Paper Info
Title
System Identification of a CNC Machining Center with Support Vector Machines.
Abstract
Support Vector Machines (SVM) is a machine learning algorithm with inherent generalization ability and a convex optimization problem. This paper studies the application of the SVM method for the online identification of the nonlinear dynamic behavior of the feed velocity in a CNC machining center. Both blackbox and greybox modeling approaches are tested for this purpose. Within the German Cluster of Excellence "Integrative Production Technology for High-Wage Countries", a modelbased predictive control (MPC) strategy with a linear state-space model is already implemented for the feed velocity of the CNC machining center. Due to nonlinearities, the model of the controlled system has to be identified and updated during the process. Therefore, the SVM method should be used to recurrently identify a model in every time-step. Additionally, the identified models should be capable of being formulated in a linear state-space model. The methodology is validated with measured data from the CNC machining center. The gained results for the blackbox and greybox approaches show only small deviations from the measured behavior of the system.
Year
DOI
Venue
2018
10.1109/MED.2018.8442437
Mediterranean Conference on Control and Automation
Field
DocType
ISSN
Online identification,Nonlinear system,Numerical control,Computer science,Model predictive control,Support vector machine,Machining,Control engineering,System identification,Convex optimization
Conference
2325-369X
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Muzaffer Ay101.35
Sebastian Stemmler222.58
Dirk Abel37643.90
Max Schwenzer411.42
Fritz Klocke55126.45