behavior of the system, consisting of the machine tool, spindle and milling tool, based on an FRF

Summary

Introduction

Chatter vibrations are common challenges in milling processes, leading to an insufficient workpiece quality and reduced lifetime of the machine tool and cutting tools [1,2], especially if long and slender milling tools are necessary to machine the desired workpiece geometry [3,4,5,6]. For an optimization of milling processes with varying engagement conditions and complex desired workpiece shapes, geometric physically-based process simulations can be used [10]. In this context, the dynamic behavior of the compliant system, consisting of the combination of the machine tool, spindle, and cutting tool, can be modeled by a set of uncoupled, damped harmonic oscillators to represent the FRF of the system measured at the tool center point (TCP) [12]. Especially when machining free-formed surfaces of large workpieces, the pose-dependent load of the spindle bearings and axis drives influences the modal properties of the system significantly [11,13,14,15], resulting in different frequency response behaviors for each pose defined by the NC path

Methods

Results

Conclusion